Precision Agriculture Practices Research Articles

Delineation of management zones in integrated crop–livestock systems

AbstractThe lack of studies on spatial variability in integrated crop–livestock systems (ICLS) hinders understanding how to increase their efficiency by implementing precision agriculture (PA) practices. As such, little is known about how grain and forage crops interact and how to improve the decision‐making process on fertilization and forage management. One technique that can help manage such systems is the delineation of management zones (MZs), regions with similar yield potential and soil and topography characteristics. Thus, this paper assesses the spatial correlation between yield and potential factors affecting it, and identifies whether it is possible to establish MZs for field management of grain and forage crops in succession in ICLS. Bivariate Moran's index was used to identify the attributes most spatially correlated with the yields. Elevation, soil apparent electrical conductivity, and clay content were the most spatially correlated variables with soybean [Glycine max (L.) Merr.] yield, while soil organic matter content and elevation were the most spatially correlated with the forage yield. Spatial principal components analysis and fuzzy c‐means clustering algorithm were combined to delineate MZs for each crop. The MZs created for soybean were statistically different in grain yield, available phosphorus (P) in the 0‐to‐0.40‐m layer and pH in the 0‐to‐0.20‐m layer. The forage MZs showed significant differences in terms of available P in the 0‐to‐0.40‐m layer. We conclude that MZs for ICLS tends to be crop specific, demanding different MZs to characterize soybean and forage spatial variability.

Detecting coffee leaf rust with UAV-based vegetation indices and decision tree machine learning models

Coffee leaf rust (CLR) is one of the most devastating leaf diseases in coffee plantations. By knowing the symptoms, severity, and spatial distribution of CLR, farmers can improve disease management procedures and reduce losses associated with it. Recently, Unmanned Aerial Vehicles (UAVs)-based images, in conjunction with machine learning (ML) techniques, helped solve multiple agriculture-related problems. In this sense, vegetation indices processed with ML algorithms are a promising strategy. It is still a challenge to map severity levels of CLR using remote sensing data and an ML approach. Here we propose a framework to detect CLR severity with only vegetation indices extracted from UAV imagery. For that, we based our approach on decision tree models, as they demonstrated important results in related works. We evaluated a coffee field with different infestation classes of CLR: class 1 (from 2% to 5% rust); class 2 (from 5% to 10% rust); class 3 (from 10% to 20% rust), and; class 4 (from 20% to 40% rust). We acquired data with a Sequoia camera, producing images with a spatial resolution of 10.6 cm, in four spectral bands: green (530–570 nm), red (640–680 nm), red-edge (730–740 nm), and near-infrared (770–810 nm). A total of 63 vegetation indices was extracted from the images, and the following learners were evaluated in a cross-validation method with 10 folders: Logistic Model Tree (LMT); J48; ExtraTree; REPTree; Functional Trees (FT); Random Tree (RT), and; Random Forest (RF). The results indicated that the LMT method contributed the most to the accurate prediction of early and several infestation classes. For these classes, LMT returned F-measure values of 0.915 and 0.875, thus being a good indicator of early CLR (2 to 5% of rust) and later stages of CLR (20 to 40% of rust). We demonstrated a valid approach to model rust in coffee plants using only vegetation indices and ML algorithms, specifically for the disease's early and later stages. We concluded that the proposed framework allows inferring the predicted classes in remaining plants within the sampled area, thus helping the identification of potential CLR in non-sampled plants. We corroborate that the decision tree-based model may assist in precision agriculture practices, including mapping rust in coffee plantations, providing both an efficient non-invasive and spatially continuous monitoring of the disease.

Robots poised to transform agriculture

PurposeThis paper aims to illustrate the growing importance of agricultural robots by providing details of recent product developments and their applications.Design/methodology/approachFollowing a short introduction, this first discusses a range of agricultural applications of drones. It then provides details of a selection of mobile field robots and their applications. Finally, concluding comments are drawn.FindingsCommercially available aerial and terrestrial robots are playing a rapidly growing role in a diversity of agricultural practices. Key capabilities and benefits include detecting crop stress and disease, predicting crop yields, reducing agrochemical use, overcoming manpower shortages and reducing labour costs and facilitating precision agricultural practices such as highly localised pesticide and herbicide application and the replacement of large, heavy agricultural machines by fleets of small, lightweight robots.Originality/valueThis provides a detailed insight into the many ways in which robots are transforming agricultural practices.

