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ISSN : 1225-5009(Print)
ISSN : 2287-772X(Online)
Flower Research Journal Vol.27 No.4 pp.226-241
DOI : https://doi.org/10.11623/frj.2019.27.4.01

Tools for Controlling Smart Farms: The Current Problems and Prospects in Smart Horticulture

Toan Khac Nguyen, Minjung Kwon, Jin-Hee Lim*
1Department of Plant Biotechnology, Sejong University, Seoul 05006, Korea
Corresponding author: Jin-Hee Lim Tel: +82-2-3408-4374 E-mail: jinheelim@sejong.ac.kr
02/10/2019 26/11/2019 03/12/2019

Abstract


In the Fourth Industrial Revolution, digitally effective automatic tools in agriculture are being devised to produce food quickly. Therefore, it is essential to develop a deep understanding of digital technology and its features for the consideration of new relationships, arranged skills, and cultural economics to attain the best value for the consumption of agricultural products. Farmers play a crucial role in replacing traditional agricultural methods with new technologies in their fields. Experienced farmers can draw on the Internet of Things (IoT) to benefit from smart technologies, such as gateway data stores in light, humid, and pH sensors, and improve all sensor data daily in big sectors starting from their smart farms. This report focuses on the relationship between smart farms and IoT with new network knowledge and smart devices to benefit agriculture. This report hopes to support the farmers who intend to draw on the most recent advances in IoT-based smart farming against the backdrop of evolving sensors, tackling current problems, and outlining prospects in smart horticulture.



초록


    Ministry of Agriculture, Food and Rural Affairs
    318061-03

    Introduction

    Based on the development of science and technology, “the World” concept seems better than its way over the last 3 decades by high force in different respects of life. The life quality will boost up 3 things: education, communication, and transportation. Especially, the expansion of communication is 5G smartphone, digital television, and IoT to make great control outside or inside of the farm, easily communicated family, and world-wide society. Farmers reinstate their manu al f actor to u pgrade t heir w orks w ith intelligent automatic tools to control their farms. Smart agriculture, which is a concrete name as a smart farm, was defined as an approach to optimize sensors for changing the current environment operation (Rehman and Shaikh 2009).

    There are many consecutive steps in smart farm: the first, local framework sensors and identification of located sensors and their data; the second, issue of decision by automatic understanding the different transferring data to farm data control to make the right decision based on history data; the third, motivation and control based on supporting on-time sensor decision making.

    All of t he r esearchers a re c ontinu ing to p rovide s mart farm by establishing and upgrading software projects which were related to plant and soil monitoring, crop pathogen monitoring, animal farm, and monitoring of animal behavior, etc (Paraforos et al. 2016). The various sensor productions (such as cameras including with microphones are used to monitor farm in the connection to smartphones and computers, positioning sensors, motion sensors, etc.) are established in smartphones or computers to assist farmer can be investigated their farm in advances. Farmers seem to miss the tools to make the communication in farm decisions which were related to their financial works, cost of the user-account, and in the margin of their profits.

    The aim of this paper reviews the current technology of smart farm and give opinions to become the best farmer in their farm.

    What is a smart farm?

    Smart Agriculture was defined by Rehman and Shaikh based on the following steps of the optimization of sensors’ operations: the first, sensing local agricultural parameters; the second, identification of sensing location and data gathering; the third, transferring data from crop field to control station for decision making; the fourth, decision making based on local data, domain knowledge and history; the fifth, actuation and control based on decision (Rehman and Shaikh 2009). The smart farm can be defined by the increasing of farm productivity, foreseeing crop performance in the various change of conditional environment, and it shou ld b e considered t o u se a s ystem for providing the crop performance analysis, applications, and recommendations (Jayaraman et al. 2016). According to Wolfert et al. smart farming is an advancement of maintaining information and communication technology in the cycle of farm management. Wolfert et al. hope the future of smart farming is continuing two ultimate schemes: the first, deeply integrated systems; the second, combining systems in the chain network between the farmer and every other stakeholder (Wolfert et al. 2017). The involvement of information and communication technologies within sensors, appliance, and equipment for being used in the systems of agricultural production is defined as smart farming (Pivoto et al. 2018). In Ireland, the innovation of smart farming research is the risks and benefits for agriculture and society in the key of decision-making and government (Regan 2019).

    There are three types of the farm which have different names depending on three factors such as technology, productivity, and time for upgrading system. An ordinary farm is an area of land where is devoted primarily to agricu ltural processes with t he p rimary o bjective o f basic growing for use as food, fiber, and fuel production. An ordinary farm usually has farm-house where equipment such as hand tools, and supplies are stored. An ordinary farm can be defined by three factors as a farm with has small productivity which is provided by handle technology in the time of personal experience. A convenient farm is an upgrading agricultural machine and using the half-automatic system for controlling the productivity in a time of sharing experience b etween a g rou p farmer a nd semi-au tomatic controlling system. That means the productivity is higher than an ordinary farm in the use of water, temperature, and humidity controlling by the on-off switches and simply sensors. A smart farm is an upgrading farm base on convenient farm in fully automatic technology, connecting day-by-day and upgrading system, and has the biggest productivity; especially the connection for controlling smart farm is the Internet of Things (IoT) or Web of Things (WoT) or Mobile of Things (MoT) in modern times the smart farm has been extended so far to include such industrial operations as plant smart farm and animal smart farm (Fig. 1). Smart farm is not only the best in increasing productivity but also the best control automatically to keep productivity all times even when a problem occurs in a crop.

    To discuss this in more detail, the smart farm is a combination of many factors such as system, processes, and people. Each factor not only combines but also connect with other factors to come into being interaction. For the system of the smart farm, it is big data with historical data, market data, and environment data (data of sensors and cameras) to be affected by high-speed data transfer. For the processes of the smart farm, there are three elements: monitor, optimization, and control to produce more farm product and markerting. For people activities, it is also understood as a business farmer to know the way of collecting data, to have system, processes, or agricultural training, to increase fertilizers and irrigation, to reduce consumption and risk, and to save water, money, and time (Fig. 2).

    Tools for controlling smart farm

    The particularity of smart farm is dependent on data gathering, system analysis, and processing to increase productivity and help the farmer save time and money by using minimum resources such as fertilizer, seeds, and water. The gathering data (such as sensor data, device data) can be transferred to the internet through data analytics tools to be used to enhance yields and reduce impacts on the environment and risk. During the 1980s, Global Positioning System (GPS) has been accessed for civilian use such as monitoring crop fields, applying fertilizer, and weed treatments. Next decade, the generated fertilizer and pH correction recommendations were applied in crop yield. Until now, more variables, more accurately recommendations, more sensing technologies, and more programming software have been integrated in a crop model. Type of sensors can be distributed according to the specific problem to be solved or controlled, the purpose of using, or its application (Pajares et al. 2013). Base on the conditional requirements such as the specifying-used in biological individual (direct-access inquiry), specifying-measured in biological survey (indirect-access inquiry), and the needs of identified by farmer; this review paper presents four main groups of controlling in the smart farm by biological sensors, accelerometers, image sensors, and GPS.

