Precision Agriculture最新文献

The article defines the key determinants of adopting precision agriculture technologies and digitalisation. The research objectives are fulfilled by the systematic review and meta-analysis of relevant studies, identified and selected in accordance with the PRISMA protocol in the Web of Science and Scopus databases. The findings emphasize the importance of socio-economic factors, such as education, age, and farm size. High technical literacy and adequate information about new technologies—including their expected profitability—are crucial for assessing the benefits of precision agriculture and digitalisation, on which a more considerable expansion of these technologies into the practice of agricultural entities depends. Large and capital-intensive enterprises are more likely to implement new technologies in production practices, especially if they are led by younger and more educated managers who are more open to modern technologies and are more willing to take risks.

The global attention to the utilization of unmanned aerial vehicle remote sensing drones in crop disease-wide detection has led to the urgent need to find an adapted model for different environmental conditions. Therefore, the current study has focused on spatiotemporal usage of different multispectral cameras in acquiring spectral reflectance models of in-field rice bacterial blight stresses. Where, long short-term memory (LSTM) model was compared with the other models in transfer learning strategy for assessing the blight stress severity. The results revealed that by extracting 30% of the data from the target domain and transferring it to the source domain, the adaptability of the model across different sites was effectively enhanced. Besides, LSTM showed high tuning transfer efficiency that demonstrated optimal predictive performance and the shortest training time in transfer tasks. Its coefficient of the prediction set was 0.82, and its residual prediction deviation has reached 2.26. In practice, LSTM enabled the acquisition of reliable prediction results at a minimal sample collection cost while circumventing feature reduction resulting from inter-domain data alignment. When the transfer ratio reached 20%, the coefficient of determination of the prediction set reached 0.71, and the residual prediction deviation reached 1.79. The novelty of this study came from the transfer learning efficiency in improving the model’s application capabilities across the different sites, environment, and unmanned aerial vehicle in farmland disease detection.

The first stage in the precision agriculture cycle has been a vital study area in recent years because it allows soil testing followed by data analysis. In this stage, a strategic delineation of site-specific management zones acquires a particular interest because it enables site-specific treatment to improve crop yield by efficiently using the input of resources. The delineation of site-specific management zones problem is to determine the minimum number of zones that cover the entire field so that each zone’s homogeneity is significant according to a specific biological, chemical, or physical soil property. Furthermore, the delineated zones should be orthogonal-shaped to be practical for agricultural machinery. This work has proposed a new bio-inspired algorithm, specifically an Estimation of Distribution Algorithm, based on a decoder that heavily relies on the Disjoint-Set algorithm and a new reactive penalized fitness function that detects unfeasible solutions. The new methodology improves the solutions presented in the literature by using a new search engine that drastically reduces the computational times of similar algorithms. Our algorithm has been tested with the literature benchmark, considering a new reactive penalization in the fitness function. It obtains the best solutions for 66.66% of the instances benchmark compared to the best literature method. Due to the algorithm’s efficiency, a new set of larger instances is introduced to test the scalability and robustness of the method. It obtained an efficiency of 79.3%.

Introduction

Optimizing fruit quality and yield in agriculture requires accurately monitoring leaf nitrogen (N) status spatially and temporally throughout the growing season. Standard remote sensing approaches for assessing leaf N rely on proxies like vegetation indices or leaf chlorophyll a + b (C_ab) content. However, limitations exist due to the C_ab-N relationship’s saturation and early nutrient deficiency insensitivity.

Methods

The study utilized Sentinel-2 satellite imagery to estimate a set of plant biochemical traits in large almond orchards in a two-year study. These traits, including leaf dry matter, leaf water content, and leaf C_ab retrieved from the radiative transfer model, were used to explain the observed variability of leaf N. Airborne hyperspectral imagery-derived leaf N using C_ab and solar-induced fluorescence served as a benchmark for validation.

Results

Results demonstrate that plant traits quantified from Sentinel-2 were strongly associated with leaf N variability across the orchard, with a strong contribution from the estimated leaf C_ab content and leaf dry matter biochemical constituent, outperforming the consistency of vegetation indices. The Sentinel-2 model explaining leaf N variability yielded r² = 0.82 and nRMSE = 13% in a two-year dataset, obtaining consistent performance and trait contribution across both years.

Conclusion

This study highlights the potential application of Sentinel-2 satellite imagery for monitoring leaf N variability in almond tree orchards. Incorporating plant biochemical traits allows for a more consistent and reliable prediction of leaf N compared to traditional vegetation indices over two years, making it a promising method for precision agriculture applications.