A Versatile, Low-Power and Low-Cost IoT Device for Field Data Gathering in Precision Agriculture Practices

Spatial and temporal variability characterization in Precision Agriculture (PA) practices is often accomplished by proximity data gathering devices, which acquire data from a wide variety of sensors installed within the vicinity of crops. Proximity data acquisition usually depends on a hardware solution to which some sensors can be coupled, managed by a software that may (or may not) store, process and send acquired data to a back-end using some communication protocol. The sheer number of both proprietary and open hardware solutions, together with the diversity and characteristics of available sensors, is enough to deem the task of designing a data acquisition device complex. Factoring in the harsh operational context, the multiple DIY solutions presented by an active online community, available in-field power approaches and the different communication protocols, each proximity monitoring solution can be regarded as singular. Data acquisition devices should be increasingly flexible, not only by supporting a large number of heterogeneous sensors, but also by being able to resort to different communication protocols, depending on both the operational and functional contexts in which they are deployed. Furthermore, these small and unattended devices need to be sufficiently robust and cost-effective to allow greater in-field measurement granularity 365 days/year. This paper presents a low-cost, flexible and robust data acquisition device that can be deployed in different operational contexts, as it also supports three different communication technologies: IEEE 802.15.4/ZigBee, LoRa/LoRaWAN and GRPS. Software and hardware features, suitable for using heat pulse methods to measure sap flow, leaf wetness sensors and others are embedded. Its power consumption is of only 83 μA during sleep mode and the cost of the basic unit was kept below the EUR 100 limit. In-field continuous evaluation over the past three years prove that the proposed solution—SPWAS’21—is not only reliable but also represents a robust and low-cost data acquisition device capable of gathering different parameters of interest in PA practices.

Predicting Eucalyptus Diameter at Breast Height and Total Height with UAV-Based Spectral Indices and Machine Learning

Machine learning techniques (ML) have gained attention in precision agriculture practices since they efficiently address multiple applications, like estimating the growth and yield of trees in forest plantations. The combination between ML algorithms and spectral vegetation indices (VIs) from high-spatial-resolution line measurement, segment: 0.079024 m multispectral imagery, could optimize the prediction of these biometric variables. In this paper, we investigate the performance of ML techniques and VIs acquired with an unnamed aerial vehicle (UAV) to predict the diameter at breast height (DBH) and total height (Ht) of eucalyptus trees. An experimental site with six eucalyptus species was selected, and the Parrot Sequoia sensor was used. Several ML techniques were evaluated, like random forest (RF), REPTree (DT), alternating model tree (AT,) k-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), linear regression (LR), and radial basis function (RBF). Each algorithm performance was verified using the correlation coefficient (r) and the mean absolute error (MAE). We used, as input, 34 VIs as numeric variables to predict DHB and Ht. We also added to the model a categorical variable as input identifying the different eucalyptus trees species. The RF technique obtained an overall superior estimation for all the tested configurations. Still, the RBF also showed a higher performance for predicting DHB, numerically surpassing the RF both in r and MAE, in some cases. For Ht variable, the technique that obtained the smallest MAE was SVM, though in a particular test. In this regard, we conclude that a combination of ML and VIs extracted from UAV-based imagery is suitable to estimate DBH and Ht in eucalyptus species. The approach presented constitutes an interesting contribution to the inventory and management of planted forests.

Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture

Precision farming aims to optimizing the crop production process and managing sustainable supply chain practices as more efficient and reasonable as possible. Recently, various advanced technologies, such as deep-learning and internet of things (IoT), have achieved remarkable intelligence progress in realistic agricultural conditions. However, crops species recognition can be considered as fine-grained visual classification (FGVC) problem, suffering the low inter-class discrepancy and high intra-class variances from the subordinate categories, which is more challenging than common basic-level category classification depended on traditional deep neural networks (DNNs). This paper presents a fine-grained visual recognition model named as MCF-Net to classifying different crop species in practical farmland scenes. Proposed MCF-Net is consisted of cross stage partial network (CSPNet) as backbone module, three parallel sub-networks, and cross-level fusion module. With multi-stream hybrid architecture utilizing massive fine-granulometric information, MCF-Net obtains preferable representation ability for distinguishing interclass discrepancy and tolerating intra-class variances. In addition, the end-to-end implementation of MCF-Net is optimized by cross-level fusion strategy to accurately identify different crop categories. Several experiments on in-field CropDeepv2 datasets demonstrate that our method favorably against the state-of-the-art methods. The recognition accuracy and F1-score of MCF-Net achieves very competitive results up to 90.6% and 0.962 separately, both of which outperforming contrasted models indicate better recognition accuracy and model stability of our method. Moreover, the overall parameters of MCF-Net are only 807 MByte with achieving a good balance between model’s performance and complexity. It is acceptable and suitable to the implementation of IoT platforms in precision agricultural practices.

Application of Core/Shell Nanoparticles in Smart Farming: A Paradigm Shift for Making the Agriculture Sector More Sustainable.

Modern agriculture has entered an era of technological plateau where intervention of smarter technology like nanotechnology is imminently required for making this sector economically and environmentally sustainable. Throughout the world, researchers are trying to exploit the novel properties of several nanomaterials to make agricultural practices more efficient. Core/shell nanoparticles (CSNs) have attracted much attention because of their multiple attractive novel features like high catalytic, optical, and electronic properties for which they are being widely used in sensing, imaging, and medical applications. Though it also has the promise to solve a number of issues related to agriculture, its full potential still remains mostly unexplored. This review provides a panoramic view on application of CSNs in solving several problems related to crop production and precision farming practices where the wastage of resources can be minimized. This review also summarizes different classes of CSNs and their synthesis techniques. It emphasizes and analyzes the probable potential applications of CSNs in the field of crop improvement and crop protection, detection of plant diseases and agrochemical residues, and augmentation of chloroplast mediated photosynthesis. In a nutshell, there is enormous scope to formulate and design CSN-based smart tools for applications in agriculture, making this sector more sustainable.

Visualization and application of disease diagnosis codes for population health management using porcine diseases as a model.

Accurate and timely results of diagnostic investigations and laboratory testing guide clinical interventions for the continuous improvement of animal health and welfare. Infectious diseases can severely limit the health, welfare, and productivity of populations of animals. Livestock veterinarians submit thousands of samples daily to veterinary diagnostic laboratories (VDLs) for disease diagnosis, pathogen monitoring, and surveillance. Individual diagnostic laboratory reports are immediately useful; however, aggregated historical laboratory data are increasingly valued by clinicians and decision-makers to identify changes in the health status of various animal populations over time and geographical space. The value of this historical information is enhanced by visualization of trends of agent detection, disease diagnosis, or both, which helps focus time and resources on the most significant pathogens and fosters more effective communication between livestock producers, veterinarians, and VDL professionals. Advances in data visualization tools allow quick, efficient, and often real-time scanning and analysis of databases to inform, guide, and modify animal health intervention algorithms. Value is derived at the farm, production system, or regional level. Visualization tools allow client-specific analyses, benchmarking, formulation of research questions, and monitoring the effects of disease management and precision farming practices. We present here the approach taken to visualize trends of disease occurrence using porcine disease diagnostic code data for the period 2010 to 2019. Our semi-automatic standardized creation of a visualization platform allowed the transformation of diagnostic report data into aggregated information to visualize and monitor disease diagnosis.

Exploration of sub-field microclimates and winter temperatures: Implications for precision agriculture.