    Biological sensor (including chemical and gas analyzers)

    Water sensors

    Based on the using of Wireless Sensor Networks (WSN), the information of water level can be optimized to increase the packet delivery ratio by giving the estimated cost in the helping of genetic algorithm and neural networks (Singh et al. 2011). Soil degradation and scarce water resources are important in a gricu lture i n climate change e ffection. F or farmers to estimate soil's plant available water (PAW, mm) in their a rrangement o f u sing the H owleaky m odeling engine, SoilWaterApp's modelling engine can be reliably estimated patterns of PAW through fallows and crops in which is a practical application to recording weather, climate, soil, and crop information together (Freebairn et al. 2018). For reducing errors in water balance in shorter wet periods, the potential of distributing sensor supplies a more accurate value of soil water sensors in field-scale of deeper layers for long time periods of > 60 mm in soil water storage (Vidana Gamage et al. 2019). The accomplishment of four sensors (Acclima 315L – ‘ACC-315L’, Decagon GS1 – ‘DEC-GS1’, Campbell Scientific 655 – ‘CS-655’, and Watermark 200SS – ‘WM-200SS’) was installed and applied to factory calibration and derived field corrections by comparison of calibrating neutron moisture meters. It showed a significant underestimation of water storage (> 60 mm) in t he 0 .9 m s oil profile i n the u se of Watermark 200SS (Chen et al. 2019).

    Meteorological sensors

    The developing system which gives localized and timely information presents a cost-effective, automated solar-powered weather station to solve agricultural decisions in rural communities by interfacing various meteorological sensors to microcontrollers. The approach of accessing the weather information via the LCD unit on the system is used to sending a mobile message to ensure food security in arid and semi-arid African countries (Adoghe et al. 2017). The evaluation of variety in meteorological indicators assesses agricultural to provide important context for illustrating meteorological indication on widely to monitor and lead to a better understanding of where / when such monitoring and early warning tools can be indicative of likely agricultural stress and impacts (Bachmair et al. 2018). Based on IoT, the new system is developed to help the farmers make the decisions about knowing the climatic conditions such as temperature, humidity, and photon intensity within their hand in an automatic manner through its alerts via email and SMS (Short Message Service) to increase their crop yield in the 4.0 agricultural revolution (Kumar et al. 2019).

    Weed seekers

    The application of plant herbicide is dependent on sensor systems for recognizing small weeds in the early stages of development (in the stage of two- or four-leaf presence) on the real-time recognition and identification of individual weed and crop plants. By using mathematical algorithms and decision models including on the color spectrum characteristic, reflectance characteristics of invalidated plant areas and areas with useful crop areas (Schmittmann and Schulze Lammers 2017). In the western United States, there are three species of economically important infesting weeds: Kochia (Kochia scoparia L.), prickly lettuce (Lactuca serriola L.), and Russian thistle (Salsola tragus L.) to produce a lot of their seeds in post-harvest. The study on the information of weed distributions at harvest may be useful for determining and detecting the chlorophyll of green plant presences using GPS in the red waveband (638 - 710 nm) to show visual evaluations of three-weed-species (Barroso et al. 2017). Nowadays, TRIMBLE has launched next-generation WeedSeeker new version of selective spot spraying with the WeedSeeker 2 platform to introduce many new features and improvements.

    Optical sensors – hyperspectral, multispectral, fluorescence, and thermal sensing

    Optical cameras

    The potential use of thermal remote sensors (the objects are converted into visible images and radiation patterns) includes evaluating the maturity of fruits, pathogen detections, greenhouse monitoring, and estimating fruit yield (Ishimwe et al. 2014). Three dimensional (3-D) sensors are technologically advanced and economically affordable to provide key information through the implementation of robotics and automation in agriculture (Vázquez-Arellano et al. 2016).

    Light Detection and Ranging (LIDAR)

    In recent years, the lidar technology has been applied for real-time monitoring agricultural aerosol emissions in high temporal and spatial resolution. The study on a 355 nm polarization LIDAR system was used for measuring the emission during the operations of pesticide spraying to overcome misinterpretation of the different type of aerosol results in agricultural aerosols. For the upgrading of LIDAR systems, the higher depolarization ratios between the field dust (0.220 - 0.268) and the road dust (0.385) can be used to make a differentiated each type of aerosol for designing of the impacted activities on air quality in agriculture (Gregorio et al. 2018). By the conducting environment mapping of point clouds between Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors, the proper LIDAR method are recorded, mapped, and evaluated using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL) (Christiansen et al. 2017). The technology was studied on the highly accurate 3D tree models derived from LIDAR scanning to evaluate the accomplishment of new plant phenotyping varieties such as commercial solution for tree scanning of whole groves, orchards, and nurseries (Colaço et al. 2018). LIDAR technology was successfully applied for the accurate assessment of agriculture, forest resources, and coastal in Davao Region where is one of the most advantageously agricultural regions in the Philippines (Novero et al. 2018).

    Sensors for detection of Microorganisms and Pest management

    Many biotic-living organisms, such as parasites, carnivore, pathogen, and non-biotic factors as humidity, temperature, rainfall, sunlight, etc are affected in agricultural production. To detect and maintain the pests, the system of managing crop production was linked between monitoring and forecasting which were three main factors: the first, host response analysis by novel sensors; the second, biophotonics and phage display by biosensors; the third, the techniques of sensor remoting (Martinelli et al. 2015). The use of unmanned aerial vehicles (UAVs) for detection and surveillance of insect pests (applied on grape phylloxera in vineyards) was integrated with RGB sensors, multispectral, and advanced digital hyperspectral for developing a novel methodology in different applications such as the processing of variable remoting sense and plant pest surveillance (Vanegas et al. 2018).

    Photometric sensors

    Based on the use of Land Parcel Identification System (LPIS), the geometric orthoimage quality provided farmers to monitor the photometric quality for individual identification in their fields and choose the displayed photometric setting band (Tarko et al. 2015). To improve the efficiency of automated plant phenotyping, PS-Plant which is a portable 3D plant phenotyping platform in low-cost using photometric sensors and a novel-image technique to plant phenotyping was calibrated on Arabidopsis thaliana in the day-night cycle and examined growing characteristics under different conditions to demonstrate the climatic effection on plant phenotype. It was the first report of photometric stereo data, which included 221 manually annotated Arabidopsis rosettes (a total of 1,768 images) (Bernotas et al. 2019).

