Precision Agriculture Tools Research Articles

Moisture-Sensitive Ratiometric Color-Changing Response: a Useful Tool for Precision Farming

Anthra[1,2-d]imidazole-6,11-dione-based charge transfer probes have been developed for the detection of water impurities in a wide range of laboratory-used organic solvents. The deprotonated (anionic) forms of the probe molecules, obtained upon addition of basic anions such as fluoride, demonstrate a ratiometric color-changing response (blue to yellow in presence of F– and red to yellow with CN– ion) even in the presence of a trace amount of moisture (detection limit: 0.008 wt %). Considering their high sensitivity and “naked-eye” response, the anionic receptors are utilized for the quantification of moisture level or water impurity in a wide range of real-life samples, such as building raw materials, packaged food items, soil, plant leaves, and so on. Determination of the moisture level of plant leaves or soil samples is particularly important for precision farming as it helps the farmers to schedule the irrigation events. Finally, low-cost, reusable, and optical kit-based dye-coated paper strips were developed for rapid, on-location detection as well as quantification purposes. Since such a sustainable approach does not involve any sophisticated instruments or complex sample preparation steps, the general public with little knowledge of science or technology will be able to use it without much difficulty.

The leading role of perception: the FACOPA model to comprehend innovation adoption

In this work, we explore the link between the perception of complexity and the possibility of adopting precision agricultural tools (PATs). Many studies have analysed the role of perception, mostly considering it a determinant of adoption on the same level as other contextual factors. In contrast, this study contributes by assuming that farmers' perceived complexity is the main factor influencing their propensity to innovate and should be analysed on a different level. Starting from this assumption, a new theoretical model is proposed with the aim of studying the “factors–perception of complexity–adoption” (FACOPA) process. To test the validity of our hypothesis, a survey is conducted based on a purposive sample of 285 farmers. First, a linear regression model permits us to identify determinants of the perception of complexity. Then, a multinomial logistic model is used to determine which aspects of perceived complexity may affect the choice to adopt precision farming tools made by three different types of agricultural entrepreneurs: adopters, non-adopters, and planners. First, the linear regression results show that socio-structural variables have a logical relationship with perceived complexity, with age, farm size, the intensity of information and the intensity of work being significant. Then, the multinomial logistic model highlights that non-adopters perceive almost all aspects of complexity as barriers to adoption. Planners show a lower perception of complexity than non-adopters, with complexity being determined by financial and network aspects. The results provide interesting suggestions for policy-makers. Indeed, the FACOPA model offers insights into an intervention framework in which policy measures can be diversified to disseminate PATs based on farmer categories. Non-adopters require a broader set of policy instruments, while planners should be encouraged to become adopters through financial support and the activation of innovation networks.

Economically targeting conservation practices to optimize conservation and net revenue using precision agriculture tools

Economically targeting conservation practices to optimize conservation and net revenue using precision agriculture tools

Sustainable Crop and Weed Management in the Era of the EU Green Deal: A Survival Guide

Agricultural systems in the EU have become more vulnerable and less sustainable due to an overreliance on herbicides and the tremendous increase in herbicide-resistant weeds. The EU Green Deal aims to reduce the use and risk of chemical pesticides by 50% by 2030, although it is still undefined whether a reduction in herbicide use could be feasible in different farming systems and situations. This review aims to provide a holistic framework for sustainable crop and weed management to reduce the herbicide input and ensure crop protection. Current and future dilemmas and policies that need to be handled to ensure the agroecological transition of the EU’s agricultural systems are also discussed. The integration of non-chemical alternatives for integrated weed management is feasible and includes novel cultivation techniques (e.g., intercropping, false seedbed, reduced tillage, crop rotation and diversification, adjustments on sowing densities and dates), non-chemical tools (e.g., flaming, seed coating, beneficial microorganisms, mechanical weeding, biocontrol agents and natural herbicides), competitive plant material (hybrids and cultivars, cover crops, service crops), and new technologies and precision agriculture tools (e.g., Decision Support Systems, robots, remote sensing, UAVs, omics and nanotechnology). A special focus should be appointed to agroecology and biodiversity conservation.

Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches

Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i.e., Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel2-MultiSpectral Instrument (S2-MSI)) have been used to assess their potential in identifying bare soil pixels over an area in northeastern Tunisia, the Lebna and Chiba catchments. A spectral index image and a spectral bands image are generated for each satellite sensor (i.e., TM, OLI, ASTER, and S2-MSI). Then, two multi-sensor satellite image fusions are generated, one from the spectral index images and the other from spectral bands. The resulting spectral index and spectral band images based on mono-and multi-sensor satellites are compared through their spectral patterns and ability to predict the topsoil clay content using the Multilayer Perceptron with backpropagation learning algorithm (MLP-BP) method. The results suggest that for clay content prediction: (i) the spectral bands’ images outperformed the spectral index images regardless of the used satellite sensor; (ii) the fused images derived from the spectral index or bands provided the best performances, with a 10% increase in the prediction accuracy; and (iii) the bare soil images obtained by the fusion of many multispectral sensor satellite images can be more beneficial than using mono-sensor images. Soil maps elaborated via satellite multi-sensor data fusion might become a valuable tool for soil survey, land planning, management, and precision agriculture.

Intra-Canopy Sensing Using Multi-Rotor sUAS: A New Approach for Crop Stress Detection and Diagnosis

HighlightsA novel platform was developed for intra-canopy insertion of sensors from a multi-rotor sUAS.The system enables real-time data acquisition from inside the crop canopy comparable to an in-person view.The system provides ideal data for use in modern CNN-based stress diagnosis in row crop production.Abstract. Remote sensing is a critical tool in precision agriculture, giving producers the ability to monitor field conditions throughout the growing season. Although several remote sensing platforms are in use today, small unmanned aerial systems (sUAS) provide the greatest flexibility with the highest resolution. As sUAS capabilities continue to increase (i.e., payload, flight time, and speed), their potential in commercial row crop production is substantial. However, like other forms of remote sensing, traditional sUAS are limited to a nadir view of the target and only capture the top of the crop canopy. Although disease epidemiology and stress origins vary, this limited view usually does not capture the impact of stress at the initial manifestation. For example, stresses such as macronutrient deficiencies in corn originate at the base of the plant and then move upward as nutrients translocate. By the time the stress is detectable at the top of the canopy, the opportunity to mitigate yield loss is limited. A new sUAS platform is needed for sensing beneath the upper portion of the canopy. The Stinger platform, developed to meet this need, consists of a 4.0 m fiberglass rod, custom sensor mount, communication network, and radio link. Using this platform, a variety of sensors can be inserted into the crop canopy from a hovering sUAS. The Stinger platform, when combined with artificial intelligence (AI), significantly expands the capabilities of sUAS for diagnosis of crop stress in row crop production. Keywords: Intra-canopy sensing, Remote sensing, RGB imagery, Stress diagnosis, sUAS.

SOIL ORGANIC MATTER FRACTIONS AND MULTIVARIATE ANALYSIS IN THE DEFINITION OF PASTURE MANAGEMENT ZONES

The use of Precision Agriculture tools and soil attributes has contributed to local agricultural management in the identification of different productive potentials and quality of pastures. The present study aimed to use Precision Agriculture tools to investigate the spatial variability of soil organic matter fractions and soil chemical and physical attributes and to delineate management zones in pasture soils cultivated under tropical conditions. The study was conducted on a hay production farm located in Seropédica, Brazil. Fifty points were collected on a irregular grid and soil samples were taken at depths of 0-0.20 and 0.20-0.40 m and crop productivity was evaluated. Chemical and physical soil organic matter fractionation was performed to obtain data on fulvic acid, humic and humin fractions, mineral-associated and particulate organic carbon, and light organic matter. Geostatistics and multivariate analysis (principal component analysis and k-means clustering) were performed to define the management zones. The results obtained contributed to the division of the pasture area into two regions that can be managed in different ways aiming to increase soil organic matter in a localized manner with the possibility of reducing the use of inputs and directed management that respects the productive potential of the pasture on the farm.