The field of precision agriculture has brought the concept for "big data" to farming by bringing sensor technology into the field allowing growers to make more efficient management decisions. However much of the research and practice of precision agriculture has focused on soil-related issues while sub-field microclimates have been mostly unstudied despite their known importance to crop production. This study sought to explore the differences in temperature at a sub-field level during an entire season using weather microsensors recording data every minute from 11 Dec 2017 to 11 Apr 2018. Twenty-two cost-effective sensors were placed within a ~ .5 ha area satsuma orange (Citrus unshiu) grove along the Gulf Coast on Baldwin County, Alabama. The sensors were placed in aerated housings in a vertical column on the west face of eleven trees at a height of 1 and 2 m off the ground. We focus on several events where temperatures hovered near 0 °C or near - 7 °C, a temperature known to damage satsuma trees and find that temperatures can vary by as much as 1.5 to 2 °C at the same moment in the same grove. Extreme cold events were also found to be non-uniform within the grove, and the response was seen on a tree-by-tree basis where increased exposure to < - 7 °C temperatures led to increase defoliation (r2 = 0.92) and lower fruit count in the following year (r2 = 0.71). We discuss the implication of these differences in temperature and what it may mean for the future of precision agriculture.

Precision Agriculture for Resource Use Efficiency in Smallholder Farming Systems in Sub-Saharan Africa: A Systematic Review

Opportunities exist for adoption of precision agriculture technologies in all parts of the world. The form of precision agriculture may vary from region to region depending on technologies available, knowledge levels and mindsets. The current review examined research articles in the English language on precision agriculture practices for increased productivity among smallholder farmers in Sub-Saharan Africa. A total of 7715 articles were retrieved and after screening 128 were reviewed. The results indicate that a number of precision agriculture technologies have been tested under SSA conditions and show promising results. The most promising precision agriculture technologies identified were the use of soil and plant sensors for nutrient and water management, as well as use of satellite imagery, GIS and crop-soil simulation models for site-specific management. These technologies have been shown to be crucial in attainment of appropriate management strategies in terms of efficiency and effectiveness of resource use in SSA. These technologies are important in supporting sustainable agricultural development. Most of these technologies are, however, at the experimental stage, with only South Africa having applied them mainly in large-scale commercial farms. It is concluded that increased precision in input and management practices among SSA smallholder farmers can significantly improve productivity even without extra use of inputs.

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.

Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery

Accurately mapping farmlands is important for precision agriculture practices. Unmanned aerial vehicles (UAV) embedded with multispectral cameras are commonly used to map plants in agricultural landscapes. However, separating plantation fields from the remaining objects in a multispectral scene is a difficult task for traditional algorithms. In this connection, deep learning methods that perform semantic segmentation could help improve the overall outcome. In this study, state-of-the-art deep learning methods to semantic segment citrus-trees in multispectral images were evaluated. For this purpose, a multispectral camera that operates at the green (530–570 nm), red (640–680 nm), red-edge (730–740 nm) and also near-infrared (770–810 nm) spectral regions was used. The performance of the following five state-of-the-art pixelwise methods were evaluated: fully convolutional network (FCN), U-Net, SegNet, dynamic dilated convolution network (DDCN) and DeepLabV3 + . The results indicated that the evaluated methods performed similarly in the proposed task, returning F1-Scores between 94.00% (FCN and U-Net) and 94.42% (DDCN). It was also determined the inference time needed per area and, although the DDCN method was slower, based on a qualitative analysis, it performed better in highly shadow-affected areas. This study demonstrated that the semantic segmentation of citrus orchards is highly achievable with deep neural networks. The state-of-the-art deep learning methods investigated here proved to be equally suitable to solve this task, providing fast solutions with inference time varying from 0.98 to 4.36 min per hectare. This approach could be incorporated into similar research, and contribute to decision-making and accurate mapping of plantation fields.

Geophysical and Contamination Assessment of Soil Spatial Variability for Sustainable Precision Agriculture in Omu-Aran Farm, Northcentral Nigeria