    Soil respiration or moisture

    Five ecosystems are setting up for measuring soil respiration such as soil moisture, soil temperature, soil electric conductivity, soil pH, and soil organic carbon. A small change in the rate of soil respiration may be affected in t he r esu lt o f significant changes in CO2 levels in the atmosphere. The significant soil CO2 emissions were showed various values between the differences of agricultural and natural ecosystems. The main factors of soil respiration are the soil temperature and soil electric conductivity should be careful when adapting the native vegetation to cropland in t he v iew of g reenhou se g as emissions (Lai e t al. 2012). For optimum sensor location and control in water content monitoring and irrigation, selection of number and location of the sensor would be the first step for the underground greenhouse environment (Ryu et al. 2014). Tillage systems not only improve soil moisture but also reduce or increase soil CO2 emission. Based on the u se of LI-8100 system and the gravimetric method, soil respiration was identified in the winter wheat (Triticum aestivum L) and summer maize (Zea mays L). Rotary tillage with crop residues reduced CO2 emission during the wheat and maize growing period in North China (Albert et al. 2016). The system, which was provided by ZigBee technology, was developed by the study on IoT application for real-time monitoring of citrus soil moisture and nutrient to help farmers improving their production, reduce labor cost and pollution by chemical fertilizer (Zhang et al. 2017). The study on land-use type in Southwest China by using a linear mixed-effects model determines the changing effection in soil temperature and moisture is the main driver of variation in soil respiration (48% in rubber plantation and 30% in the natural forest). After applying soil moisture, the model interpreted 70% in natural forest and 76% in rubber plantation (Zhao et al. 2018). The presence of water in the soil can be remotely detected found allowing for monitoring application in a landslide (Pichorim et al. 2018). The continued advance of managing on nutrient efficiency and water use can perform an important work for providing increased products in agriculture. The use of 20 5TE (Decagon Devices, Pullman, WA, USA) sensors which is the soil moisture sensor-based automated fertigation system to monitor and control fertigation can save a considerable amount of water and fertilizer and also produce the high-quality garden chrysanthemums (Rhie et al. 2018). Based on the study of maize farmland, ridge tillage and straw mulching can be expanded t he s oil temperatu re s ensitivity index o f soil respiration. The portable photosynthesis system (LI-6400) connecting with the respiratory chamber (6400-09) was used for measuring soil respiration (Zhang et al. 2019).

    Photosynthesis sensors

    Recently, photosynthesis has been understood in the most important physiological function because it increases crop productivity biomass, it effects on the climate change on crop yields, and it achieves solar energy for photochemistry (Swift et al. 2018). Based on the developing a novel FPGA-based photosynthesis smart sensor to measure relative humidity, solar radiation, temperature, CO2, air pressure and airflow; the study on photosynthetic response of chili pepper (Capsicum annuum L.) can be applied as new method by estimating the aforementioned variables, detecting different stress conditions, and permitting farmers to apply an a dju stment s trategy with o pportu nity (Millan-Almaraz et al. 2013). The photosynthesis of the sago palm (Metroxylon sagu Rottb.) was sensitivity to changes in the element of air temperature (Azhar et al. 2018). Based on the measuring light methods, there are three main different way of checking light value such as the use of Electrical Light meter, LUX meter, Quantum meter. Photosynthetic Active Radiation (PAR) was applied for measuring photosynthetic photon flux density. It was made with a blue enhanced silicon photodiode and IR blocking filters such as Apogee Quantum Sensor (SQ-420 & SQ-520) and VTB8440BH sensor (Caya et al. 2018).

    Leaf area index (LAI) sensors

    The leaf area index (LAI), as the projected area of leaves over a given unit of land area, is determined one unit of LAI is an equivalent parameter to describe 10,000 m2 of leaf area per hectare in the productivity and make management decisions. The study of rapid LAI mapping by demonstrated ground-based laser distance sensors rangefinders mounted on a vehicle offers potential for rapid mapping of LAI in the trials were winter wheat (Triticum aestivum L.), winter rye (Secale cereale L.), and oilseed rape (Brassica napus L. ssp. napus) (Gebbers et al. 2011). The linked information between photographic and wireless sensor network (WSN) techniques can be applied to develop LAI data and continuous monitor of growing points and used 3G WIFI to setup and upgrade the crop photos in real-time (Li et al. 2015). Based on a combined testing of the LAINet observation system between the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method and Gaussian process regression (GPR), the study on cropland site in China indicated that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R2 = 0 .88 correlation w ith grou nd m easu rements as evaluated over the entire growing season. This is newly advanced software and hardware methods in deriving concomitant LAI and changeableness maps with the high spatiotemporal resolution to accuracy agriculture as well as to the retrieval and validation of LAI products (Yin et al. 2019). The LAI – vegetation index (LAI-VI) relationships in different phenophases and for different VI were suggested for the assessments of the LAI instead of a single LAI-VI relationship for the active period of vegetation such as according to the growing characteristics of vegetation (Qiao et al. 2019).

    Accelerometers

    Rangefinders

    For the measuring site-specific crop parameters such as volume, height, and biomass density; the sensor is the best choice to support the important optimizing production processes. Last decade, laser rangefinder sensors have been applied to many crop biomass density under field conditions for non-contact indirect measurement. Based on the experiments and technical data of rangefinders, the flight-time method has a good choice for the site of specific crop management (Ehlert et al. 2009a). Low-cost laser rangefinders which can be expected to reduce measuring accuracy in small-sized crops and face distances (Ehlert et al. 2009b). The applied corporation for analyzing range data was preferable setting as a laser rangefinder likes a primary sensor combining with a pan-tilt unit and inertial measurement unit (Teng et al. 2016).

    Dendrometers

    Dendrometers, which is measured the tree-diameter, can be applied as high-resolution dendrometers to assess variou s insights i nto tree growth s u ch a s stem d aily w ater status and the respond of short-term growth through effectively environmental conditions (Drew and Downes 2009). The over-bark measurements of stem circumference can be validated on growing time by days to weeks for characterizing developments in hydration and stem growth (Herrmann et al. 2016). Based on the developed monitoring IoT system of radial plant growth using a low-cost optoelectronic sensor, the principle sensor system was applied in guava fruits (Psidium guajava L.) to detect alternating white and black narrow bar printed on reflective tapes which were installed encircling the fruit. The sensors data can be monitored in real-time to measure the radial fruit-growth with a maximum error of 2 mm. In term of data transfer, the success rate of the upgraded system was 97.54% c an b e u sed as a p owerfu l tool f or p lant g rowth monitoring (Slamet et al. 2018). The seasonal growth patterns of sycamore (Acer pseudoplatanus L.) and wild cherry (Prunus avium L .) i n sou thwest G ermany u sing high-resolution point-dendrometers starting three years (2010 - 2016) after pruning in a widely spaced system can be clearly understanding of the effects of stem height, climate on radial growth dynamics, and pruning treatment (Sprengel et al. 2018). Tree-stem growth, which is an important metric, requires monitoring growth on various individual trees for evaluating many ecological and afforested research questions such as environmental changing and geographies. The stem-growth measurements data of 31 red maples (Acer rubrum) in t wo u rban a reas in the eastern United States by three dendrometers were highly correlated and validated the utility of the inexpensive band (Just and Frank 2019).

