Site-specific Crop Management Research Articles

Monitoring Land Management Practices Using Vis–NIR Spectroscopy Provides Insights into Predicting Soil Organic Carbon and Limestone Levels in Agricultural Plots

This study aimed to establish sound relationships between soil properties of agricultural land in central Spain and their spectral attributes to contribute to deriving an indicator for sustainable farm management. Sixteen plots, managed under various conditions, eight with traditional tillage and eight with other alternative managements, were selected to gather soil samples representing three predominant soil orders (Leptosols, Cambisols, and Luvisols). Soil sampling was conducted from depths ranging from 0 to 30 cm (0–50; 5–10, 10–20, and 20–30 cm), ensuring a broad spectrum of sample variability across different times and locations. The reflectance of soil 64 soil samples was measured within the range of 400 to 900 nm, and the corresponding concentrations of soil organic carbon and majoritarian minerals, calcium carbonate, quartz, phyllosilicates, K-Feldspar, and plagioclase were determined for each sample. Partial least squares analysis was employed to construct prediction models using a calibration dataset comprising 66% of randomly selected samples. The remaining 33% of samples were utilized for model validation. The prediction models for the measured soil chemical properties yielded R2 values ranging from 0.14 to 0.79. Only SOC and limestone provided accurate prediction models. These findings hold promise for developing a soil health indicator tailored for site-specific crop management. However, the complex composition of soil organic carbon and calcium carbonate in certain soils underscores the importance of careful interpretation and validation of remote sensing data, as well as the need for advanced modeling approaches that can account for the interactions between multiple soil constituents.

Web-based spatial decision support system for precision agriculture: A tool for delineating dynamic management unit zones (MUZs)

Precision agriculture (PA) is a farming management concept that aims to provide agronomic, economic, and environmental benefits. One of the fields of research in PA is the delineation of Management Unit Zones (MUZs). MUZs are the sub-division of fields featuring an inter-zonal variation delineated by agronomists for on-field PA operations. To develop MUZs, three factors typically need to be considered: input multi-dimensional data, procedures to process the information, and the optimal number of zones a field should be divided into. PA uses digital technologies to collect and analyze a large amount of data, outline MUZs, monitor crops, and carry out site-specific crop management. Web-based spatial decision support systems (WB-SDSS) can provide users with tools that ease the complex procedures for PA. The objective of this study is twofold: on the one hand, we developed a free and open source (FOSS) WB-SDSS to facilitate the implementation and use of such tools for delineating MUZs and monitoring crops; on the other, a MUZs outline procedure was developed based on Sentinel-2 and Planetscope time series data, and spatio-temporal dynamic clustering model using fuzzy c-means. Our study highlighted that the WB-SDSS might be a helpful solution for harmonizing data collected from different sources, easing the implementation and use of complex geospatial procedures for PA, and delineating MUZs. We tested the system on a particularly representative farm in the Emilia Romagna region (Northern Italy), with 512 hectares of durum wheat crops. Using the WB-SDSS, we quickly delineated homogeneous zones for 27 fields in the study area during the phenological cycle of durum wheat (November 2018-June 2019).

Multi-growth stage plant recognition: A case study of Palmer amaranth (Amaranthus palmeri) in cotton (Gossypium hirsutum)