On-farm experiments (OFE) typically do not account for limitations of grain yield monitors such as the dynamics of grain flow through a large combine. A common question asked within OFE is how ground speed impacts yield estimates from grain yield monitors. Therefore, the objective of this study was to determine if combine ground speed influences the ability of grain yield monitors to report yield differences for OFE. Six sub-plot treatment resolutions that differed in length (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) of imposed yield variation were harvested at combine ground speeds of 3.2 and 6.4 km h⁻¹. Treatments were replicated 3 times. The intentional yield variability in maize (Zea mays L.) was created by alternating nitrogen application (0–202 kg N ha⁻¹) across the treatment lengths. A factory installed yield monitor (YM3) and a third-party platform (P1) using the controller area network (CAN) bus data were used to collect yield data and compared to plot combine data collected from adjacent rows for each treatment length along a pass. Comparisons were made between each YM and plot combine yield estimates for each low and high yield treatment lengths. Combine ground speed did not significantly impact yield estimates (p ≥ 0.31 for all speed interactions) except speed * method due to lack of calibration. There were no significant differences the computed yield differences (all speed interactions p ≥ 0.40). Combine ground speed did not significantly influence the ability of yield monitoring technologies (i.e. mass flow sensor) to estimate the average low and high yields (p ≥ 0.31 for all speed interactions for individual plot lengths except when operating outside the calibrated flow range of the mass flow sensor. Operating outside the calibrated flow range of the mass flow sensor resulted in mass flow rate being overestimated by an average of 23% for both yield monitors (YM3 and P1).

Spatial and temporal variability of the soil’s apparent electrical conductivity (ECa) and other soil attributes can be analyzed using specific digital platforms for precision agriculture, contributing to agricultural management decision-making. Understanding these variations enables more efficient and sustainable management practices tailored to each area’s characteristics, leading to higher crop yields and reduced environmental impacts. A critical question arises: should ECa measurement be done regularly or just once? This study aims to evaluate the spatial and temporal variability of soil’s apparent electrical conductivity to determine if a single ECa measurement can characterize spatial soil variability. The experiment was conducted in two areas under different management practices in Céu Azul, PR, Brazil. One area operates under a direct planting system, cultivating soybeans in the summer and rotating with wheat or corn during the winter. The second area is used as pasture during the winter and planted with corn or soybeans in the summer. ECa data from 2013 to 2016, along with chemical and physical soil attributes from 2013, were retrieved from our laboratory database. Additionally, ECa data were collected on 19/05/2022, 18/10/2022, and 10/03/2023. All ECa measurements were performed using an EM38-MK2 conductivity meter in horizontal dipolar and drag mode. ECa normalization methods such as range, average, and standard score were employed to mitigate temporal influences partially. Data was processed using the AgDataBox web platform, which included data cleaning, data interpolation, creation of thematic maps, delineation of management zones, and spatial correlation matrix procedures. Thematic maps revealed that ECa spatial variability exhibited a stable pattern. Both areas showed significant cross-correlation among topography and most soil chemical and physical attributes. The study concluded that ECa measurement could be performed once as a co-variable for interpolating other variables since the ECa pattern remained stable in both areas. The average method was the most effective normalization method in both areas. Furthermore, management zones (MZs) were delineated using equivalent normalized ECa (ECa_Eq) (mS/m) with the three data normalization methods. The agreement between MZs was sufficient to conclude that the influence of the normalization methods can be ignored.

Nitrogen (N) is vital for plant growth, but its imbalance can negatively affect crop yields, the environment, and water quality. This is especially crucial for California’s almond orchards, which are the most N-hungry nut crop and require substantial N for high productivity. The current practices of uniform and extensive N application lead to N leaching into the groundwater, creating environmental hazards. Traditional remote sensing methods often rely on data-driven approaches that work well statistically (achieving a high R² value) with one dataset but aren’t adaptable across different datasets. To create a more robust, data-driven model, one would typically need a vast and varied collection of datasets. Our goal, however, is to develop a more universally applicable model using smaller datasets, typical of commercial orchards, that can accurately estimate N content in tree canopies, regardless of differences in spatial, spectral, and temporal data. In this study, we investigate and evaluate multiple remote sensing approaches for estimating N concentration in Californian almonds, utilizing hyperspectral imaging at the canopy level. We assess various classical vegetation indices, machine learning models, and a physics-informed 3D radiative transfer model. While cross-validated results show comparable results for radiative transfer models and best-performing machine learning models, most single vegetation indices are not capable of exceeding the baseline model (:fleft(mathbf{x}right)=bar{y}) and thus had R² value less than 0. Despite being less commonly used, 3D radiative transfer modeling shows promise as a strong and adaptable method, producing results that are comparable to the best machine learning models.