Growth and productivity assessments of peanut under different irrigation water management practices using CSM-CROPGRO-Peanut model in Eastern Mediterranean of Turkey

Irrigation water scheduling is crucial to make the most efficient use of ever-decreasing water. As excessive irrigation decreases yield, while imprecise application also causes various environmental issues. Therefore, efficient management of irrigation frequency and irrigation level is necessary to sustain productivity under limited water conditions. The objective of the current study is to assess the water productivity at various irrigation regimes during peanut crop growing seasons (2014 and 2015) in Eastern Mediterranean, Turkey. The field experiments were conducted with treatments consisting of three irrigation frequencies (IF) (IF1: 25 mm; IF2: 50 mm; and IF3: 75 mm of cumulative pan evaporation (CPE)), and four irrigation water levels (WL1 = 0.50, WL2 = 0.75, WL3 = 1.0, and WL4 = 1.25). WL1, WL2, WL3, and WL4 treatments received 50, 75, 100, and 125 of cumulative pan evaporation. The CSM-CROPGRO-Peanut model was calibrated with experimental data in 2014 and evaluated with second-year experimental data (2015). The model simulated seed yield and final biomass (dry matter) reasonably well with low normalized root mean square error (RMSEn) in various irrigation intervals. The model simulated reasonably well for days to anthesis (RMSE = 2.53, d-stat = 0.96, and r2 = 0.90), days to physiological maturity (RMSE = 2.55), seed yield (RMSE = 1504), and tops biomass dry weight at maturity (RMSE = 3716). Simulation results indicated good agreement between measured and simulated soil water content (SWC) with low RMSEn values (4.0 to 16.8% in 2014 and 4.3 to 18.2% in 2015). Further results showed that IF2I125 irrigation regime produced the highest seed yield. Generally, model evaluation performed reasonably well for all studied parameters with both years’ experimental data. Results also showed that the crop model would be a precision agriculture tool for the extrapolation of the allocation of irrigation water resources and decision management under current and future climate.

Remote Sensing Monitoring of Rice Fields: Towards Assessing Water Saving Irrigation Management Practices

Rice cultivation is one of the largest users of the world’s freshwater resources. The contribution of remote sensing observations for identifying the conditions under which rice is cultivated, particularly throughout the growing season, can be instrumental for water, and crop management. Data from different remote sensing platforms are being used in agriculture, namely to detecting anomalies in crops. This is attempted by calculating vegetation indices (VI) that are based on different vegetation reflectance bands, especially those that rely on the Red, Green, and near-infrared bands, such as the Normalised Difference Vegetation Index (NDVI) or the Green Normalised Difference Vegetation Index (GNDVI). However, particular features of different crops and growing conditions justify that some indices are more adequate than others on a case-to-case basis, according to the different vegetation’s spectral signatures. In recent years, a vegetation index related to the Red Edge reflectance band, the Normalised Difference Red Edge (NDRE) has shown potential to be used as a tool to support agricultural management practices; this edge band, by taking a transition position, is very sensitive to changes in vegetation properties. This work, focusing on the rice crop and the application of different irrigation practices, explores the capability of several VIs calculated from different reflectance bands to detect variability, at the plot scale, in rice cultivation in the Lower Mondego region (Portugal). The remote sensing data were obtained from satellite Sentinel-2A imagery and using a multispectral camera mounted on an Unmanned Aerial System (UAS). By comparing several vegetation indices, we found that NDRE is particularly useful for identifying non-homogeneities in irrigation and crop growth in rice fields. Since few satellite sensors are sensible in the Red Edge band and none has the spatial resolution offered by UAS, this study explores the potential of UAS to be used as a useful support information tool in rice farming and precision agriculture, regarding irrigation, and agronomic management.