Many advanced image-based precision agricultural technologies for plant breeding, field crop research, and site-specific crop management hinge on the reliable detection and phenotyping of plants across highly variable morphological growth stages. Convolutional neural networks (CNNs) have shown promise for image-based plant phenotyping and weed recognition, but the ability to recognize growth stages, often with stark differences in appearance, is uncertain. Palmer amaranth (Amaranthus palmeri) is a particularly challenging weed plant in cotton (Gossypium hirsutum) production; due to high genetic diversity, it exhibits highly variable plant morphology across growth stages over a growing season, as well as between plants at a given growth stage. This paper investigates eight-class growth stage recognition of A. palmeri in cotton as a challenging detection case study for You Only Look Once (YOLO) architectures. In total, 26 architecture variants from YOLO v3, v5, v6, v6 3.0, v7, and v8 are compared on an eight-class growth stage dataset of A. palmeri. The highest mean average precision (mAP@[0.5:0.95]) for recognition of all growth stage classes was 47.34% achieved by v8-X, with inter-class confusion across visually similar classes. With all growth stages grouped as a single class, performance increased, with a maximum mAP@[0.5:0.95] of 67.05% achieved by v7-Original. Single-class recall of up to 81.42% was achieved by v5-X, and precision of up to 89.72% was achieved by v8-X. Class activation maps (CAM) were used to understand model attention on the complex dataset. Fewer classes, grouped by visual or size features improved performance over the ground-truth eight-class dataset. Successful growth stage detection highlights the substantial opportunity for improving plant phenotyping and weed recognition technologies with open-source object detection architectures. Additionally, the first open-access, benchmarking growth stage dataset ‘Palmer amaranth growth stages – 8 (PAGS8)’ is presented, which is available here: https://weed-ai.sydney.edu.au/datasets/5c78d067-8750-4803-9cbe-57df8fae55e4.

Comparing quasi-3D soil moisture derived from electromagnetic induction with 1D moisture sensors and correlation to barley yield in variable duplex soil

Farms in Western Australia (WA) are highly variable in soil texture and water retention capacity; therefore, information of soil moisture spatial variability in the field is important for effective crop management. In practice, farmers often rely on point sensors to determine soil moisture in their fields for crop planning. The limitation of point measurements to account for spatial variability highlights the need to develop methods that can assess soil moisture across variable broadacre fields. This information can enable more effective site-specific crop management practices. In this study, we used a mobile non-intrusive electromagnetic induction (EMI) sensor to map soil apparent electrical conductivity (ECa) to predict soil moisture levels at three different depths (0–0.5, 0.5–0.8 and 0.8–1.6 m) across the field and compared it with barley yield production. To convert the depth specific electrical conductivity (EC) from EMI surveys into soil moisture estimates, calibrations between electrical resistivity tomography (ERT) to volumetric moisture were used. These calibrations were developed for the different soil textural classes of the field, with R2 of 0.97–0.99. There was no significant difference between the estimated soil moisture from EMI surveys and the point moisture measurements obtained from soil moisture sensors and soil sample extracts, with Pearson R values of 0.64 for 0–0.5 m depth and 0.73 for 0.5–0.8 m depth. Additionally, correcting the temperature drift relative to the measured soil temperature on EMI surveys by 10% in dry season and 31% in wet season, improved the moisture estimation results. This study successfully demonstrated the effectiveness of spatial soil moisture estimation using EMI sensor in a field characterized by horizontal and vertical soil texture variability. However, no significant correlation was found between the mapped soil moisture or soil texture with barley yield. This may be due to relatively high initial moisture levels resulting from two years of fallow rotation.

Generic optimization approach of soil hydraulic parameters for site-specific model applications

Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R2 of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R2 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model.

Spatial Variability in Soil Micronutrients and Soil Separates of Ujjain Tehsil of Ujjain District of Madhya Pradesh, India

Mapping of soil properties is an important operation as it plays an important role in the knowledge about soil properties and how it can be used sustainably. Knowledge of soil variability of any region is crucial for development of site-specific management practices for that region as this will enhance the crop productivity and maintain the good soil health. With this background, present study was conducted to quantify the spatial variability of different soil physical properties and soil available micronutrients in Ujjain tehsil (Ujjain) district of Madhya Pradesh state, India. A total one hundred fifty geo-coded surface (0-15 cm depth) soil samples, were collected across the study area. These samples were analyzed using standard method for different some soil properties viz: soil texture (sand, silt and clay) and soil available micronutrients, viz. extractable zinc (Zn), copper (Cu), manganese (Mn) and iron (Fe) in laboratory. The range of sand, silt, clay, Zn, Cu, Fe and Mn in the study region were varied from 9.15 to 24.06 %, 24.00 to 41.55 %, 40.20 to 58.60 %, 0.12 to 1.66 mg kg-1, 2.06 to 6.22 mg kg-1, 3.70 to 10.40 mg kg-1, and 2.41 to 14.64 mg kg-1, respectively. The data were analyzed using standard statistical methods and geostatistics, which included creating semi-variograms and mapping by standard kriging procedures. Semi-variograms were produced for soil properties and their regional distributions were plotted. The observed soil parameters were best represented by four models: Exponential, Circular, Gaussian, and Hole effect. The modelled variables showed strong and moderate spatial dependencies, as demonstrated by the Nugget/Sill (Co/Co+C) ratio. The distribution maps of soil features may serve as a reference for implementing site-specific crop management in soils with comparable characteristics. Further, this research indicates the relevance of GIS- application in soil variability investigations.