    Hygrometers

    A hygrometer, which is a tool used to measure the moisture content of the air, can be used either indoors or outdoors to report humidity reading of specialized and gather d ata abou t moistu re c ontent. F or a f u ll h u midity reading, such as relative humidity or dew point (two measurements used commonly in weather reports), that is a need to collect other data, like temperature or atmospheric pressure. The hygrometer (relative humidity sensors - RH sensors) have various kind of functional materials: organic, inorganic, polymers, porous ceramics (semiconductors), ceramic/polymer, electrolytes to conduct mechanism and fabrication technologies. The sensor structure can be affected by the flexibility of the thick film and thin film processes, the choice of shape and size. On the other hand, the ceramic sensors show faster response than other types (Farahani et al. 2014). There are three very specific fields such as psychrometry, psychrometric equipment main errors, and handling difficulties of thermocouple psychrometers in the field (M. Martinez et al. 2011). By using a microfiber form-factor, the sensor head resides an optical microfiber with 10 μm diameter and 2 mm length constructed to form a compact U-shaped probe, and functionalized with a polyelectrolyte multilayer coating of 1.0 bilayer to show a miniature and fast-response hygrometer (3 ms response time) (Chen et al. 2017). The soil volumetric water content sensor was the first introduced with fiber Bragg grating-based soil moisture sensors for its exploitation in landslide prevention applications w ou ld b e given a v alu e u p to 3 7% w hen buried in the soil (Leone et al. 2017). The study on the Met Office practice for adjusting hygrometers to reduce the errors recommends identifying for developing a methodology (Bell et al. 2017). The microcontroller board, which the name is Arduino Uno together with The FC28 Hygrometer and DHT11 sensors, is used to collect the data of soil moisture, humidity, and temperature respectively in India. The board displays the processing and mapping data as codes on the LCD unit (Bhadani and Vashisht 2019).

    Temperature sensors

    Temperature sensors and humidity sensors were constructed as a combined system for getting the environmental data in agriculture which was integration module with self-recorded data in real-time via the control circuit and transferred data through IoT communication (Kim and Oh 2016). The new approach for developing a three-dimensional (3D) real-time model-based virtual sensors using computational fluid dynamics (CFD) to monitor and control the temperature in greenhouses was designed as a small-scale greenhouse based on the convection heat transfer equation. It is the first time that CFD monitoring analysis and a control strategy can be combined selection to become system models for gathering temperature data in greenhouses or large greenhouses using 3D real-time simulators (Guzmán et al. 2018).

    Gas sensors

    By using an electronic nose system (e-nose) for identifying the product quality in agriculture and its application in the analysis o f different f ru it j ams. The study was designed by gas sensor array and gas channels by Delphi. The composed sensor system was twelve sensors with metal-oxide type and solid-electrolyte type for monitoring and detecting the quality of jam (Zhao et al. 2012). The smart packaging systems are used radio frequency identification (RFID) sensors, ripeness indicators, biosensors, and temperature indicators. The freshness of agricultural quality and the product was affected by nano biosensor, carbon dioxide sensor, oxygen gas sensor in smart packaging systems (Meng et al. 2014).

    Chlorophyll meters

    The study on potato c rop in which different s ite of I taly, Belgium, Scotland, and The Netherlands from 1995 to 1999 was assessed the nitrogen status and guided nitrogen fertilization by using chlorophyll meter (Gianquinto et al. 2004). Chlorophyll meter readings (SPAD) can be applied in maize breeding for the selection of stress-adaptive genotypes using 30 inbred lines had higher rates at dry conditions (Gekas et al. 2013). For some application of chlorophyll concentration, the handheld leaf chlorophyll meters need to upgrade and more innovative devices. The measurements were achieved in the field at Maccarese (Central Italy) in 2012, the comparison between spectral reflectance indices and full spectral information (400 - 2500 nm) under the calibration of SPAD on maize (Zea mays L.) and of Dualex on winter wheat (Triticum aestivum L.), durum wheat (Triticum durum Desf.), horse bean (Vicia faba L .) a nd m aize u sing p artial l east s qu ares regression (PLSR). The correlation between reflectance-based and transmittance-based (SPAD and Dualex) takes advantage of the fu l l s pectral information throu gh P LSR (Casa e t al. 2014). By monitoring leaf nitrogen status using chlorophyll meter, the relationship between leaf nitrogen content per leaf area and SPAD readings is completely affected by various environmental factors and leaf features of each crop species in agricultural systems (Xiong et al. 2015). The pairing of dynamic chlorophyll distribution and irregular leaf shapes information in rice (Oryza sativa L.) can be determined a promising approach for the calibration of SPAD meter measurement (Yuan et al. 2016). By using Google Glass, the developed software application was introduced for indirect measurement of chlorophyll concentration in plant leaves taken two images in portable illuminator device under red and white light-emitting-diode illumination for processing and chlorophyll quantification with less than 10 seconds of the results. That platform can be displayed the results in spatiotemporal and tabular forms and would be decidedly advantageous for monitoring of plant health (Cortazar et al. 2015). The SPAD-502 chlorophyll meter is intensely flexible for the reading of oil palm leaf nitrogen and chlorophyll index concentration as well as the photosynthetic rate (C. Sim et al. 2015). By improving the accuracy of non-destructive nitrogen estimations, the optimization of plant nitrogen-use-efficiency has been studied as a rapid and inexpensive method in complexity and optimality (Ali et al. 2017). The SPAD-based nitrogen management strategy was constructed efficiently management of nitrogen fertilizer in wheat for developing the productivity and nitrogen use efficiency (Ghosh et al. 2017). The comparison for diagnosing the nutritional status of orchard between the SPAD-502 and the FieldScout CM 1000 chlorophyll meters showed that the SPAD-502 appeared to have a good choice in the diagnosis (Afonso et al. 2017). For the estimation of crop nitrogen status, the unmanned aerial vehicle (UAV) was applied on rice (Oryza sativa L.) with canopy sensor RapidSCAN CS-45 which produced red, red edge, and near-infrared wavebands (Li et al. 2018). The capacity of high leaf chlorophyll content was measured without a saturation response to consider an important for the practical use of chlorophyll meters (Padilla et al. 2018) which was applied on sweet pepper crops (de Souza et al. 2019). To determine the climate impact on a crop above-ground biomass (AGB), the calculation based on using variations in a chlorophyll content index (CCI) showed that the leaf CCI increased simulation accuracy in good statistical diagnostic results between simulated and observed crop biomass (Liu et al. 2019). The day-time-effection and nitrogen addition measurements were used for evaluating chlorophyll values in greenhouse-grown sweet pepper by using two chlorophyll meters (SPAD-502 and MC-100), and two active canopy reflectance sensors (GreenSeeker handheld and Crop Circle ACS-470). The measurement of Normalized Difference Vegetation Index with the GreenSeeker and ACS-470 and Green Normalized Difference Vegetation Index with the ACS-470 indicated not affection by day-time in the nitrogen treatments to show that these sensors can use in confidence anytime (Padilla et al. 2019).

    Image sensors

    The use of thermal remote sensing provides visible images which are called thermograms or thermal images can be used to acquiring portable, hand-held, or thermal sensors that connected with optical systems mounted on an airplane or satellite. That use includes greenhouse monitoring, nursery, plants disease detection, irrigation scheduling, estimating fruit yield, and evaluating vegetables and fruits detection (Ishimwe et al. 2014). The approach of nondestructive sensor-based methods was applied to plant disease assessment (Mahlein 2015). The study on a web-camera for protected chrysanthemum production under artificial lights was useful to determine the malfunctioning of lighting lamps by RGB pixel values (Chung et al. 2015). The system of modern RGB-D sensors with the Intel D435 sensor supplied a viable tool for c lose range phenotyping tasks in fields (Vit and Shani 2018).