Spatial Variability of Production and Quality in Table Grapes ‘Flame Seedless’ Growing on a Flat Terrain and Slope Site

(1) Background: Precision agriculture has been used mostly to study spatial variability in vineyards for winemaking. Nevertheless, there is little available information on the impacts of its use on table grape vineyards under different slope conditions. (2) Methods: The aim was to study the spatial variability of production and berry quality in ‘Flame Seedless’ vines established on a flat (3% slope) and sloping (23% slope) terrain in the Chilean hyper-arid northern region. (3) Results: The results showed that in both vineyards, the measured variables presented a high spatial variability according to their coefficient of variation, being higher in slope than in the flat vineyard. The geostatistical analysis showed that 82% of the measured variables presented a strong spatial dependence in the slope vineyard, whereas 45% and 55% of the variables measured in the flat vineyard presented strong and moderate spatial dependence, respectively. Elevation was related to berry quality parameters in both vineyards, while trunk vine circumference was related to berry quality for the slope vineyard and to yield for the flat vineyard. (4) Conclusions: There is an important spatial variability in table grape vineyards mostly those cultivated on slope sites. Therefore, precision agriculture tools can be useful for zoning table grape vineyards, and thus improving both economic returns of viticulturists and sustainability.

Monitoring Plant Health with Near-Infrared Fluorescent H2O2 Nanosensors

Near-infrared (nIR) fluorescent single-walled carbon nanotubes (SWCNTs) were designed and interfaced with leaves of Arabidopsis thaliana plants to report hydrogen peroxide (H2O2), a key signaling molecule associated with the onset of plant stress. The sensor nIR fluorescence response (>900 nm) is quenched by H2O2 with selectivity against other stress-associated signaling molecules and within the plant physiological range (10−100 H2O2 μM). In vivo remote nIR imaging of H2O2 sensors enabled optical monitoring of plant health in response to stresses including UV-B light (−11%), high light (−6%), and a pathogen-related peptide (flg22) (−10%), but not mechanical leaf wounding (<3%). The sensor’s high biocompatibility was reflected on similar leaf cell death (<5%) and photosynthetic rates to controls without SWCNT. These optical nanosensors report early signs of stress and will improve our understanding of plant stress communication, provide novel tools for precision agriculture, and optimize the use of agrochemicals in the environment.

Spatial and fractal characterization of selected soil nutrients along a catena

Soil nutrient elements influence crop productivity and environmental sustainability and they vary across several slope positions. The objective of the current study was to evaluate the spatial and fractal variability of selected soil macro- and micronutrient elements across five slope positions: summit, shoulder, back, foot, and toe slopes. Triplicate soil samples (0–18 cm) were collected from each slope position from a pasture field planted to tall fescue (Festuca arundinacea syn. schendonorus arundinaceus). Soil nutrient elements analyzed include phosphorus (P), potassium (K), nitrate N (NO3-N), iron (Fe), and manganese (Mn). Additionally, soil pH, bulk density (BD), and soil organic carbon (SOC) were determined. Results show that SOC was 26% higher, while BD was 10% lower at the toe slope compared with the summit. Similarly, NO3-N was 15, 16, 18, and 1% higher at the toe slope position compared with the summit, shoulder, back and foot slopes probably due to nutrient mobility with water flow. Semivariogram analysis showed that the gaussian isotropic model provided be best fit (R2 > 0.97) for all nutrient elements across all slope positions. The range of spatial variability (A0) of nutrient elements was between 5.8 (K at the backslope position) and 71.0 m (P and Fe at the shoulder and back slope positions, respectively). The fractal dimensions of soil nutrient elements across all slope positions ranged from 0.216 to 1.949 suggesting that as the sampling distance exceeds half the A0, soil nutrient elements tend to become self-similar. The variability in soil nutrient elements from the current study can serve as a useful nutrient application recommendation tool for precision agriculture across five slope positions.