Quantifying agricultural characteristics using several indicators and extensive field surveys in an ecologically fragile watershed of India

The present research was carried out in an ecologically fragile watershed of North-East India to comprehend the agricultural growth and development by quantifying its status employing several indicators, viz. physiological density, crop diversification, crop intensity and concentration. The study has demonstrated the use of primary data collected through extensive field surveys based on multistage sampling method in conjunction with a relevant secondary database. The findings revealed marked variation within the region in cropping patterns and associated aspects. The physiological density ranges between 1.91 to 85.54, cropping intensity ranges from 69.02% to 186.42%, crop diversification (6.42 to 100.14) and crop concentration (0.32 to 4.49), respectively. Continuous monitoring of the agricultural environment and adopting site-specific crop management practices supported by relevant information would boost the agricultural outcome and sustainability. Thus, the study would assist in framing guidelines to improve the agricultural landscape of the region

Spatial Variability of Soil Macronutrients and Chemical Properties in Ujjain Tehsil of Ujjain District of Madhya Pradesh, India

Understanding the characteristics of soil and how it may be utilised sustainably makes soil property mapping a crucial process. To improve crop yield and preserve healthy soil, it is essential to understand the heterogeneity of the soil in each location in order to establish site-specific nutrient management strategies for that area. The current research was carried out in the Ujjain tehsil (Ujjain) district of Madhya Pradesh state, India, with the aim of measuring the spatial variability of several soil macronutrients and soil chemical characteristics. Throughout the research region, 150 geo-coded surface soil samples with a depth of 0 to 15 cm were collected. The soil characteristics, namely pH, electrical conductivity (EC), soil organic carbon (SOC), available nitrogen (Av-N), available phosphorous (Av-P), available potassium (Av-K), and available sulphur (Av-S), were assessed in these samples using standard methods. The research region's ranges for soil pH, EC, SOC, Av-N, Av-P, and Av-K were 7.01 to 8.15, 0.10 to 0.79 dSm-1, 0.30-0.60%, 139.00 – 235.00 kg ha-1, 8.00 – 25.60 kg ha-1, and 301.00 – 463.00 kg ha-1, in that order. The data were analysed using classic statistics and geo--statistics by constructing semi-variograms and mapping by ordinary kriging techniques. Semi-variograms were calculated for soil characteristics and their spatial distributions were mapped. Best-fit models for measured soil properties were Exponential, Circular, Gaussian and Hole effect with Nugget/Sill (Co/Co+C) ratio for modelled variables indicated strong and moderate spatial dependences. The distribution maps of soil attributes could be utilized as a guide for site-specific crop management in similar soils. Further, this study demonstrates the usefulness of GIS- application in soil variability studies.

Role of 5G Technology in Enhancing Agricultural Mechanization

Advanced mechanization strategies such as site specific crop management machinery, Unmanned Aerial Vehicles (UAVs), robotic harvesters relies greatly on the precision technologies and IoT. Present networks (3G/4G), WiFi are in limited availability and bandwidth that cause delays in data transfer (latency) and hinder effective data transfer. But high-speed communication system, reliable and secured data connection is required for precision agriculture, cyber physical management systems and use of IoT in farm operations. Loss of data connection between devices, sensors or data servers’ impacts the precision and effectiveness of technology applied. High data transfer volumes and low latency data exchange were the major advantages of 5G technology, facilitates its use in precision farming and IoT applications in a most efficient way. It also enables the autonomous and robotic machinery for various operations in the aspects of agriculture and livestock production. The major application of 5G in mechanization is control systems for robotic harvesters, drones, autonomous vehicles, surveillance etc. Remote locations such as hill farms has the inherent constraints such as remoteness, marginality, slope etc., remains underutilized, can be developed with advanced communication systems. Even though there are several constraints in adoption of 5G technology, such as shortage in electricity supply, lower range coverage, the technology can ensure raise in agricultural production through mechanization. The chapter discusses the advancements in application of 5G technology in agricultural mechanization.