    The Spectral Disease Indices (SDIs) have considered improving Flavescence Dorée disease detection, classification (higher than 90%) and monitoring in vineyards (Al-Saddik et al. 2017). The achievement of identifying radish, mulching film, and bare ground from a radish field was showed a n accu racy of ≥ 97.4%. T he u se of a deep convolutional neural network for detecting Fusarium wilt of radish has recorded an accuracy of 93.3%. The equipment of UAVs with computational techniques outperformed the standard machine learning algorithm (82.9% accuracy) are encouraging tools to improve agricultural products (Ha et al. 2017).

    Digital cameras have become extremely useful common as the product prices have come down. The CMOS (complementary metal-oxide-semiconductor) image sensor has been falling price and which are much less expensive to manufacture than CCD (change-coupled device) sensors. Various smartphone camera applications were used for many purposes in the environment, agriculture, and food depended on sensitivity improvements of CCDs and CMOS to link between computer and smartphone. (Kwon and Park 2017). The system of the CCD camera, an optical reflectance sensor (Crop Circle), and an ultrasonic module were investigated mounting height and angle with different grass growth levels under three conditions such as static, vibration and no traveling, and vibration with traveling in the fields. To compare the performance of potential sensors, the camera angle of 90° was indicated the best performance with less noise (Chung et al. 2017). For the harvesting of potato, the yield monitoring of potato was evaluated by the potentials of candidate sensors such as mass-based (load cell) and volume-based (CCD camera) sensors would be convenient for farmers and product incomes (Kabir et al. 2018). By the use of CCD camera images, the variable rate fertilization for sod production fields was depended on the site-specific grass growth levels to improve grass quality and growth (Kabir et al. 2019).

    Global Positioning System (GPS)

    The uses of GPS have advanced impressively in variable-rate farming, prescription farming, site-specific farming in agriculture. Some farmers are now using GPS for saving observation data such as slope, weed growth, nutrients, coloring, growth conditions, plant stress, …etc that mapped with a Geographical Information Systems (GIS) programs. GPS can be created as a larger database for farmer and scientist users. For GIS database is required to store and handle these data to valuable in precision agriculture (Goswami et al. 2012).

    The smart farmer or the best farmer?

    Base on the type of farm or area under cultivation (condition 1 - C1), the individual information (condition 2 - C2), the farming equipment (condition 3 - C3), the knowledge database (condition 4 - C4), and the farming practice (condition 5 - C5); there are four types of farmers: the tendentious farmer, the model farmer, the recommendable farmer, and the best farmer. The tendentious farmer; who has C1 as ordinary farm or small land or little family-farm, C2 is as the personal knowledge of farming by the family-farmer touch or teaching, C3 is known to use hand tools and a few tractor farm implement, C4 is known as misunderstanding or no learning, and C5 is as farming-secret teaching; has not much farm-product for selling in hometown market or small market. The model farmer; who has C1 as convenient farm or big land, C2 is as updated information day by day from farming-public service, C3 is changed half or fully handle tools and designed for using almost all tractor farm implements, C4 is possible to understand a part of IoT and use the half-automatic system, C5 is sharing and learning farming experience i n farmer g rou p; c an m ake more farming productions. The recommendable farmer (or smart farmer) has the same condition of the model farmer, but the farmer can update farming information all the time and have some of the outstanding suggestions based on the use of smart equipment and smart applications such as sensors, cameras, and GPS (Fig. 3). Smart farmers can design to use the developed and innovative systems for sustainable, profitable horticulture, and apply them to improve their farm productions. By following various benefits for becoming the best farmer (who has the same condition of the recommendable farmer but C1 is smart farm, C4 is well-known about IoT kit, web or mobile interface, and database), there are tailored comprehensive advice and production plans to ensure you get the most out of your farming business, including cash flow and gross margin returns by product. The best farmer have to access to a team of technical experts who are invested in getting expert results, receive expert advice to mitigate or manage production challenges, access to the latest technology to get ahead in the industry, understand the ongoing scheduled monitoring and testing of their produce or livestock to maximize seasonal opportunities, and conduct farming inspections and spraying recommendations to control pests and diseases. From the target farmer to becoming the smart farmer or the best farmer, why not?

    The current stages of smart horticulture in Korea

    Currently, smart farm technology in Korea is being progressed to the second generation of smart farm that collects bioinformation data in the first stage, which is the growth environment data collection stage (Kim 2019). The second generation of smart farm technology can be automatically measured and frequently collected growth data and environmental data. The use of that technique can be developed a growth model to optimize the growth management for the best agricultural production.

    It includes big data utilization technology that can improve productivity by analyzing data such as information and deriving precise production management conditions (Kim 2017). However, there are some problems in smart farm technology such as the compatibility, the accuracy of various sensors, and the components for increasing data. Thus, the standardization and the classify of measuring method need to be achieved (Kim 2019). To forward to the second generation smart farm, the Agricultural Technology Commercialization Foundation established the TTA PG426's driver interface for smart greenhouses (TTAK.KO-10.0845) group standard and the sensor interface for smart greenhouses established in 2016. EN-10.0903) Established a national standard under the name of KS X 3265 (Driver Interface for Smart Greenhouse) and KS X 3266 (Sensor Interface for Smart Greenhouse) in December 2018, after advancing the group standard and consulting with TTA (Foundation of Agri. Tech. commercialization & Transfer, 2018). As mentioned above, many standards are established for each field of smart farm, but considering the small domestic smart farm market structure and the development of agricultural technology in the future, producers need more standardization work. (Kim and Yue 2019). In domestic smart farm, the vegetable sector accounts for a large portion as 71% for vegetables, 4% for flowers, and in Korea (Kim et al. 2016). The mainly crop portion is paprika (31%), tomatoes (30%) and strawberries (10%) (Kim et al. 2016). The average farm output of tomato farms using smart farms increased by 30.5% from 55.09 kg to 71.89 kg, and the effects of smart farms were greatly increased. In the case of strawberries, the increase was 29.2% from 11.73 kg to 15.14 kg and paprika increased by 35.9% from 44.45 kg to 60.39 kg (Korea Agency of Education, Promotion and information served in Food, Agriculture, Forestry and Fisheries 2017). As a result of confirming the effect of smart farm in the vegetable field, it is necessary to study the introduction of smart farm in other plant crops such as flowers.

    The current problems and prospects in smart horticulture

    There are some current problems in smart horticulture: environmental problems, post-harvest problems, marketing problems, and economic problems. Environmental changes are responsible for agricultural production over the next decades (Tuomisto et al. 2017). The main of frequent occurrence in horticultural harvesting is the severe post-harvest loss and quality deterioration such as transporting, storage, and marketing (Geda and Bekele 2016). The turndown of globally economic conditions caused consumers to decrease in their discretionary income. Thus, they are probably buy many h igh priced i tems s u ch a s fresh fru it a nd vegetables (Warrington 2011). To solve these problems, the use of smart technologies is a challenge for offering great opportunity to improve horticultural crop productivity and make new technology and software. In the further of horticultural goals, there are many expectation challenges of the produ ction: f irst, the improvement of smart systems for controlling horticultural farm must be considered carefully; second, the sustainability and resource use must be increased efficiently; third, the education and training for farmer must be attached importantly; fourth, the research, development, and innovation are needed for high precision agriculture by suggesting growth model.