The AgriQ: A low-cost unmanned aerial system for precision agriculture

Precision agriculture currently presents significant growth thanks to the development of three main technologies: drones, vision-based systems, and embedded electronics. To take advantage of these technologies, services and products have launched cameras, drones, and algorithms in the cloud, from which the farmer can easily obtain valuable information for the optimization of their production. Notwithstanding the previous, in Latin America (and in general in the undeveloped countries), these technologies are costly and sometimes difficult to access for most farmers. This study proposes developing the low-cost Unmanned Aerial System (UAS) for precision agriculture tasks called AgriQ. It comprises three subsystems: (a) the drone; (b) the multispectral imaging system; and (c) the open-source software responsible for computing valuable information for the farmers. Intending to obtain a competitive UAS, we tackle the problem from four main points: (1) the construction of the drone; (2) the vision algorithms; (3) the autonomous trajectory considering all the parameters for properly recovering all the crops’ visual information; and (4) the construction of a low-cost multispectral imaging system with multiple bands. The multispectral imaging system is onboard the quadrotor and is composed of two low-cost cameras that have been modified to output multispectral imagery. With all this unmanned aerial system, we compute 8 vegetation indices that provide useful information to the farmers. To verify that the AgriQ is competitive versus commercial systems, we compared its performance to the professional multispectral camera RedEdge-M and popular Pix4D software. For this purpose, we have conducted experiments in two real environments, one of which is a real crop field. The experiments’ data show that our AgriQ UAS achieved prominent results at a fraction of the commercial system cost. Furthermore, to provide the user with a complete low-cost tool for precision agriculture, all the parts of the AgriQ are presented and explained in detail. The software is developed using open-source code where our contribution is available online on our GitHub site.

Application of a Combined Transmittance/Fluorescence Leaf Clip Sensor for the Nondestructive Determination of Nitrogen Status in White Cabbage Plants.

The correct fertilization of vegetable crops is commonly determined on the basis of soil and plant costly destructive analyses, demanding more sustainable non-invasive optical detection. Here, we tested the ability of the combined transmittance/fluorescence leaf clip Dualex device for determining the nitrogen (N) status of cabbage plants. Fully developed leaves from plants grown under different N rates of 0; 100; 200; 300 kg N ha−1 in 2018 and 2019 were measured in the field by the Dualex sensor twice a year in July and October. The chlorophyll (Chl) and nitrogen (nitrogen balance index, NBI) indices and the flavonols (Flav) index of the sensor were positively and negatively correlated to leaf nitrogen, respectively. Merging the two-years data, the NBI versus leaf N correlation was less point dispersed in October than July (R2 = 0.76 and 0.64, respectively). NBI was also correlated to cabbage yield, better in July than October. Our results showed that the multiparametric Dualex device can be used as precision agriculture tool for the early prediction of plant N and cabbage yield with economic advantage for the growers and reduced environmental contamination due to nitrate leaching.

Using Multioutput Learning to Diagnose Plant Disease and Stress Severity

Early diagnosis of leaf diseases is a fundamental tool in precision agriculture, thanks to its high correlation with food safety and environmental sustainability. It is proven that plant diseases are responsible for serious economic losses every year. The aim of this work is to study an efficient network capable of assisting farmers in recognizing pear leaf symptoms and providing targeted information for rational use of pesticides. The proposed model consists of a multioutput system based on convolutional neural networks. The deep learning approach considers five pretrained CNN architectures, namely, VGG‐16, VGG‐19, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB0, as feature extractors to classify three diseases and six severity levels. Computational experiments are conducted to evaluate the model on the DiaMOS Plant dataset, a self‐collected dataset in the field. The results obtained confirm the robustness of the proposed model in automatically extracting the discriminating features of diseased leaves by adopting the multitasking learning paradigm.

