Precision Agriculture最新文献

Mechanical weeding is an important part of integrated weed management. It destroys weeds between (interrow) and in (intrarow) crop rows. Preventing crop damage requires precise detection and tracking of the plants. In this work, a detection and tracking algorithm was developed and integrated on an intrarow hoeing prototype. The algorithm was developed and validated on 12 rows of 950 cauliflower plants. Therefore, a methodology was provided to automatically generate a label based on the crop plants’ Global Navigation Satellite System (GNSS) position during data collection with a robot platform. A CenterNet architecture was adjusted for plant centre detection by comparing different encoder networks and selecting the optimal hyperparameters. The monocular camera projection error of the plant centre detections in pixel to 3D coordinates was evaluated and used in a position- and velocity-based tracking algorithm to determine the timing for intrarow hoeing knife actuation. A dataset of 53k labelled images was created. The best CenterNet model resulted in an F1 score on the test set of 0.986 for detecting cauliflower centres. The position tracking had an average variation of 1.62 cm. Velocity tracking had a standard deviation of 0.008 (mathrm {m,,s^{-1}}) with respect to the robot’s operational target velocity. Overall, the entire integration showed effective actuation of the prototype in field conditions. Only one false positive detection occurred during operation in two test rows of 135 cauliflowers.

Optimizing the timing of rice paddy drainage and harvest is crucial for maximizing yield and quality. These decisions are guided by rice grain moisture content (GMC), which is typically determined by destructive plant samples taken at point locations. Providing rice farmers with predictions of GMC will reduce the time burden of gathering, threshing and testing samples. Additionally, it will reduce errors due to samples being taken from unrepresentative areas of fields, and will facilitate advanced planning of end-of-season drain and harvest timing. This work demonstrates consistent relationships between rice GMC and indices derived from Sentinel-2 satellite imagery, particularly those involving selected shortwave infrared and red edge bands (r=0.84, 1620 field samples, 3 years). A methodology was developed to allow forecasts of grain moisture past the latest image date to be provided, by fusing remote sensing and accumulated weather data as inputs to machine learning models. The moisture content predictions had root mean squared error between 1.6 and 2.6% and (hbox {R}^2) of 0.7 with forecast horizons from 0 to 28 days. Time-series grain moisture dry-down predictions were summarized per field to find the optimal harvest date (22% grain moisture), with an average RMSE around 6.5 days. The developed methodology was operationalized to provide rice growers with current and projected grain moisture, enabling data-driven decisions, ultimately enhancing operational efficiency and crop outcomes.

Purpose

Extracting the extent of wheat lodging is essential for post-disaster emergency response, disaster assessment, and accurate agricultural insurance claims. However, traditional methods for identifying lodged crops often lack flexibility, exhibit low levels of automation, and suffer from inefficiency.

Methods

This study proposes a rapid identification algorithm for wheat lodging, utilizing adaptive thresholding and a two-peak search of UAV imagery for reliable extraction of lodging regions. Initially, the red, green, and blue (RGB) visible band characteristics of UAV images after wheat lodging are analyzed. Subsequently, an Enhanced Wheat Lodging Index (EWLI) is proposed to quantitatively represent the lodging state. Second, a two-peak search dynamic thresholding algorithm, based on the square chunking of wheat lodging, is proposed to automatically determine thresholds for extracting winter wheat lodging regions.

Results

Experimental results demonstrate that the Enhanced Wheat Lodging Index (EWLI) effectively represents wheat lodging, while the two-peak search dynamic thresholding algorithm achieves robust performance. The proposed method achieves an overall accuracy of 96%, an F1 score of 0.97, and a Kappa coefficient exceeding 0.95, surpassing the performance of the OTSU method (maximum inter-class variance) and the KSW method (maximum entropy) with global thresholding.

Conclusion

The proposed method is applicable to diverse wheat lodging scenarios and demonstrates robust stability in identification accuracy. Key advantages include lightweight modeling, adaptive threshold determination, and the elimination of human intervention, making it an efficient, reliable, and highly practical approach for wheat lodging monitoring.

