Precision Agriculture最新文献

Assessing the grain yield of wheat remains a great challenge in field breeding trials.

Multispectral and RGB images acquired by UAVs offer a promising tool for in-season prediction yet with varying results during the growing season.

Therefore, enhancing prediction accuracy through optimizing multi-date models seems necessary but needs to be weighted with time and costs.

Multi-date models outperform single-date models, with repeated data collection during the grain-filling phase being most effective.

RGB indices can compete with multispectral indices.

Purpose

Management zones (MZs) are the subdivision of a field into a few contiguous homogeneous zones to guide variable-rate application. Delineating MZs can be based on geostatistical or clustering approaches, however, the joint use of these approaches is not usual. Here, we show a joint use of both techniques. The objective of this manuscript is twofold: (1) compare different procedures for creating management zones and (2) determine the relation of the MZs delineated with i) coffee yield maps and ii) the summarizing power of each method for each input variable inside the MZs delineated.

Methods

The techniques compared to summary spatial data were: (1) summarizing the variables into a soil fertility index (SFI), (2) the MULTISPATI-PCA technique, and (3) the multivariate Min/Max autocorrelation factors (MAF) approach. Then, clustering methods were applied to perform field partition into binary MZs (grouping lower and higher values of input variables).

Results and discussion

The MAF approach achieved the best field partition regarding clustering metrics (McNemar’s test, Silhouette Score Coefficient, and variance reduction). In this paper we did not use yields as a cluster variable but as a measure of success. MAF also was the best one for separating low- from high-yielding areas over the MZs. The results show that the proposed approach could be effectively used for management zone delineation.

Conclusions

This methodology facilitates evaluating innovative approaches in challenging spatial modeling scenarios, such as low-sampled fields with outliers. A wide range of summarization methods and clustering techniques are available, making this agnostic approach quite interesting for delivering MZ maps. This flexible approach can guide precision nutrient management in low-sampled areas, allowing the joint use of data science tools and agronomical knowledge to delineate variable rate application strategies.

Graphical abstract

Increasing problems with pesticide resistance and the adverse environmental side effects of pesticide use have increased the demand for developing alternative methods to control pests. Site-specific pest management can reduce the negative impact of pest management in horticulture and agriculture. In recent years, there has been an increasing focus on using laser beams to control pests by directing the laser beam toward the pest and killing or damaging it with heat. Lasers are energy demanding, and therefore, the laser beam should only be directed towards the pest and not irradiate the whole infested area. Precise location and identification of the pests can be done with artificial intelligence, and mirrors can direct the laser toward the target point of the pest. Using a laser beam with a diameter of 2 mm to control fifteen pests will only expose less than 0.02% of the area to the treatment. Therefore, laser is the most site-specific pest management method achievable. This article discusses the development of controlling pests with lasers and the advantages and disadvantages.

Soil compaction caused by heavy agricultural machinery poses a significant challenge to sustainable farming by degrading soil health, reducing crop productivity, and disrupting environmental dynamics. Field traffic optimization can help abate compaction, yet conventional algorithms have mostly focused on minimizing route length while overlooking soil compaction dynamics in their cost function. This study introduces Soil2Cover, an approach that combines controlled traffic farming principles with the SoilFlex model to minimize soil compaction by optimizing machinery paths. Soil2Cover prioritizes the frequency of machinery passes over specific areas, while integrating soil mechanical properties to quantify compaction impacts. Results from tests on 1000 fields demonstrate that our approach achieves a reduction in route length of up to 4-6% while reducing the soil compaction on headlands by up to 30% in both single-crop and intercropping scenarios. The optimized routes improve crop yields whilst reducing operational costs, lowering fuel consumption and decreasing the overall environmental footprint of agricultural production. The implementation code will be released with the third version of Fields2Cover, an open-source library for the coverage path planning problem in agricultural settings.

Purpose

The use of UAVs (Unmanned Aerial Vehicles) equipped with sensors such as laser scanners offers an alternative to conventional, labor-intensive manual measurements in agriculture, as they enable precise and non-destructive field surveys.

Methods

This paper evaluates the use of UAV-based laser scanning (RIEGL miniVUX-SYS) for estimating the crop height and the plant area index (PAI) of winter wheat. (Methods) It further introduces a novel ground classification method, enhancing early growth stage classification through sensor attributes like intensity and pulse shape deviation.

Results

The crop height estimation shows a high (R^2) score with (99.69~%) but a systematically lower estimate with a mean absolute error of 7.4 cm. The potential of PAI derivation is analyzed with three different estimation strategies and provides an overview and limitations of the approach. Additional weighting based on the scan angle and the adaptation of the extinction coefficient present results with (R^2) of (97.66~%) and a mean absolute error of 0.25.

Conclusion

The investigation discusses further the impact of the calculated gap fraction, which describes the ratio of laser beams penetrating through the crop canopy in comparison to the total number of measurements.

The provision of ecosystem services from Mediterranean permanent grasslands is threatened due to shifting management practices and environmental pressures. This observational study tested the hypothesis that Land Surface Phenology (LSP) parameters from high-resolution satellite data can characterize various permanent grasslands to support conservation and improvement practices. The potential of LSP derived from Sentinel-2 data in identifying the multi-layer mixed vegetation of Mediterranean grasslands, including silvopastoral systems, that were well-characterized from an agronomic and ecological perspective through field surveys, was assessed. Forty-nine polygons, representing eleven sites characterized by different grassland vegetation, soil, climate and management, were identified in Sardinia (Italy). Sentinel-2 satellite images from 2017 to 2023 were processed to derive NDVI, and LSP parameters were calculated using TIMESAT 3.3 software. The Canonical Correspondence Analysis showed a significant association (p < 0.05) between a combination of LSP metrics used as proxies of a set of relevant agronomical indicators. It was then possible to differentiate managed vs. abandoned grasslands (e.g., start and peak of the season significantly later under unmanaged grasslands, p < 0.0001), wooded grasslands vs. open grasslands(e.g., base value significantly higher in woodlands and wooded grasslands, p < 0.0001) across environmental gradients (altitude) and management practices (green-down rate significantly higher under mown than unmown areas, p < 0.0001). The LSP parameters proved to be promising proxies to characterize agronomic features (e.g., length of the growing season, earliness, forage availability, mowing and grazing intensity, unpalatable species) of Mediterranean permanent grasslands. The characterization can support management design or monitoring to detect abandonment or environmental pressures early.

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.