Precision Agriculture Applications Research Articles

Metal–Organic-Frameworks: as Revolutionizing Material Utilized for Distinct Applications in Precision Farming

Metal–Organic-Frameworks: as Revolutionizing Material Utilized for Distinct Applications in Precision Farming

Implementation of a Wireless Sensor Network for Environmental Measurements

Nowadays, the need to monitor different physical variables is constantly increasing and can be used in different applications, from humidity monitoring to disease detection in living beings, using a local or wireless sensor network (WSN). The Internet of Things has become a valuable approach to climate monitoring, daily parcel monitoring, early disease detection, crop plant counting, and risk assessment. Herein, an autonomous energy wireless sensor network for monitoring environmental variables is proposed. The network’s tree topology configuration, which involves master and slave modules, is managed by microcontrollers embedded with sensors, constituting a key part of the WSN architecture. The system’s slave modules are equipped with sensors for temperature, humidity, gas, and light detection, along with a photovoltaic cell to energize the system, and a WiFi module for data transmission. The receiver incorporates a user interface and the necessary computing components for efficient data handling. In an open-field configuration, the transceiver range of the proposed system reaches up to 750 m per module. The advantages of this approach are its scalability, energy efficiency, and the system’s ability to provide real-time environmental monitoring over a large area, which is particularly beneficial for applications in precision agriculture and environmental management.

Assessing radiometric calibration methods for multispectral UAV imagery and the influence of illumination, flight altitude and flight time on reflectance, vegetation index and inversion of winter wheat AGB and LAI

Radiometric correction is essential to unmanned aerial vehicle (UAV) precision agriculture applications, especially for crop multi-period analysis and quantitative inversion of phenotypic and physiological parameters. In this study, we investigated the performance of four radiometric correction methods: “Camera only” (A), “Camera and sun irradiance” (B) and “Camera, sun irradiance and sun angle” (C) methods provided by Pix4d Mapper and an irradiance sensor-based method (D) implemented with the R software. UAV campaigns were conducted at flight altitudes of 30 m, 60 m and 90 m under clear and cloudy skies at different times over an experimental field of winter wheat. The influences of illumination, flight altitude and flight time on reflectance and vegetation indexes derived from different radiometric calibration methods were analyzed. Moreover, we further assessed the impact of reflectance and vegetation index variations on the above-ground biomass (AGB) and leaf area index (LAI) estimations of winter wheat. Results showed that method D is a promising approach for radiometric calibration, which produced consistent reflectance with method A at noon (e.g., 10:44:41 AM March 17 and 11:39:37 AM April 7) and mid-afternoon (e.g., 15:30:02 PM May 1) and more reliable reflectance in the morning (e.g., 7:15:44 AM March 17). Methods B and C seriously underestimated the reflectance at 10:44:41 AM March 17 and 11:39:37 AM April 7. This was caused by the incorrect attitude angles of the irradiance sensor and further miscalculation of the solar irradiance by Pix4d Mapper. Interestingly, these errors were eliminated in the calculation of the ratio type of vegetation indexes. Methods B and C yielded vegetation indexes that were almost the same as those of methods A and D. Under cloudy sky, when direct sunlight is not blocked by clouds, method D was effective; with cloud occlusion, results on April 24 indicated that the method failed to estimate the total sunlight and direct sunlight ratio. Reflectance and vegetation indexes obtained from different radiometric correction methods were all obviously affected by flight altitude. Furthermore, the variations of NDVI with flight altitude increasing were different with reflectance. Changes in reflectance and vegetation indexes with flight altitude had little effect on the correlation coefficients yielded with AGB, but showed a significant influence on those of LAI. Through analyzing the variation of observed solar radiation at different time intervals, caution is recommended when using the assumption that solar radiation is stable during the UAV flight. The longer the flight, the greater the variation in solar radiation, especially at sunrise and sunset. Even at noon, when the flight duration is 30 min, the change in solar radiation may exceed 10 % (e.g., 16.5 % on March 20).