Growth and instability of rice production: a district level analysis in West Bengal, India

West Bengal is the largest rice-producing state in the country. It contributes significantly to the national production pool and is one of the important rice consuming state. It is essential to study the growth pattern of rice production in the state. The district wise information of growth rate and instability is not available. Also the association of growth rate and instability has not been studied. Hence the present study was conducted to compute the district wise growth rate and instability in the area, production and yield of rice in West Bengal state and to examine the association between growth rate and instability. The district-wise data on rice was sourced from the Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare, Government of India for the period 1983-84 to 2019-20. The data has been divided into five periods for analysis. Compound annual growth rate and Cuddy-Della Valle Instability index have been computed for the five periods. The instability was divided into five classes. These classes are very low instability, low instability, medium instability, high instability and very high instability.The highest growth rate for area and production was recorded in Nadia district during period I (1983-34 to 1989-90) while the highest growth rate for yield was recorded in South Dinajpur district during period IV (2010-11 to 2019-20). The growth rate for production and yield was positive for all the districts during periods IV (2010-11 to 2019-20) and V (1983-84 to 2019-20). Only one district recorded a negative growth rate for production during period I (1983-84 to 1989-90) and for yield during the period I (1983-34 to 1989-90), III (2000-01 to 2009-10) and IV (2010-11 to 2019-20). The instability varied from 0.63 to 26.57 percent in area, 1.49 to 19.46 percent in production and 0.70 to 45.95 per cent in yield across the districts over five periods.None of the districts recorded very high instability for area, production and yield; high instability for area and production; and medium instability for yield.Most of the districts showed very low instability for area and yield during periods I (1983-34 to 1989-90), II (1990-91 to 1999-2000), III (2000-01 to 2009-10) and IV (2010-11 to 2019-20); and low instability during period V (1983-84 to 2019-20). For production, the highest number of districts recorded low instability during periods I (1983-34 to 1989-90), III (2000-01 to 2009-10), and V (1983-84 to 2019-20) and very low instability during period II (1990-91 to 1999-2000) and IV (2010-11 to 2019-20). The growth in area is moving around zero, which indicates little scope in area expansion. The increase in rice demand can achieved through introduction of new high yielding varieties and application of site specific crop management techniques. For this a comprehensive database may be developed at disaggregated level to implement the policy at district level in West Bengal.

Transformative Role of Remote Sensing in Advancing Horticulture: Optimizing Sustainability, Efficiency and Resilience

The field of horticulture, vital for addressing global challenges like food security and sustainable agriculture, has been revolutionized by remote sensing technology. This comprehensive review explores the transformative impact of remote sensing on horticulture, emphasizing its role in optimizing resource utilization, promoting environmental sustainability, and mitigating the effects of climate change. Remote sensing, encompassing a range of sensors, satellites, and data analysis techniques, enables the collection of critical information from a distance, providing insights into crop health, soil conditions, water availability, and more. Precision agriculture, including the use of GPS and GIS, is integrated with remote sensing to enhance agricultural efficiency while minimizing environmental impacts. Site-Specific Crop Management (SSCM) is highlighted as a key component of precision agriculture, enabled by geospatial technologies, including remote sensing. It discusses how remote sensing systems, with their multispectral and multi-temporal capabilities, support various horticultural applications such as crop yield estimation, abiotic and biotic stress management, crop classification, canopy measurement, crop area estimation, and even crop insurance validation. The use of Geographic Information Systems (GIS) and the Global Positioning System (GPS) in tandem with remote sensing is explored in the context of spatial analysis, mapping, and precise navigation.