Soil sampling is crucial for capturing soil variability and obtaining comprehensive soil information for agricultural planning. This article evaluates the potential of MaxVol, an optimal design method for soil sampling based on selecting locations with significant dissimilarities. We compared MaxVol with conditional Latin hypercube sampling (cLHS), simple random sampling (SRS) and Kennard-Stone algorithm (KS) to evaluate their ability to capture soil data distribution. We modeled spatial distributions of soil properties using simple kriging (SK) and regression kriging (RK) interpolation techniques and assessed the interpolation quality using Root Mean Square Error. According to the results, MaxVol performs similarly or better than popular sampling designs in describing soil distributions, particularly with a smaller number of points. This is valuable for costly and time-consuming field surveys. Both MaxVol and Kennard-Stone are deterministic algorithms, unlike cLHS and random sampling, providing a reliable sampling scheme. Thus, the proposed MaxVol algorithm enables obtaining soil property distributions based on environmental features.

There has been a recent surge in the number of studies that aim to model crop yield using data-driven approaches. This has largely come about due to the increasing amounts of remote sensing (e.g. satellite imagery) and precision agriculture data available (e.g. high-resolution crop yield monitor data), as well as the abundance of machine learning modelling approaches. However, there are several common issues in published studies in the field of precision agriculture (PA) that must be addressed. This includes the terminology used in relation to crop yield modelling, predicting, forecasting, and interpolating, as well as the way that models are calibrated and validated. As a typical example, many studies will take a crop yield map or several plots within a field from a single season, build a model with satellite or Unmanned Aerial Vehicle (UAV) imagery, validate using data-splitting or some kind of cross-validation (e.g. k-fold), and say that it is a ‘prediction’ or ‘forecast’ of crop yield. However, this poses a problem as the approach is not testing the forecasting ability of the model, as it is built on the same season that it is then validating with, thus giving a substantial overestimation of the value for decision-making, such as an application of fertiliser in-season. This is an all-too-common flaw in the logic construct of many published studies. Moving forward, it is essential that clear definitions and guidelines for data-driven yield modelling and validation are outlined so that there is a greater connection between the goal of the study, and the actual study outputs/outcomes. To demonstrate this, the current study uses a case study dataset from a collection of large neighbouring farms in New South Wales, Australia. The dataset includes 160 yield maps of winter wheat (Triticum aestivum) covering 26,400 hectares over a 10-year period (2014–2023). Machine learning crop yield models are built at 30 m spatial resolution with a suite of predictor data layers that relate to crop yield. This includes datasets that represent soil variation, terrain, weather, and satellite imagery of the crop. Predictions are made at both the within-field (30 m), and field resolution. Crop yield predictions are useful for an array of applications, so four different experiments were set up to reflect different scenarios. This included Experiment 1: forecasting yield mid-season (e.g. for mid-season fertilisation), Experiment 2: forecasting yield late-season (e.g. for late-season logistics/forward selling), Experiment 3: predicting yield in a previous season for a field with no yield data in a season, and Experiment 4: predicting yield in a previous season for a field with some yield data (e.g. two combine harvesters, but only one was fitted with a yield monitor). This study showcases how different model calibration and validation approaches clearly impact prediction quality, and therefore how they should be interpreted in data-driven crop yield modelling

Efficient mechanical harvesting of wild blueberries across uneven topographies calls for precise header height adjustments to optimize fruit picking. Conventionally, an operator requires manual adjustment of the harvester header to accommodate the spatial variations in plant height, fruit zone, and field terrain. This can result in inadequate header positioning, which leads to berry losses and increased operator stress. This study aimed to investigate the integration of machine learning techniques with real-time geo-location data to develop an innovative system to automate harvesting operations. A supervised machine learning Random Forest (RF) model was trained based on pre-defined header setting data and integrated with the harvester’s controller to predict and position the header height using real-time geo-location data from the Starfire (SF) 6000 Global Positioning System (GPS) receiver. During harvesting, the system’s performance was evaluated at tractor ground speeds (0.31, 0.45, and 0.58 ms⁻¹) and segment lengths (5, 10, and 15 m). Results indicated that segment size minimally affected the system’s ability to adjust header height. However, at the lowest segment length, 5 m, the coefficient of determination was 97.24, 98.12, and 82.71% for the 0.31, 0.45, and 0.58 ms⁻¹, respectively. This study provided convincing results for automating the harvester header based on pre-defined settings, marking a significant step toward complete automation of the wild blueberry harvester. Automation of wild blueberry harvesting can help to increase picking efficiency and enhance profit margins for growers to justify the ever-increasing cost of production.