Optimal Design of Quadcopter Chassis Using Generative Design and Lightweight Materials to Advance Precision Agriculture

This research addresses the imperative challenge of a lightweight design for an Unmanned Aerial Vehicle (UAV) chassis to enhance the thrust-to-weight and power-to-weight ratios, crucial for optimal flight performance, focused on developing an intriguing lightweight yet robust quadcopter chassis. Advanced generative design techniques, integrated with topology optimization, using Autodesk Fusion 360 software (v. 16.5. 0.2083), 3D-printing methods and lightweight materials like Polylactic Acid (P.L.A.), Acrylonitrile Butadiene Styrene (A.B.S.), and Nylon 6/6 play a significant role in achieving the desired balance between structural integrity and weight reduction. The study showcases successful outcomes, presenting quadcopter chassis designs that significantly improve structural efficiency and overall performance metrics. The findings contribute to aerial robotics and hold promise for precision agriculture applications with relevant performed simulations, emphasizing the importance of tailored design methodologies for other engineering domains. In conclusion, this research provides a foundational step toward advancing drone technology, with weight reductions of almost 50%, P/W and T/W ratios increment of 6.08% and 6.75%, respectively, at least an 11.8% increment in Factor of Safety, at least a 70% reduction in stress values and reduced manufacturing time from its comparative DJI F450 drone, demonstrating the critical role of innovative design approaches in optimizing operational efficiency for targeted applications.

Cable-driven agribot prototype: Enabling precision agriculture through innovative design

The ongoing depopulation of rural areas and the increasing demand for high-quality food, along with the need to improve the quality of work in terms of stress, injuries, and social disputes, needs significant attention towards rethinking and organizing agricultural practices to meet these needs and improve the quality of life. These changes call for the implementation of new reforms, incentive systems, and the exploration of innovative systems for field operations. In this context, the research, design, and commercialization of electrified autonomous and collaborative systems capable of precision agriculture applications, known as agribots, are gaining paramount importance. This article presents an innovative multi-robot for smart farming. The agribot is composed by a lightweight cart designed to traverse crop field rows through a cable-way system installed on two heavy carts equipped with a power train system and PV rooftop panels. The agribot is conceived to achieve a high level of autonomy, reduce energy consumption, and minimize soil compaction while working on flat fields with low-height crops. The article is structured in two parts: the first part describes the agribot's ICT network, the heavy carts electric grid and the power systems, providing an energy consumption analysis related to the traversing of a crop field row.The second part focuses on the control, actions coordination, and supervision system (supervisor), implemented using Robot Operating System (ROS). The supervisor system, driven by a decision-making system based on a finite state machine, oversees the execution of path planning for the agribot, providing high modularity and flexibility to adapt to various system actions.

Information Communication and Computation Technologies (ICCT) for Agricultural and Environmental Information Systems for Society 5.0

Purpose: This paper aims to discover the dynamic landscape of Information Communication and Computation Technologies (ICCT) within agriculture and environmental information management, elucidating their evolutionary trajectory and the resonance of Society 5.0 principles in fostering innovative solutions. By scrutinizing the core technologies constituting ICCT in these sectors, it endeavours to shed light on the potential for integration within the framework of Society 5.0, contemplating both the possibilities and challenges inherent in this convergence. Methodology: This exploratory chapter delves into the evolving landscape of Society 5.0 and its pivotal emphasis on the integration of Information Communication and Computation Technologies (ICCT) to address the complex challenges in agriculture and environmental information management. Analysis/Results: The paper provides a background on the evolution of Society 5.0 and establishes the rationale for exploring the role of ICCT in advancing Agricultural and Environmental Information Systems within this transformative societal framework. The core technologies in ICCT are explored through IoT applications in precision agriculture, the transformative impact of blockchain in agricultural supply chains, and the utilization of remote sensing and Earth observation systems along with data analytics for environmental insights. The paper further investigates the integration of ICCT in Society 5.0 agricultural and environmental systems, unveiling how these technologies support smart farming practices, citizen engagement in agricultural decision-making, and sustainable resource management. Case studies highlight successful ICCT implementations in agriculture and environmental management, underscoring best practices and lessons learned. Emerging trends in ICCT for agriculture and environmental science are explored, providing insights into future developments. Originality/Value: Through the lens of case studies showcasing successful ICCT implementations, it seeks to distill key insights, while also conducting a forward-looking assessment of emerging trends and applications, thus contributing to a deeper understanding of the evolving role of ICCT in shaping sustainable agricultural and environmental information paradigms within the societal context of the future. Type of Paper: Exploratory analysis.