Early detection of the Mediterranean Fruit Fly, Ceratitis capitata (Wied.) in oranges using different aspects of remote sensing applications

Mediterranean Fruit Fly, Ceratitis capitata (Diptera: Tephritidae) is regarded as an important pest of orange (Citrus). Early detection of pest infestations enables theoptimal application of preventative and control measures. This study was carried out under laboratory conditions, in order to predict and monitor orange pest infestations. Consequently, the scope was to find a remote sensing application that can help in the prediction of Mediterranean Fruit Fly infestation in oranges with the least loss in production. Spectroscopic and thermal imaging techniques were investigated, as effective tools in determination of pest infestation and damage in orange fruits. According to the findings, the optimum spectral zones that can be used to discriminate and differentiate between healthy (non-infected) orange fruit and infected ones were red and near infrared bands. Six vegetation indices were calculated to analyze the Field Spectral measurements. By calculating the NPCI (Normalized Pigment Chlorophyll Index), it was found that NPCI values for infected orange fruits were higher in comparison to healthy ones. Thermal imaging showed that the infected orange fruit temperatures were on average 0.8 °C higher than that of healthy fruits. As the maximum temperature differential (MTD) between healthy and infected fruits were 23.7–24.5 °C, respectively. These spectral reflectance curves were useful for researchers working on Site-specific crop management, as they can use remote sensing to detect individual fruit infections. Also, this technique should be used as a powerful and non-destructive method for assistance in agriculture.

Customer Value Proposition for Precision Farming

A company's value proposition is established by the distinctive benefits and values it offers to its clients. Site-specific crop management (SSCM) and precision agriculture (PA) are farming management techniques that use technology to monitor and evaluate certain areas and crops. Between 2023 and 2030, the market for precision agriculture is anticipated to increase at a CAGR of 12.6%, reaching $15.6 billion. Precision farm planning, field maps, crop scouting, yield maps, and the estimation of input quantities are driving factors in this growth, along with the Internet of Things and advanced analytics. A company's commitment to influencing the attitudes, beliefs, and behavior of its target market is stated in its value proposition. The goal of the study was to address agricultural issues and increase productivity and profitability for banana farmers in the Anand and Umreth Talukas. Educate stakeholders, decision-makers, and service providers on how to support precision farming techniques. The study, conducted in Anand and Umreth Taluka, Gujarat, involved 50 banana growers with over 2 ha of land, using a descriptive design and semi-structured survey. Formulating a customer value proposition for identifying the problems faced by banana growers will help in understanding the current issues and areas of improvement within the agricultural sector. The findings of this research were contributing to the knowledge and understanding of precision farming in Anand and Umreth Talukas. Customers place a premium on product quality, the accessibility of the sales staff, and price, while farmers must deal with issues including disease, weather, nutrition, insect pests, and irrigation. Irrigation was viewed as less significant than weather and illness, which were found the major problems in the study area. Decision-making in agriculture depends heavily on weather forecasts and practices. A substantial effort was needed to improve agricultural practices because of the influence of climate change, input market accessibility, and remote farm access.

GIS Based Spatial Prediction of Major Nutrient Status of Soils at Farm Scale for Site Specific Crop Management

ABSTRACT In the current research, Global Positioning System (GPS) based 121 surface soil samples were collected and analyzed in the laboratory for different soil fertility parameters. The results showed that the bulk density (BD) of soils of 1.39 Mg m−3, dark gray and grayish brown, sandy clay loam, neutral to slightly alkaline soil reaction (pH), safe in electrical conductivity (EC), low to medium in organic carbon (OC) and non-calcareous in nature. The nitrogen (N), phosphorus (P), potassium (K) and sulfur (S) were analyzed as low, medium, high and low with NI of 1.26, 2.19, 2.69 and 1.31, respectively. In the Farm, field (F) wise, OC, N, P, K and S content in soil varied from 4.89 (F20) to 7.33 g kg−1(F15), 196.71 (F20) to 264.25 kg ha−1 (F15), 4.10 (F19) to 27.04 kg ha−1 (F16), 292.72 (F2) to 674.80 kg ha−1 (F13) and 3.99 (F18) to 13.75 mg kg−1 (F13), respectively. Post hoc results also revealed that fields and land use types were significantly different. Ordinary Kriging in Geo-statistical tools revealed that the exponential model was best fitted for, N, P, K, OC, √CaCO3 and √K, which exhibited moderate spatial dependence (>25 but < 75%), whereas Gaussian model was used for BD, pH and CaCO3. Correlation ranges(m) for P was 717.57 m and exhibited strong spatial dependency (<25%). The J-Bessel model was used for EC and√EC. The spherical model was used for S which showed 57.95% spatial dependence. The spatial maps could be used as a basis for precision farming.

Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery

The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 &gt; 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions.

Site-Specific Seed Yield Prediction of Red Fescue (Festuca rubra L.) Based on Drone Imaging and Local Regression Models

Yield maps give farmers information about growth conditions and can be a tool for site-specific crop management. Combine harvesters may provide farmers with detailed yield maps if there is a constant flow of a certain amount of biomass through the yield sensor. This is unachievable for grass seeds because the weight of the intake is generally too small to record the variation. Therefore, there is a need to find another way to make grass seed yield maps. We studied seed yield variation in two red fescue (Festuca rubra) fields with variation in management and soil fertility, respectively. We estimated five vegetation indices (VI) based on RGB images taken from a drone to describe yield variation, and trained prediction models based on relatively few harvested plots. Only results from the VI showing the strongest correlation between the index and the yield are presented (Normalized Excess Green Index (ExG) and Normalized Green/Red Difference Index (NGRDI)). The study indicates that it is possible to predict the yield variation in a grass field based on relatively few harvested plots, provided the plots represent contrasting yield levels. The prediction errors in yield (RMSE) ranged from 171 kg ha−1 to 231 kg ha−1, with no clear influence of the size of the training data set. Using random selection of plots instead of selecting plots representing contrasting yield levels resulted in slightly better predictions when evaluated on an average of ten random selections. However, using random selection of plots came with a risk of poor predictions due to the occasional lack of correlation between yield and VI. The exact timing of unmanned aerial vehicles (UAVs) image capture showed to be unimportant in the weeks before harvest.

Geometrical Characterization of Hazelnut Trees in an Intensive Orchard by an Unmanned Aerial Vehicle (UAV) for Precision Agriculture Applications

Knowledge of tree size is of great importance for the precision management of a hazelnut orchard. In fact, it has been shown that site-specific crop management allows for the best possible management and efficiency of the use of inputs. Generally, measurements of tree parameters are carried out using manual techniques that are time-consuming, labor-intensive and not very precise. The aim of this study was to propose, evaluate and validate a simple and innovative procedure using images acquired by an unmanned aerial vehicle (UAV) for canopy characterization in an intensive hazelnut orchard. The parameters considered were the radius (Rc), the height of the canopy (hc), the height of the tree (htree) and of the trunk (htrunk). Two different methods were used for the assessment of the canopy volume using the UAV images. The performance of the method was evaluated by comparing manual and UAV data using the Pearson correlation coefficient and root mean square error (RMSE). High correlation values were obtained for Rc, hc and htree while a very low correlation was obtained for htrunk. The method proposed for the volume calculation was promising.

Prediction of winter wheat yield and dry matter in North China Plain using machine learning algorithms for optimal water and nitrogen application

Accurate prediction of crop yield and dry matter as well as optimized water and nitrogen management can favor rational decision-making for farming systems. Combining high-performance computing with innovative technologies of big data processing, machine learning (ML) advances data-intensive science and provides an important supporting frame for crop yield prediction. This paper evaluated the performance of five ML algorithms, including linear regression (LR), decision tree (DT), support vector machine (SVM), ensemble learning (EL), and Gaussian process regression (GPR), for winter wheat (Triticum aestivum L.) yield and dry matter prediction using data collected from previous studies conducted within the last twenty years in the North China Plain (NCP). In addition, winter wheat yield and dry matter were explored using the best algorithm, while polynomial functions were proposed that could describe the relationship of water and nitrogen application with winter wheat yield and dry matter. Results confirmed that the GPR model outperformed all other models for predicting the yield (R2 = 0.87) and dry matter (R2 = 0.86) of winter wheat. The prediction errors of the GPR model for maximum yield and dry matter of winter wheat were 5.8 % and 1.1 %, respectively. The yield and dry matter of winter wheat in the NCP could be predicted by the GPR model and polynomial functions, and the optimal water and nitrogen application for maximum yield and dry matter could be obtained. The results provide insight into site-specific crop management.