    Conclusion

    Viewed by various tools as proper for the future stage of sustainable development in agriculture, this review paper is possible to help the farmers and scientists to understand the introduction of Smart Farming technologies. If smart farming methods are ongoing realize their potential, then the thinking in database licenses is always mentioned to farmers on u se t hem or leave t hem in t he basis t o bu ild knowledge, with education practice, and gathering agricultural community skills around the controlling, collection, sharing, and using of agricultural data. These ways in which they are being built to connect with smart technologies and their winners.

    Acknowledgments

    We would like to thank the members of the Floriculture Lab, especially Suong Tuyet Thi Ha for her assistance.

    This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry, and Fisheries (IPET) through Agri-Bioindustry Technology Development Program, funded by the Ministry of Agriculture, Food, and Rural Affairs (MAFRA) (Project No. 318061-03).

    Figure

    FRJ-27-4-226_F1.gif

    Three types of farm.

    FRJ-27-4-226_F2.gif

    The design from the groundwork for smart farm.

    FRJ-27-4-226_F3.gif

    The relationship between the best farmer and smart farm in the Fourth Industrial Revolution.

    Table

    Reference

    1. Adoghe A , Popoola SM , Chukwuedo O , Airoboman A , Atayero P (2017) Smart weather station for rural agriculture using meteorological sensors and solar energy. Proceedings of the World Congress on Engineering 2017, Vol I, WCE 2017, July 5-7, 2017, London
    2. Afonso S , Arrobas M , Ferreira I , Rodrigues M (2017) Assessing the potential use of two portable chlorophyll meters in diagnosing the nutritional status of plants. J Plant Nutr 41:261-271
    3. Albert HA , Liang G , Gao L , Jing L , Wu X , Wu H , Wang X , Cai D (2016) Effect of conservation tillage on soil respiration rate and water content under wheat/maize system in North China Plain. J Soil Sci Environ Manage 7:10-22
    4. Ali MM , Al-Ani A , Eamus D , Tan DKY (2017) Leaf nitrogen determination using non-destructive techniques -A review. J Plant Nutr 40:928-953
    5. Al-Saddik H , Simon JC , Cointault F (2017) Development of spectral disease indices for ‘Flavescence Doree’ grapevine disease identification. Sensors 17:2772
    6. Azhar A , Makihara D , Naito H , Ehara H (2018) Photosynthesis of Sago Palm (Metroxylon sagu Rottb.) seedling at different air temperatures. Agriculture 8:4
    7. Bachmair S , Tanguy M , Hannaford J , Stahl K (2018) How well do meteorological indicators represent agricultural and forest drought across Europe? Environ Res Lett 13: 034042
    8. Barroso J , McCallum J , Long D (2017) Optical sensing of weed infestations at harvest. Sensors (Basel, Switzerland) 17:2381
    9. Bell S , Carroll P , Beardmore S , England C , Mander N (2017) A methodology for study of in-service drift of meteorological humidity sensors. Metrologia 54:S63–S73
    10. Bernotas G , Scorza LCT, Hansen, MF , Hales IJ , Halliday KJ , Smith LN , Smith ML , McCormick AJ (2019) A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth. GigaScience 8:1-15
    11. Bhadani P , Vashisht V (2019) Soil moisture, temperature and humidity measurement using Arduino. In 2019 9th Inter Confer Cloud Computing, Data Sci Engin (Confluence), pp 567-571
    12. Casa R , Castaldi F , Pascucci S , Pignatti SA (2014) Chlorophyll estimation in field crops: an assessment of handheld leaf meters and spectral reflectance measurements. J Agri Sci 153:876-890
    13. Caya MVC , Alcantara JT , Carlos JS , Cereno SSB (2018) Photosynthetically active radiation (PAR) sensor using an array of light sensors with the integration of data logging for agricultural application. In 2018 3rd Inter Con Com Commun Sys (ICCCS), pp 377-381
    14. Chen GY , Wu X , Kang YQ , Yu L , Monro TM , Lancaster DG , Liu X , Xu H (2017) Ultra-fast hygrometer based on U-shaped optical microfiber with nanoporous polyelectrolyte coating. Sci Rep 7:7943
    15. Chen Y , Marek GW , Marek TH , Heflin KR , Porter DO , Moorhead JE , Brauer DK (2019) Soil water sensor performance and corrections with multiple installation orientations and depths under three agricultural irrigation treatments. Sensors 19:2872
    16. Christiansen MP , Laursen MS , Jørgensen RN , Skovsen S , Gislum R (2017) Designing and testing a UAV mapping system for agricultural field surveying. Sensors (Basel, Switzerland) 17:2703
    17. Chung S , Kang N , Ngo V , Kim Y (2017) Sensors for grass growth estimation. In 2017 11th Intern Confer Sensing Techno (ICST), pp 1-6
    18. Chung SO , Kim YJ , Lee KH , Sung NS , Lee CH , Noh HK (2015) Remote monitoring of light environment using web-camera for protected chrysanthemum production. CNU J Agri Sci 42:447-453
    19. Colaco AF , Molin JP , Rosell-Polo JR , Escola A (2018) Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges. Horti Res 5:35
    20. Cortazar B , Koydemir HC , Tseng D , Feng S , Ozcan A (2015) Quantification of plant chlorophyll content using Google Glass. Lab on a Chip 15:1708-1716
    21. de Souza R , Pena-Fleitas MT , Thompson RB , Gallardo M , Grasso R , Padilla FM (2019) The use of chlorophyll meters to assess crop N status and derivation of sufficiency values for sweet pepper. Sensors 19:2949
    22. Drew D , Downes G (2009) The use of precision dendrometers in research on daily stem size and wood property variation: A review. Dendrochronologia 27:159-172
    23. Ehlert D , Adamek R , Horn HJ (2009a) Laser rangefinderbased measuring of crop biomass under field conditions. Precis Agric 10:395-408
    24. Ehlert D , Adamek R , Horn HJ (2009b) Vehicle based laser range finding in crops. Sensors (Basel, Switzerland) 9:3679-3694
    25. Farahani H , Wagiran R , Hamidon MN (2014) Humidity sensors principle, mechanism, and fabrication technologies: A comprehensive review. Sensors (Basel, Switzerland) 14: 7881-7939
    26. Foundation of Agri. Tech. commercialization & Transfer (2018) https://www.fact.or.kr
    27. Freebairn DM , Ghahramani A , Robinson JB , McClymont DJ (2018) A tool for monitoring soil water using modelling, on-farm data, and mobile technology. Environ Modell Softw 104:55-63