Exploring the Potential of Remote Sensing to Facilitate Integrated Weed Management in Smallholder Farms: A Scoping Review

In light of a growing population and climate change compounding existing pressures on the agri-food system, there is a growing need to diversify agri-food systems and optimize the productivity and diversity of smallholder farming systems to enhance food and nutrition security under climate change. In this context, improving weed management takes on added significance, since weeds are among the primary factors contributing to crop yield losses for smallholder farmers. Adopting remote-sensing-based approaches to facilitate precision agricultural applications such as integrated weed management (IWM) has emerged as a potentially more effective alternative to conventional weed control approaches. However, given their unique socio-economic circumstances, there remains limited knowledge and understanding of how these technological advancements can be best utilized within smallholder farm settings. As such, this study used a systematic scoping review and attribute analysis to analyze 53 peer-reviewed articles from Scopus to gain further insight into remote-sensing-based IWM approaches and identify which are potentially best suited for smallholder farm applications. The findings of this review revealed that unmanned aerial vehicles (UAVs) are the most frequently utilized remote sensing platform for IWM applications and are also well suited for mapping and monitoring weeds within spatially heterogeneous areas such as smallholder farms. Despite the potential of these technologies for IWM, several obstacles to their operationalization within smallholder farm settings must be overcome, and careful consideration must be given on how best to maximize their potential before investing in these technologies.

Summarizing soil chemical variables into homogeneous management zones – case study in a specialty coffee crop

Homogeneous management zones (HMZs) delineation is important for the application of precision agriculture because farm management decisions are based on it. Diverse soil chemical characteristics are important for the HMZs delineation. However, summarizing several variables into homogeneous zoning while taking into account the spatial distribution pattern of soil chemical characteristics is a challenge. Addressing this challenge is important to produce HMZs oriented for practical use for the farmers. In this work, 17 soil chemical variables were jointly analyzed for HMZ delineation by using indicator kriging (IK) to interpolate a soil fertility index (SFI). Soil samples were taken from a 4.5 ha area in a quasi-regular grid at 0 - 0.20 m depths in November 2019 (66 samples) and May 2021 (40 samples). Soil P, K, Ca, Mg, S, Na, H, Mn, Fe, Zn, B, cation exchange capacity, aluminum saturation, total organic carbon, base saturation, and organic matter were analyzed. In May 2021 the coffee yield was sampled together with the soil. Applying the SFI and then interpolating it using IK were effective for summarizing soil chemical variables into binary HMZs, showing a zone with higher priority for fertilization (therefore, lower general soil fertility) and another zone with low priority for fertilization. The summarizing process of several variables into binary HMZ was validated by evaluating the boxplots of each variable in each HMZ. Also, higher soil fertility areas presented a higher average coffee yield. Results indicated that joint use of SFI and IK was adequate to delineate HMZs in terms of summarizing soil fertility and separating coffee yield average variability. Delineating management zones by using the SFI approach is flexible for relatively limited sampled studies (less than one hundred samples) where machine learning and geostatistical methods may fail for lack of data.

From Space to Field: Combining Satellite, UAV and Agronomic Data in an Open-Source Methodology for the Validation of NDVI Maps in Precision Viticulture

Recent GIS technologies are shaping the direction of Precision Agriculture and Viticulture. Sentinel-2 satellites and UAVs are key resources for multi-spectral analyses of vegetation. Despite being extensively adopted in numerous applications and scenarios, the pros and cons of both platforms are still debated. Researchers have currently investigated different aspects of these sources, mainly comparing different vegetation indexes and exploring potential relationships with agronomic variables. However, due to the costs and limitations of such an approach, a standardized methodology for agronomic purposes is still missing. This study aims to fill such a methodology gap by overcoming the potential flaws or shortages of previous works. To achieve this, an image acquisition campaign covering 6 months and over 17 hectares was carried out, followed by an NDVI comparison between Sentinel-2 and UAV to eventually explore relationships with agronomic variables. Comparative analyses were performed by using both classical (Ordinary Least Squares regression and Pearson Correlation) and spatial (Moran’s Index) statistical approaches: here, 90% of cases show r and MI scores above 0.6 for plain images, with these scores expectedly lowering to 72% and 52% when considering segmented images. Moreover, NDVI thematic maps were classified into clusters and validated by the Chi-squared test. Finally, the relationship and distribution of agronomic variables within NDVI and clustered maps were consistently validated through the ANOVA test. The proposed open-source pipeline allows to strengthen existing UAV and satellite applications in Precision Agriculture by integrating more agronomic variables.