Estimation of Rice Aboveground Biomass by UAV Imagery with Photosynthetic Accumulation Models

The effective and accurate aboveground biomass (AGB) estimation facilitates evaluating crop growth and site-specific crop management. Considering that rice accumulates AGB mainly through green leaf photosynthesis, we proposed the photosynthetic accumulation model (PAM) and its simplified version and compared them for estimating AGB. These methods estimate the AGB of various rice cultivars throughout the growing season by integrating vegetation index (VI) and canopy height based on images acquired by unmanned aerial vehicles (UAV). The results indicated that the correlation of VI and AGB was weak for the whole growing season of rice and the accuracy of the height model was also limited for the whole growing season. In comparison with the NDVI-based rice AGB estimation model in 2019 data (R2 = 0.03, RMSE = 603.33 g/m2) and canopy height (R2 = 0.79, RMSE = 283.33 g/m2), the PAM calculated by NDVI and canopy height could provide a better estimate of AGB of rice (R2 = 0.95, RMSE = 136.81 g/m2). Then, based on the time-series analysis of the accumulative model, a simplified photosynthetic accumulation model (SPAM) was proposed that only needs limited observations to achieve R2 above 0.8. The PAM and SPAM models built by using 2 years of samples successfully predicted the third year of samples and also demonstrated the robustness and generalization ability of the models. In conclusion, these methods can be easily and efficiently applied to the UAV estimation of rice AGB over the entire growing season, which has great potential to serve for large-scale field management and also for breeding.

Efficiency of assimilating leaf area index into a soybean model to assess within-field yield variability

Methods for accurately estimating within-field yield are essential to improve site-specific crop management and resource use efficiencies, which would be a major step toward sustainable intensification of agricultural systems. We set out to assess the accuracy of within-field soybean yields predicted by two data assimilation methods and to assess these methods’ assimilation efficiency (AE). Yields were estimated by assimilating remotely sensed leaf area index (LAI) data from Sentinel-2 into a soybean crop growth model on a pixel basis. The LAI data was integrated into the model by Ensemble Kalman Filtering (EnKF) or by recalibrating with the Subplex algorithm (recalibration-based). An open-loop setting which only integrates information on the soil layers was used as a baseline scenario for quantifying the AE. We assessed both data assimilation techniques on eight fields (3067 pixels) in the Corn Belt region (Nebraska, Kansas and Kentucky) in the United States. The data set encompassed substantial variation in crop growth conditions: three growing seasons (2018, 2019 and 2020), rainfed and irrigated fields, and early and late planting dates. Ground truth yield acquired from combine monitors was used to validate the yield estimations. Agreement between predicted and observed yield at pixel level was two times higher for both data assimilation methods compared to the open-loop. The root mean square error (RMSE) was 476 kg.ha−1 (RRMSE of 10 %) in the recalibration-based method and 573 kg.ha−1 (RRMSE of 12 %) in the EnKF-based method. For both data assimilation methods, assimilating the LAI improved predictions for 68 % of the pixels. For a further 12 % of pixels, there was no accuracy improvement. For the remaining 20 %, AE was positive for one of the two assimilation methods. The high proportion of pixels with positive AE indicates the potential for overcoming the limitations in applying crop models at high spatial resolution by integrating a crop growth indicator. Assimilating an in-season indicator of crop growth (LAI) into a soybean model made it possible to adjust the simulation pathway, thereby greatly improving the accuracy of the yield estimations at the pixel level. This study elucidates the practical applications of data assimilation strategies for fine-scale within-field crop yield mapping.