    28. Gebbers R , Ehlert D , Adamek R (2011) Rapid mapping of the leaf area index in agricultural crops. Agron J 103:1532-1541
    29. Geda M , Bekele A (2016) Post-harvest loss and quality deterioration of horticultural crops in Dire Dawa Region, Ethiopia. J of the Saudi Soc of Agr Sci 17:88-96
    30. Gekas F , Pankou C , Mylonas I , Ninou E , Sinapidou E , Lithourgidis A , Papathanasiou F , Petrevska J–K, Papadopoulou, F , Zouliamis P , Tsaprounis G , Tokatlidis I , Dordas C (2013) The use of chlorophyll meter readings for the selection of maize inbred lines under drought stress. Inter J Bio Food Veter Agri Engin 7:472-476
    31. Ghosh M , Swain D , Kumar Jha M , Tewari VK (2017) Chlorophyllmeter-based nitrogen management of wheat in Eastern India. Exp Agric 54:349-362
    32. Gianquinto G , Goffart JP , Olivier M , Guarda G , Colauzzi M , Dalla Costa L , Delle Vedove G , Vos J , Mackerron DKL (2004) The use of hand-held chlorophyll meters as a tool to assess the nitrogen status and to guide nitrogen fertilization of potato crop. Potato Res 47:35-80
    33. Goswami S , Matin S , Saxena A , Bairagi G (2012) A Review: The application of remote sensing, GIS and GPS in precision agriculture. Inter J Advan Tech Engin Res (IJATER)
    34. Gregorio E , Gené J , Sanz R , Rocadenbosch F , Chueca P , Arnó J , Solanelles F , Rosell-Polo JR (2018) Polarization lidar detection of agricultural aerosol emissions. J Sensors
    35. Guzman CH , Carrera JL , Duran HA , Berumen J , Ortiz AA , Guirette OA , Arroyo A , Brizuela JA , Gomez F , Blanco A , Azcaray HR , Hernandez M (2018) Implementation of virtual sensors for monitoring temperature in greenhouses using CFD and control. Sensors (Basel, Switzerland) 19:60
    36. Ha JG , Moon H , Kwak JT , Hassan SI , Dang M , Lee ON , Park HY (2017) Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles. J Appl Remote Sens 11:042621
    37. Herrmann V , McMahon SM , Detto M , Lutz JA , Davies SJ , Chang-Yang C-H , Anderson-Teixeira KJ (2016) Tree circumference dynamics in four forests characterized using automated dendrometer bands. PLOS ONE 11: e0169020
    38. Ishimwe R , Abutaleb K , Ahmed F (2014a) Applications of thermal imaging in agriculture - A Review. Advan Remote Sens 3:128-140
    39. Jayaraman PP , Yavari A , Georgakopoulos D , Morshed A , Zaslavsky A (2016) Internet of things platform for smart farming: Experiences and lessons learnt. Sensors (Basel, Switzerland) 16:1884
    40. Just MG , Frank SD (2019) Evaluation of an easy-to-install, low-cost dendrometer band for citizen - Science tree sesearch. J For 117:317-322
    41. Kabir M , Chung S-O , Jang B-E , Kim Y-J , Lee K-H , Okayasu T , Inoue E (2019) Variable fertilizer recommendation for grass production by image-based growth status. J Fac Agr Kyushu U 64:145-155
    42. Kabir M , Myat Swe K , Kim Y-J , Chung S-O , Jeong D-U , Lee S-H (2018) Sensor comparison for yield monitoring systems of small-sized potato harvesters. 14th International conference on precision agriculture. Jun 24-27, 2018, Montreal, Quebec, Canada
    43. Kim and Yue (2019) Smart farm technology and standardization status in Korea. J Korean Inst Commun Sci 36:25-31 (in Korean)
    44. Kim B , Oh S (2016) Design of temperature and humidity integrated sensor module for farm management. Adv Sci Lett 22:3232-3236
    45. Kim SC (2017) The 4th Industrial Revolution and smart farm technology development. J Korean Soc Agr Eng 59:10-18 (in Korean)
    46. Kim SC (2019) The 4th Industrial Revolution and smart farm technology development. J Korean Soc Agr Machinery 24:121-140 (in Korean)
    47. Kim YJ , Park JY , Park YG (2016) An Analysis of the Current Status and Success Factors of Smart Farms. Korea Rural Economic Inst Report, pp 1-74
    48. Korea Agency of Education, Promotion and information served in Food, Agriculture, Forestry and Fisheries (2017). http://www.smartfarmkorea.net
    49. Kumar R , Maheshwary P , Malche T (2019) Meteorological sensors oriented climatic condition based globally handled smart farming using internet of things
    50. Kwon O , Park T (2017) Applications of smartphone cameras in agriculture. Environment, and food: A review. J Biosyst Eng 42:330-338
    51. Lai L , Zhao X , Jiang L , Wang Y , Luo L , Zheng Y , Chen X , Rimmington GM (2012) Soil respiration in different agricultural and natural ecosystems in an Arid region. PLOS ONE 7:e48011.
    52. Leone M , Principe S , Consales M , Parente R , Laudati A , Caliro S , Cutolo A , Cusano A (2017) Fiber optic thermohygrometers for soil moisture monitoring. Sensors 17: 1451
    53. Li S , Ding X , Kuang Q , Ata-UI-Karim ST , Cheng T , Liu X , Tian Y , Zhu Y , Cao W , et al (2018) Potential of UAV-based active sensing for monitoring rice leaf nitrogen status. Front Plant Sci
    54. Li X , Liu Q , Yang R , Zhang H , Zhang J , Cai E (2015) The design and implementation of the leaf area index sensor. Sensors (Basel, Switzerland) 15:6250-6269
    55. Liu C , Liu Y , Lu Y , Liao Y , Nie J , Yuan X , Chen F (2019) Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity. Peer J 6:e6240-e6240
    56. Mahlein A-K (2015) Plant disease detection by imaging sensors – Parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis 100:241-251
    57. Martinelli F , Scalenghe R , Davino S , Panno S , Scuderi G , Ruisi P , Villa P , Stroppiana D , Boschetti M , Goulart LR , Davis CE , Dandekar AM (2015) Advanced methods of plant disease detection - A review. Agron Sustain Dev 35:1-25
    58. Meng X , Kim S , Puligundla P , Ko S (2014) Carbon dioxide and oxygen gas sensors-possible application for monitoring quality, freshness, and safety of agricultural and food products with emphasis on importance of analytical signals and their transformation. J Korean Soc Appl Biol Chem 57:723-733
    59. Millan-Almaraz JR , Torres-Pacheco I , Duarte-Galvan C , Guevara-Gonzalez RG , Contreras-Medina LM , Romero-Troncoso RdJ, Rivera-Guillen, JR (2013) FPGA-based wireless smart sensor for real-time photosynthesis monitoring. Computers and Electronics in Agr 95:58-69
    60. Novero A, S. Pasaporte M, M. Aurelio R, Jean G. Madanguit C, Ross M. Tinoy M, Luayon M, Paul L. Onez J, Daquiado EG, Mari A. Diez J, et al (2018) The use of light detection and ranging (LiDAR) technology and GIS in the assessment and mapping of bioresources in Davao Region, Mindanao Island, Philippines. Remote Sens Appli Soc Environ 13:1-11