Integrating YOLOv8-agri and DeepSORT for Advanced Motion Detection in Agriculture and Fisheries

This paper integrates the YOLOv8-agri models with the DeepSORT algorithm to advance object detection and tracking in the agricultural and fisheries sectors. We address the current limitations in object classification by adapting YOLOv8 to the unique demands of these environments, where misclassification can hinder operational efficiency. Through the strategic use of transfer learning on specialized datasets, our study refines the YOLOv8-agri models for precise recognition and categorization of diverse biological entities. Coupling these models with DeepSORT significantly enhances motion tracking, leading to more accurate and reliable monitoring systems. The research outcomes identify the YOLOv8l-agri model as the optimal solution for balancing detection accuracy with training time, making it highly suitable for precision agriculture and fisheries applications. We have publicly made our experimental datasets and trained models publicly available to foster reproducibility and further research. This initiative marks a step forward in applying sophisticated computer vision techniques to real-world agricultural and fisheries management.

Evaluating urban mini plant factories: engineering and software cost perspectives for agriculture sustainability

Precision agriculture serves as a crucial solution to address the challenges of food security and agriculture sustainability. It provides significant benefits and enables effective monitoring of agriculture throughout the entire supply chain using a technology-driven approach that prioritizes internet connectivity and leverages big data. A noteworthy application of precision agriculture is the Mini Plant Factory, where plants are cultivated in closed systems with artificial lighting. The development of the Mini Plant Factory requires an economic feasibility analysis as a determinant of technology viability before widespread implementation, aiming to promote agriculture sustainability. The Software Cost Estimation Model (SCEM) assesses software development’s cost and time requirements. Engineering economics evaluates economic feasibility using metrics like NPV, BCR, IRR, PBP, and BEP methods. Function Point Analysis for UMPF software development estimates a cost of Rp 33,052,414, equivalent to 1,311 man-hours. Investment feasibility analysis considers the utilization of UMPF as viable, with an NPV of IDR 46,938,615, BCR of 1.08, IRR of 28%, and projected profitability within four years. Precision agriculture strategically addresses complex agricultural challenges, fostering sustainability with innovations such as the UMPF. The SCEM and engineering economics confirm the feasibility and rationale for implementing UMPF technology, providing essential insights for sustainable agricultural practices.

Flexible multimode sensors based on hierarchical microstructures enable non-destructive grading of fruits in cold chain logistics

In practical applications, flexible pressure sensors must demonstrate adequate sensitivity, durability, and the ability to detect both dynamic and static forces across a wide range. The objective of this study is to develop a flexible dual-mechanism piezoelectric/piezoresistive sensor (FDMPS) based on layered microstructures to enable versatile detection in real fruit sorting operations. The FDMPS consists of an upper layer featuring planar MXene electrodes on a PDMS film, a mid-layer comprising microstructured Ag electrodes on a PVDF-TrFE/Silica gel/ZnO film, and a lower layer with microstructured MXene electrodes on a PDMS film composition. To ensure a secure fit, the three-layer structure is treated with APTES and plasma. All electrodes are produced using a pneumatic direct-write process, while the PDMS and piezoelectric films are created via a spin-coating process, making them suitable for large-scale production. The flexible FDMPS, with a 10 wt% ZnO content, achieves an optimal piezoelectric output of 3.6 V. Additionally, the FDMPS demonstrates excellent linearity (0.997), resistive sensitivity (23.65 kpa−1), and stability (5000 cycles). The incorporation of microstructures significantly enhances the performance of piezoelectric/piezoresistive sensing. Moreover, the FDMPS can accurately measure bending strain rate and angle within the ranges of 0–90°/s (with a sensitivity of 0.014 V/(°·s−1)) and 0–110° (with a sensitivity of 0.216/°), respectively. In a wireless, real-time mode, the FDMPS proves effective in monitoring the reciprocating motion of a robotic arm and assessing fruit ripeness during the grading process. This advancement promotes the development and application of precision agriculture and wearable sensing technologies.

Single and multi-crop species disease detection using ITSO based gated recurrent multi-attention neural network

