Precision Agriculture最新文献

In recent years, there has been a significant progress in technologies used in 3D crop spraying. The inherent goal of applying these technologies has been to reduce drift, improve efficacy in the use of Plant Protection Products (PPP) and, consequently, reduce the amount of chemicals in fruit production, thus minimizing environmental impact and enhancing human health. In order to assess the study of this impact, deposition trials were conducted in an apple orchard at two different growth stages (BBCH72 and BBCH99). Three typical sprayers were used to perform these trials: the reference sprayer, representing the most popular one used by local farmers; the Best Management Practices (BMP) sprayer, symbolizing well-adjusted equipment according the target; and the VRA sprayer, a newly developed machine provided with ultrasonic sensors and the corresponding developed hardware to achieve an on-line pesticide rate adaption, according to the canopy dimensions. This VRA sprayer has been developed within OPTIMA H2020 EU project (www.optima-h2020.eu). The VRA sprayer effectively achieved similar or better values of deposition and coverage in the whole canopy target, using up to 35% less PPP rate, compared to the reference sprayer. Additionally, the developed VRA machine has demonstrated its ability to adapt the applied PPP rate to fundamental canopy parameters such as width and density, allowing to implement alternative pesticide rates, based on canopy characteristics (i.e. Leaf Wall Area), as a new method proposed by European and Mediterranean Plant Protection Organization (EPPO).

Rapid and accurate detection of fruits is crucial for estimating yields and making scientific decisions in litchi orchards. However, litchis grow in complex natural environments, characterized by variable lighting, severe occlusion from branches and leaves, small fruit sizes, and dense overlapping, all of which pose significant challenges for accurate detection. This paper addressed this problem by proposing a method that combines unmanned aerial vehicle (UAV) remote sensing and deep learning for litchi detection. A remote sensing image dataset comprising litchi fruit was first constructed. Subsequently, an improved algorithm, YOLOv7-MSRSF, was developed. Experimental results demonstrated that YOLOv7-MSRSF’s mean average precision (mAP) reached 96.1%, outperforming YOLOv7 and pure transformer algorithms by 3.7% and 20.6%, respectively. Tests on randomly selected 24 images demonstrated that integrating the Swin-transformer module into YOLOv7 improved litchi fruit detection accuracy under severe occlusion, dense overlapping, and variable lighting by 19.55%, 6.63%, and 13.94%, respectively. YOLOv7-MSRSF showed further improvements in these three complex conditions, with detection accuracy increasing by 26.99%, 9.82%, and 18.68%, respectively, reaching 89.16%, 97.79%, and 95.56%. Furthermore, the Real-ESRGAN algorithm significantly enhanced the YOLOv7-MSRSF model’s recognition accuracy of objects in low-resolution images captured by high-altitude drones. The average detected accuracy of three images collected at 27.5 m above the canopy reached a high value of 82.2%, which was improved by 70.6% compared with that (11.6%) before super-resolution processing. The proposed method offered valuable guidance for detecting small, dense agricultural objects in large-scale, complex natural environments.

Few strategies have been developed to effectively delineate management zones (MZs) in crop-pasture rotation (CPR) systems that accommodate site-specific management for multiple crops using a single map. This study aimed to propose and evaluate several feature selection approaches that account for multiple crops in CPR systems and propose a framework for MZ delineation in CPR systems that results in a single MZ map. The feature selection approaches were based on the spatial correlation between attributes (soil, crops, and terrain attributes) and yield variables (grain and pasture yield, spatial trend of yield, and yield temporal stability). This study was conducted in an area with an integrated crop-livestock system, featuring the CPR of soybean and pasture. The results showed that the approach based on yield temporal stability was the most effective for selecting relevant attributes used in the MZ delineation in CPR systems, resulting in greater differentiation among MZs. A higher number of MZs was needed (four zones), emphasizing the importance of carefully selecting the number based on variance reduction and yield differences to ensure that the final MZ map reflects the variability across all crops and guides their integrated management. The proposed framework is one of the first to use yield temporal stability for feature selection specifically aimed at delineating MZs in CPR systems. This approach improves the ability to select significant attributes used in the MZs delineation process, providing a better solution for improving input use efficiency and maximizing grain and pasture yield in integrated farming systems.

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.