The Spatial and temporal variability of soil properties (fluid composition, structure and water content) and hydrogeological properties employed for sustainable precision agriculture can be obtained from geoelectrical resistivity methods. For sustainable precision agricultural practices, site-specific information is paramount, especially at the planting season. An integrated one-dimensional (1D) and two-dimensional (2D) electrical resistivity surveys have been adopted to characterize the subsoil parameters and delineate the aquifer unit of large farm area, especially in precision agricultural practices. Also, contamination assessment reveals the soil quality status of farmlands. This study aims to determine the site-specific soil parameters of a commercial farm in Omu-Aran, Northcentral, Nigeria. The subsoil features from the geoelectrical resistivity surveys indicate 3 to 4 distinctive lithology to a depth of 43.4 m into the subsurface of the farm. The ID (Vertical Electrical Sounding) and 2D resistivity inversion models results have revealed the heterogeneity nature of the topsoil also known as the stone zone comprising of reworked clayey soil and sandy gravelly soil, the weathered/saprolite zone (gravelly sandy/ sandy soil), the fractured basement and the fresh basement rock. Contamination factor (Cf), pollution load index (PLI) and Nemerow integrated pollution index (NIPI) were used to assess the contamination index on the farmland. Toxic elements such as arsenic, cadmium, chromium, cobalt, lead, manganese, nickel, and zinc have low to moderate contamination in the farm. The depth of investigation (≤ 3m) covers the upper root zone of significant crops grown in the area. The findings can assess soil contamination, delineate basement features, subsoil variability, soil profiling, and determine the subsoil hydrological properties.

Precision agriculture: automated irrigation system in tandem with solar panels for melon farming cultivation

This present study aimed to design and apply an automated irrigation system powered by solar panels for melon farming cultivation. It related to precision agriculture practices from which benefited as an environmental friendly approach. This study was performed by designing irrigation systems, constructing solar panels as a power source for generating pump used to irrigate melon orchard. The results showed that those combinations setup were benefited to reduce manual irrigation system which is normally conducted by human efforts. Further, it also acted as an environmental friendly practice since the system is attempted to utilize solar energy as main power source for irrigation pump. Based on obtained results, it may conclude that abundance energy in our earth can be used for precision agriculture practices with the aim to reduce environmental pollution and waste.

Farmer’s Knowledge in Land Suitability Evaluation and Farmers’ Awareness in Organic Farming for Sustainable Agriculture: A Case Study in Perlis

Sustainable agriculture is the heart of organic farming, and human resources are playing a vital role in directing the community toward sustainable development in agricultural practices. The research objective is to evaluate the Farmer’s Knowledge (FK) in land suitability variables for crop type’s suitability in Perlis. After that, the collected spatial information variables applied to allocate the spatial distribution for four main crop types in Perlis. Besides that, to investigate farmer’s awareness in organic farming in achieving sustainable agriculture and at the same time to identify young participants in the agriculture field. Participatory Geographical Information System (PGIS) practices and mixed-method approach has applied to collect spatial data information in the study area. Spatial data interpolated using the Kriging method in GIS software. It found that soil types and soil suitability are the most important variables that classed by farmers in land suitability evaluation. The spatial distribution map of crop suitability has the potential to indicate agreement or conflict with a land resource development for the study area. The results show the farmer’s awareness in applying organic farming still low, with the percentage of applying organic fertilizer is 18%. Besides, the results indicate that the young generation has a small contribution, with 30% involved in the agriculture sector. Nowadays, the participation of the young farmers is more realistic in applied new technology for instance Precision Agriculture (PA) practices.

Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation”

The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies.

Drivers and challenges of precision agriculture: a social media perspective

Precision agriculture, which has existed for over four decades, ensures efficient use of agricultural resources for increased productivity and sustainability with the use of technology. Due to the lingering perception that the adoption of precision agriculture has been slow, this study examines public thoughts on the practice of precision agriculture by employing social media analytics. A machine learning-based social media analytics tool—trained to identify and classify posts using lexicons, emoticons, and emojis—was used to capture sentiments and emotions of social media users towards precision agriculture. The study also validated the drivers and challenges of precision agriculture by comparing extant literature with social media data. By mining online data from January 2010 to December 2019, this research captured over 40,000 posts discussing a myriad of concerns related to the practice. An analysis of these posts uncovered joy as the most predominant emotion, also reflected the prevalence of positive sentiments. Robust regulatory and institutional policies that promote both national and international agenda for PA adoption, and the potential of agricultural technology adoption to result in net-positive job creation were identified as the most prevalent drivers. On the other hand, the cost and complexity of currently available technologies, as well as the need for proper data security and privacy were the most common challenges present in social media dialogue.