<p>Diseases of crop plants pose a serious danger to agricultural output and progress. Predicting the onset of a disease outbreak in advance can help public health officials better manage the pandemic. Precision agriculture (PA) applications rely heavily on current information and communication technologies (ICTs) for their contribution to long-term sustainability. Preventative measures against plant diseases require accurate early disease prediction in order to be effective. The current computer vision-based illness detection technology can only detect the disease after it has already manifested. This research intends to provide a deep learning (DL) method for early disease attack prediction using Internet of Things (IoT) directly sensed environmental factors from crop fields. There is a robust relationship between environmental factors and the life cycles of plant diseases. Disease incidence in plants can be forecast based on environmental variables in the crop field. In order to solve these issues, the research presented here suggests using a gated recurrent multi-attention neural network (GRMA-Net). The study uses multilevel modules to zero down on informative areas in order to extract additional discriminative features, as informative characteristics tend to appear at various levels in a network. In order to capture long-range dependence and contextual interaction, these characteristics are first organised as spatial sequences and then input into a deep-gated recurrent unit (GRU). Finally, an enhanced version of the Tunicate swarm optimisation model (ITSO) is used to pick the best values for the model’s hyper-parameters. Four public datasets representing a wide range of crop types are used to assess the model’s efficacy. Some of these databases cover numerous crop species, like PlantVillage (38 categories), while others focus on a single crop, such as Apple (4), Maize (4), or Rice (5). The experimental findings show that the system achieves 99.16% accuracy in identifying agricultural diseases, which is higher than the accuracy of other current deep-learning approaches.</p>

Real-Time Droplet Detection for Agricultural Spraying Systems: A Deep Learning Approach

Nozzles are ubiquitous in agriculture: they are used to spray and apply nutrients and pesticides to crops. The properties of droplets sprayed from nozzles are vital factors that determine the effectiveness of the spray. Droplet size and other characteristics affect spray retention and drift, which indicates how much of the spray adheres to the crop and how much becomes chemical runoff that pollutes the environment. There is a critical need to measure these droplet properties to improve the performance of crop spraying systems. This paper establishes a deep learning methodology to detect droplets moving across a camera frame to measure their size. This framework is compatible with embedded systems that have limited onboard resources and can operate in real time. The method leverages a combination of techniques including resizing, normalization, pruning, detection head, unified feature map extraction via a feature pyramid network, non-maximum suppression, and optimization-based training. The approach is designed with the capability of detecting droplets of various sizes, shapes, and orientations. The experimental results demonstrate that the model designed in this study, coupled with the right combination of dataset and augmentation, achieved a 97% precision and 96.8% recall in droplet detection. The proposed methodology outperformed previous models, marking a significant advancement in droplet detection for precision agriculture applications.

Integrating the Sentinel-1, Sentinel-2 and topographic data into soybean yield modelling using machine learning

It is crucial to accurately and timely estimate crop yield within field variability for sustainable management and precision farming applications. Various Earth observation systems have been developed for crop monitoring and yield prediction. However, there is a need for further research that integrates multiplatform data, advances in satellite technology, and data processing to apply this knowledge to agricultural practices. The integration of satellite imagery and environmental data has been used increasingly in recent years to predict crop yields using machine learning techniques. In recent years, VIs derived from optical satellites, particularly Sentinel 2 (S2), have gained popularity, but their availability is affected by weather conditions. On the other hand, the backscatter data from Sentinel 1 (S1) is less commonly used in agriculture due to its complex interpretation and processing, but it is not influenced by the weather. This study aims to improve the accuracy of yield predictions by combining remote sensing data with environmental variables. The use of satellite data S1 and S2 was used to identify the optimal phenological period, and a training model was developed using four machine learning techniques, including Random Forest Regression (RF), K Nearest Neighbor (KNN), Multiple Linear Regression (MLR) and Decision Tree (DT). The results showed that RF provided the highest values among the four techniques. The validation process using RF demonstrated high accuracy rates, with R2 ranging from 0.41 to 0.89, the mean square error of the root (RMSE) ranging from 0.122 to 0.224 t/ha, and the mean absolute error (MAE) ranging from 0.089 to 0.163 t/ha. The integration of satellite data S1 and S2 with topographical information may be useful for monitoring, mapping, and forecasting crop yields on small and fragmented farmlands. This approach can provide farmers, agricultural businesses, and policymakers with accurate and timely predictions of crop yield, which can facilitate decision making and provide early warnings for potential crop losses.

Towards Artificial Intelligence Applications in Precision and Sustainable Agriculture

Agriculture is the backbone of many economies across the globe [...]

Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones

Integrating deep learning for crop monitoring presents opportunities and challenges, particularly in object detection under varying environmental conditions. This study investigates the efficacy of image preprocessing methods for olive identification using mobile cameras under natural light. The research is grounded in the broader context of enhancing object detection accuracy in variable lighting, which is crucial for practical applications in precision agriculture. The study primarily employs the YOLOv7 object detection model and compares various color correction techniques, including histogram equalization (HE), adaptive histogram equalization (AHE), and color correction using the ColorChecker. Additionally, the research examines the role of data augmentation methods, such as image and bounding box rotation, in conjunction with these preprocessing techniques. The findings reveal that while all preprocessing methods improve detection performance compared to non-processed images, AHE is particularly effective in dealing with natural lighting variability. The study also demonstrates that image rotation augmentation consistently enhances model accuracy across different preprocessing methods. These results contribute significantly to agricultural technology, highlighting the importance of tailored image preprocessing in object detection models. The conclusions drawn from this research offer valuable insights for optimizing deep learning applications in agriculture, particularly in scenarios with inconsistent environmental conditions.

Application of hyperspectral technology for leaf function monitoring and nitrogen nutrient diagnosis in soybean (Glycine max L.) production systems on the Loess Plateau of China

Farmland mulching and nitrogen application are the main agricultural management measures to improve soybean (Glycine max L.) production in dryland agriculture on the Loess Plateau. However, the interaction effects of various farmland mulching materials and nitrogen application rates on soybean leaf functionality and nitrogen nutrition status remains contradictory and inconclusive, and effective and rapid diagnostic of the leaf functionality and nitrogen status are still not well-understood. Therefore, the aim of this study is twofold: 1) to investigate the interactive effects of different mulching methods and nitrogen application levels on soybean leaf growth, physiological traits, and final grain yield throughout the entire growth period; 2) to establish a chlorophyll content monitoring model based on spectral parameters and a diagnostic model for soybean nitrogen status, and to assess the effectiveness of the latter in accurately diagnosing soybean nitrogen requirements. A two-year (2021 and 2022) field experiment, which consisted of four nitrogen levels (0, 60, 120 and 180 kg N ha−1) and four mulching strategies (NM: no mulching, SM: straw mulching, FM: film mulching, SFM: straw and film mulching), was conducted in the soybean growing seasons. Subsequently, nitrogen dilution curves were established based on chlorophyll content and the optimal spectral index to compute corresponding nitrogen nutrition index (NNI) values. The results indicated that: 1) FM, in comparison to SM and SFM, had the ability to delay leaf senescence, increase chlorophyll content, and improve physiological growth parameters, thereby increasing seed yield; 2) the application of 120 kg N ha−1 stood out, boosting soybean photosynthetic productivity and providing a solid foundation for high soybean yields; 3) the Random forest (RF) model had the highest accuracy in chlorophyll content estimation (R2 =0.854, RMSE=2.765 and MRE=5.758 % in validation set); and 4) both the nitrogen dilution curves constructed based on chlorophyll content and the optimal spectral index proved to be reasonably accurate in quantitatively diagnosing the nitrogen status of soybeans. Specifically, N0 and N180 indicated sustained nitrogen deficiency and excess, respectively, while N60 and N120 effectively met soybean's nitrogen requirements. In summary, the FMN120 effectively stabilized seed yield by regulating key functional traits in soybean leaves. This study provides a theoretical basis for the application of precision agriculture and remote sensing technology in monitoring and diagnosing plant nitrogen nutrition in soybean production.

Improving Coffee Yield Interpolation in the Presence of Outliers Using Multivariate Geostatistics and Satellite Data

Precision agriculture for coffee production requires spatial knowledge of crop yield. However, difficulties in implementation lie in low-sampled areas. In addition, the asynchronicity of this crop adds complexity to the modeling. It results in a diversity of phenological stages within a field and also continuous production of coffee over time. Big Data retrieved from remote sensing can be tested to improve spatial modeling. This research proposes to apply the Sentinel-2 vegetation index (NDVI) and the Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) dataset as auxiliary variables in the multivariate geostatistical modeling of coffee yield characterized by the presence of outliers and assess improvement. A total of 66 coffee yield points were sampled from a 4 ha area in a quasi-regular grid located in southeastern Brazil. Ordinary kriging (OK) and block cokriging (BCOK) were applied. Overall, coupling coffee yield with the NDVI and/or SAR in BCOK interpolation improved the accuracy of spatial interpolation of coffee yield even in the presence of outliers. Incorporating Big Data for improving the modeling for low-sampled fields requires taking into account the difference in supports between different datasets since this difference can increase uncontrolled uncertainty. In this manner, we will consider, for future research, new tests with other covariates. This research has the potential to support precision agriculture applications as site-specific plant nutrient management.

Remote Sensing and Decision Support System Applications in Precision Agriculture: Challenges and Possibilities

Remote Sensing and Decision Support System Applications in Precision Agriculture: Challenges and Possibilities