    61. Padilla FM , de Souza R , Pena-Fleitas MT , Gallardo M , Gimenez C , Thompson RB (2018) Different responses of various chlorophyll meters to increasing nitrogen supply in sweet pepper. Fron Plant Sci 9
    62. Padilla FM , de Souza R , Pena-Fleitas MT , Grasso R , Gallardo M , Thompson RB (2019) Influence of time of day on measurement with chlorophyll meters and canopy reflectance sensors of different crop N status. Precis Agric 20:1087-1106
    63. Pajares G , Peruzzi A , Gonzalez-de-Santos P (2013) Sensors in agriculture and forestry. Sensors (Basel, Switzerland) 13:12132-12139
    64. Paraforos DS , Vassiliadis V , Kortenbruck D , Stamkopoulos K , Ziogas V , Sapounas AA , Griepentrog HW (2016) A farm management information system using future internet technologies. IFAC-Papers OnLine 49:324-329
    65. Perez MME , Barrio JJC , Garcia TSC , Seijo XXN (2011) Review. Use of psychrometers in field measurements of plant material: accuracy and handling difficulties. Span J Agric Res 9:313-328
    66. Pichorim SF , Gomes NJ , Batchelor JC (2018) Two solutions of soil moisture sensing with RFID for landslide monitoring. Sensors 18:452
    67. Pivoto D , Waquil PD , Talamini E , Finocchio CPS , Dalla Corte VF , de Vargas Mores G (2018) Scientific development of smart farming technologies and their application in Brazil. Info Proc Agric 5:21-32
    68. Qiao K , Zhu W , Xie Z , Li P (2019) Estimating the seasonal dynamics of the leaf area index using piecewise LAI-VI relationships based on phenophases. Remote Sens 11:689
    69. Regan A (2019) ‘Smart farming’ in Ireland: A risk perception study with key governance actors. NJAS - Wagen J Life Sci
    70. Rehman A-u , Shaikh Z (2009) Smart Agriculture. In Application of modern high performance networks, Chap 6, Bentham Science Pub, pp 120-129
    71. Rhie YH , Kang S , Kim DC , Kim J (2018) Production traits of garden mums subjected to various substrate water contents at a commercial production farm. Horticult J 87:389-394
    72. Ryu D-K , Ryu M-J , Chung S-O , Hur S-O , Hong S-J , Sung J-H , Kim H-H (2014) Variability of soil water content, temperature, and electrical conductivity in strawberry and tomato greenhouses in winter. J Biosys Engin 39:39-46
    73. Schmittmann O , Schulze Lammers P (2017) A true-color sensor and suitable evaluation algorithm for plant recognition. Sensors (Basel, Switzerland) 17:1823
    74. Sim CC , Rahman ZA , Tan MS , Goh K (2015) Rapid determination of leaf chlorophyll concentration, photosynthetic activity and NK concentration of Elaies guineensis via correlated SPAD-502 chlorophyll index. Asian J Agri Res 9:132-138
    75. Slamet W , Irham NM , Sutan MSA (2018) IoT based growth monitoring system of Guava (Psidium guajava L.) fruits. IOP Conference Series: Earth and Environ Sci 147:012048
    76. Sprengel L , Stangler FD , Sheppard J , Morhart C , Spiecker H (2018) Comparative analysis of the effects of stem height and artificial pruning on seasonal radial growth dynamics of wild Cherry (Prunus avium L.) and Sycamore (Acer pseudoplatanus L.) in a widely spaced system. Forests 9:174
    77. Swift T , Oliver T , Carmen Galan M, M , WhitneyH (2018) Functional nanomaterials to augment photosynthesis: Evidence and considerations for their responsible use in agricultural applications. Interface Focus
    78. Tarko A , de Bruin S , Fasbender D , Devos W , Bregt A (2015) Users' assessment of orthoimage photometric quality for visual interpretation of agricultural fields. Remote Sens 7:4919-4936
    79. Teng Z , Noguchi N , Liangliang Y , Ishii K , Jun C (2016) Development of uncut crop edge detection system based on laser rangefinder for combine harvesters. Inter J Agric Biol Engin 9:21-28
    80. Tuomisto HL , Scheelbeek PFD, Chalabi Z , Green R , Smith RD , Haines A , Dangour AD (2017) Effects of environmental change on population nutrition and health: A comprehensive framework with a focus on fruits and vegetables. Wellcome Open Research 2:21-21
    81. Vanegas F , Bratanov D , Powell K , Weiss J , Gonzalez F (2018) A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors 18:260
    82. Vazquez-Arellano M , Griepentrog HW , Reiser D , Paraforos DS (2016) 3-D imaging systems for agricultural applications - A review. Sensors 16:618
    83. Vidana Gamage DN , Biswas A , Strachan IB (2019) Field water balance closure with actively heated fiber-optics and point-based soil water sensors. Water 11:135
    84. Vit A , Shani G (2018) Comparing RGB-D sensors for close range outdoor agricultural phenotyping. Sensors 18:4413
    85. Warrington I (2011) Challenges and Opportunities for Horticulture and Priorities for Horticultural Research at the start of the Twenty-First Century. Acta Hort
    86. Wolfert S , Ge L , Verdouw C , Bogaardt M-J (2017) Big data in smart farming - A review. Agric Sys 153:69-80
    87. Xiong D , Chen J , Yu T , Gao W , Ling X , Li Y , Peng S , Huang J (2015) SPAD-based leaf nitrogen estimation is impacted by environmental factors and crop leaf characteristics. Sci Rep 5:13389-13389
    88. Yin G , Verger A , Qu Y , Zhao W , Xu B , Zeng Y , Liu K , Li J , Liu Q (2019) Retrieval of high spatiotemporal resolution leaf area index with Gaussian processes, wireless sensor network, and satellite data fusion. Remote Sens 11:244
    89. Yuan Z , Cao Q , Zhang K , Ata-Ul-Karim ST , Tian Y , Zhu Y , Cao W , Liu X (2016) Optimal leaf positions for SPAD meter measurement in rice. Front Plant Sci
    90. Zhang S , Athar Hussain H , Wang L , Hussain S , Li B (2019) Responses of soil respiration and organic carbon to straw mulching and ridge tillage in maize field of a triple cropping system in the Hilly region of Southwest China. Sustainability 11:3068
    91. Zhang X , Zhang J , Li L , Zhang Y , Yang G (2017) Monitoring citrus soil moisture and nutrients using an IoT based system. Sensors 17:447
    92. Zhao D , Zhang Y , Kong D , Chen Q , Lin H (2012) Research on recognition system of agriculture products gas sensor array and its application. Procedia Eng 29:2252-2256
    93. Zhao Y , Goldberg SD , Xu J , Harrison RD (2018) Spatial and seasonal variation in soil respiration along a slope in a rubber plantation and a natural forest in Xishuangbanna, Southwest China. J MT Sci 15:695-707.