The use of thermal-based crop water stress index (CWSI) has been studied in many crops in semi-arid regions and found as an effective method in detecting real-time crop water status of commercial fields remotely and non-destructively. However, to our knowledge, no previous studies have validated the usefulness of CWSI in a temperate crop like wild blueberries. Additionally, the temporal changes of the water status estimation model has not been well-studied. In this multi-year study, Unoccupied Aerial Vehicle (UAV)-borne thermal imageries were collected in 2019, 2020, and 2021 to test the temporal effects and the impact of different approach-based reference temperatures (T_wet, wet reference temperature; T_dry, dry reference temperature) on leaf water potential (LWP) estimation models using CWSI in two large adjacent wild blueberry fields in Maine, United States. We found that different sampling dates have a significant impact on LWP estimation models using CWSI_SE (statistical T_wet and empirical T_dry reference) and CWSI_SS (statistical T_wet and statistical T_dry reference). Further, CWSI_BB calculated with bio-indicator-based T_wet and T_dry reference was found more effective (r² = 0.79) in estimating LWP in 2021, compared to the CWSI_SE and CWSI_SS approaches in 2019 (r² = 0.34 & r² = 0.36), 2020 (r² = 0.38 & r² = 0.44) and 2021 (r² = 0.43 & r² = 0.46). CWSI_BB -LWP model-based crop water status maps show high variation in the crop water status of wild blueberries, even in an evenly irrigated field, suggesting the potential of UAV-borne thermal cameras to detect real-time crop water status within the field, with the CWSI_BB calculated from bio-indicator-based references being more reliable. Our results could be used for precision irrigation to increase the overall water use efficiency and profitability of wild blueberry production.

Durum wheat (Triticum durum Desf.) yield should be maximized to meet the growing global demand for pasta production. Precision agriculture (PA) could play a pivotal role in reaching this goal by correctly defining management zones (MZ) and optimizing the use of energy inputs. The aim of the work was to understand the relationship between MZ generated from observed yield data and those generated using a time series of Sentinel-derived vegetation indices (i.e. NDVI) obtained from satellite images and soil properties. For this purpose, two field trials of 10 ha each, cultivated with durum wheat, were carried out in Southern Italy. The results suggested a better strategy for defining MZs by merging soil characteristics and temporal NDVI stability maps. The on-the-go technology used for soil resistivity mapping also represented an excellent tool for delineating stable and homogeneous areas within the fields and estimating soil properties. In particular, the soil clay content had a determining effect on the identification of homogeneous yield areas. However, the integration of historical NDVI data helped delineate MZs within each field. To validate this hypothesis, we integrated soil and NDVI data into a linear predictive model to predict grain yield at the field level. Our findings showed a good level of accuracy and a significant improvement in yield simulated values by combining soil with crop data (R² = 0.620; RMSE = 0.425). Further studies are needed to explore the potential of NDVI stability maps into a linear predictive model to predict grain yield at the field level.

Context

Serrated tussock (Nassella trichotoma) is a weed of national significance in Australia which offers little to no nutritional value to livestock, and has the potential to reduce carrying capacity and agricultural return of infested pastures.

Aims

The aim of this study was to adapt existing Convolutional Neural Networks (CNNs) for plant segmentation and spraypoint detection in the challenging environments of pastures.

Methods

CNNs that were designed for joint plant and stem segmentation in crop fields were repurposed for dual-task applications in pastures. Given the poor performance of these models in complex pasture environments, a new model drawing inspiration from the recently proposed ConvNeXt was developed, tested for its effectiveness on unseen field data, and enhanced with a novel normalization technique, called HistMatch.

Key results

Experimentation demonstrated that unlike pre-existing models, which were designed for the simpler environments encountered in early-stage crop fields, our model was able to generalize well to growing conditions not seen during training, achieving 0.807 mIoU and 0.796 F1-score for the plant and spraypoint tasks respectively. This is in comparison to pre-existing models, which achieved 0.270 - 0.454 mIoU and 0.073 - 0.496 F1-score for the same tasks. These results were further improved to 0.854 mIoU and 0.806 F1-score using HistMatch normalization. In spite of greater model complexity, our model had a inference time of 15.7 ms which was comparable to pre-existing models, and suitable for real-time applications.

Conclusion

Models with greater complexity are required for the relatively complex environments encountered in pastures, but this greater complexity need not come at the expense of real time capability. HistMatch normalization can improve model accuracy, and is particularly effective in cases where models are struggling to generalize well to testing conditions that vary significantly from those seen during training.

Implications and impacts

The successful adaptation and improvement of CNNs for weed management in pastures could significantly reduce the reliance on blanket herbicide application. HistMatch normalization could also be considered for other agricultural applications, including weed management and disease detection in crop fields and orchards.

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.