Геоинформационное обеспечение точного земледелия на примере Тюменской области

Precision farming technologies are used all over the world and are promising for the territory of Russia. The need for their implementation is determined by the need to increase agricultural production and improve food security. Precision farming can significantly improve the quality and quantity of products, while maintaining the quality of the soil and its potential. This article discusses the implementation of precision farming technologies at an agricultural enterprise using the results of soil surveys, Earth remote sensing materials and soil scanning, using the equipment of the company “Veris”. In the course of the work and the implementation of precision farming technologies, an inventory of lands was made, layers and attributes were created, maps of the enterprise’s lands were updated, soil scanning tools were investigated, various maps were built and posted on the special web service, and recommendations were given to improve soil fertility. When performing the work, open source software products were used, such as QGIS, SAGA, OneSoil web service. The result of the study showed that the inventory of agricultural land allows you to quickly identify areas with inappropriate use, automatically update data on changes in agricultural land areas, store spatial information on crop rotation, allowing you to keep records of agricultural land. Building a web map allowed farmers to actively use web mapping tools to analyze crops, highlighting relationships between soil indicators and productivity zones in the field. Based on the results of soil scanning, vegetation index and soil sampling, it is possible to obtain recommendations for improving soil fertility and productivity. The introduction of these technologies at an agricultural enterprise makes it possible to effectively use all precision farming tools and analyze spatial information. As a result of the work, the prospects were identified and the practical use of this direction at the enterprise was checked.

Trends in Satellite Remote Sensing for Precision Agriculture

Remote sensing is an essential tool for precision agriculture, allowing producers and crop consultants to monitor spatial and temporal variability in soil properties, crop growth, and crop stress from nutrients, weeds, disease, insects, or water. During the last decade, the number and capabilities of multispectral satellite remote‐sensing products have increased dramatically. This article provides an overview of some of the trends in satellite remote sensing for precision agriculture. Earn 0.5 CEUs in Crop Management by reading this article and taking the quiz at www.certifiedcropadviser.org/education/classroom/classes/756.

Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

Potassium nutrition in oil palm: The potential of metabolomics as a tool for precision agriculture

Societal Impact StatementOil palm is the first oil‐producing crop globally, representing nearly 20 million ha. In the recent past, oil palm cultivation has been controversial not only because of land utilisation at the expense of primary tropical forests or health concerns associated with palm oil, but also pollution caused by fertilization (including CO2 produced to synthesise fertilizers). Oil palm fields are heavily fertilized with potassium (K), and thus finding better, more parsimonious methods to monitor K nutrition is more important than ever. Here, we suggest that metabolomics and subsequent machine learning of metabolic signatures represent a promising tool to probe K requirements, opening avenues for precision agriculture in oil palm industry.

Portable Raman leaf-clip sensor for rapid detection of plant stress

Precision agriculture requires new technologies for rapid diagnosis of plant stresses, such as nutrient deficiency and drought, before the onset of visible symptoms and subsequent yield loss. Here, we demonstrate a portable Raman probe that clips around a leaf for rapid, in vivo spectral analysis of plant metabolites including carotenoids and nitrates. We use the leaf-clip Raman sensor for early diagnosis of nitrogen deficiency of the model plant Arabidopsis thaliana as well as two important vegetable crops, Pak Choi (Brassica rapa chinensis) and Choy Sum (Brassica rapa var. parachinensis). In vivo measurements using the portable leaf-clip Raman sensor under full-light growth conditions were consistent with those obtained with a benchtop Raman spectrometer measurements on leaf-sections under laboratory conditions. The portable leaf-clip Raman sensor offers farmers and plant scientists a new precision agriculture tool for early diagnosis and real-time monitoring of plant stresses in field conditions.