Assessing the Influence of UAV Altitude on Extracted Biophysical Parameters of Young Oil Palm

The information on biophysical parameters—such as height, crown area, and vegetation indices such as the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE)—are useful to monitor health conditions and the growth of oil palm trees in precision agriculture practices. The use of multispectral sensors mounted on unmanned aerial vehicles (UAV) provides high spatio-temporal resolution data to study plant health. However, the influence of UAV altitude when extracting biophysical parameters of oil palm from a multispectral sensor has not yet been well explored. Therefore, this study utilized the MicaSense RedEdge sensor mounted on a DJI Phantom–4 UAV platform for aerial photogrammetry. Three different close-range multispectral aerial images were acquired at a flight altitude of 20 m, 60 m, and 80 m above ground level (AGL) over the young oil palm plantation area in Malaysia. The images were processed using the structure from motion (SfM) technique in Pix4DMapper software and produced multispectral orthomosaic aerial images, digital surface model (DSM), and point clouds. Meanwhile, canopy height models (CHM) were generated by subtracting DSM and digital elevation models (DEM). Oil palm tree heights and crown projected area (CPA) were extracted from CHM and the orthomosaic. NDVI and NDRE were calculated using the red, red-edge, and near-infrared spectral bands of orthomosaic data. The accuracy of the extracted height and CPA were evaluated by assessing accuracy from a different altitude of UAV data with ground measured CPA and height. Correlations, root mean square deviation (RMSD), and central tendency were used to compare UAV extracted biophysical parameters with ground data. Based on our results, flying at an altitude of 60 m is the best and optimal flight altitude for estimating biophysical parameters followed by 80 m altitude. The 20 m UAV altitude showed a tendency of overestimation in biophysical parameters of young oil palm and is less consistent when extracting parameters among the others. The methodology and results are a step toward precision agriculture in the oil palm plantation area.

Water Stress Estimation in Vineyards from Aerial SWIR and Multispectral UAV Data

Mapping water stress in vineyards, at the parcel level, is of significant importance for supporting crop management decisions and applying precision agriculture practices. In this paper, a novel methodology based on aerial Shortwave Infrared (SWIR) data is presented, towards the estimation of water stress in vineyards at canopy scale for entire parcels. In particular, aerial broadband spectral data were collected from an integrated SWIR and multispectral instrumentation, onboard an unmanned aerial vehicle (UAV). Concurrently, in-situ leaf stomatal conductance measurements and supplementary data for radiometric and geometric corrections were acquired. A processing pipeline has been designed, developed, and validated, able to execute the required analysis, including data pre-processing, data co-registration, reflectance calibration, canopy extraction and water stress estimation. Experiments were performed at two viticultural regions in Greece, for several vine parcels of four different vine varieties, Sauvignon Blanc, Merlot, Syrah and Xinomavro. The performed qualitative and quantitative assessment indicated that a single model for the estimation of water stress across all studied vine varieties was not able to be established (r2 &lt; 0.30). Relatively high correlation rates (r2 &gt; 0.80) were achieved per variety and per individual variety clone. The overall root mean square error (RMSE) for the estimated canopy water stress was less than 29 mmol m−2 s−1, spanning from no-stress to severe canopy stress levels. Overall, experimental results and validation indicated the quite high potentials of the proposed instrumentation and methodology.

Smartphone Applications Targeting Precision Agriculture Practices—A Systematic Review

Traditionally farmers have used their perceptual sensorial systems to diagnose and monitor their crops health and needs. However, humans possess five basic perceptual systems with accuracy levels that can change from human to human which are largely dependent on the stress, experience, health and age. To overcome this problem, in the last decade, with the help of the emergence of smartphone technology, new agronomic applications were developed to reach better, cost-effective, more accurate and portable diagnosis systems. Conventional smartphones are equipped with several sensors that could be useful to support near real-time usual and advanced farming activities at a very low cost. Therefore, the development of agricultural applications based on smartphone devices has increased exponentially in the last years. However, the great potential offered by smartphone applications is still yet to be fully realized. Thus, this paper presents a literature review and an analysis of the characteristics of several mobile applications for use in smart/precision agriculture available on the market or developed at research level. This will contribute to provide to farmers an overview of the applications type that exist, what features they provide and a comparison between them. Also, this paper is an important resource to help researchers and applications developers to understand the limitations of existing tools and where new contributions can be performed.