Dicamba Injury on Soybean Assessed Visually and with Spectral Vegetation Index

A new BWO-based RGB vegetation index and ensemble learning strategy for the pests and diseases monitoring of CCB trees using unmanned aerial vehicle

Introduction

The Cinnamomum Camphora var. Borneol (CCB) tree is a valuable timber species with significant medicinal importance, widely cultivated in mountainous areas but susceptible to pests and diseases, making manual surveillance costly.

Methods

This paper proposes a method for detecting CCB pests and diseases using Unmanned aerial vehicle (UAV) as an advanced data collection carrier, capable of gathering large-scale data. To tackle the high cost and challenging data processing issues associated with traditional hyper-spectral/multi-spectral sensors, this method only relies on UAV visible light RGB bands. The process first involves calculating and normalizing 24 visible light vegetation indices from the UAV RGB images of the monitoring area, along with the original RGB bands. To account for the collinearity relationship between indices, the random forest variable importance and correlation coefficient iterative analysis algorithm are employed to select indices, retaining the most important or lowest collinearity multiple vegetation indices. Subsequently, the Beluga Whale Optimization (BWO) algorithm is utilized to generate a new vegetation index, which is then combined with the multi-threshold segmentation method to propose a BWO-weighted ensemble strategy for obtaining the final pests and diseases detection results.

Results and discussion

The experimental results suggest that the new BWO-based vegetation index has a higher feature expression ability than single indices, and the new BWO-based ensemble strategy can yield more accurate detection results. This approach provides an effective means for low-cost pests and diseases detection of CCB trees.

Cocrystal engineering for sustained release of dicamba: Mitigating secondary drift and reducing leaching

The off-target effects of herbicides present significant challenges in agricultural practices, posing serious threats to both ecological systems and human health. Dicamba, one of the most widely used herbicides, is particularly problematic due to its high volatility and water solubility, which can lead to rapid environmental dispersal, non-target toxicity, and groundwater contamination. To mitigate these issues, we synthesized a novel cocrystal of dicamba and phenazine (DCB-PHE cocrystal) through a combination of theoretical prediction and mechanochemical screening. The DCB-PHE cocrystal was characterized using single-crystal and powder X-ray diffraction, Fourier-transform infrared spectroscopy (FT-IR), and thermal analysis. Compared to pure dicamba, the DCB-PHE cocrystal exhibited a substantial reduction in volatility by 59 % and a decrease in equilibrium solubility by up to 5.4 times across various temperatures (15 °C, 25 °C, 35 °C). Additionally, the dissolution rates were significantly lowered by over 94 %. Leaching experiments demonstrated that the DCB-PHE cocrystal reduced total leachate by 4.9 % and delayed percolation. In greenhouse trials, the DCB-PHE cocrystal caused less damage to exposed soy plants and enhanced herbicidal activity against target weeds, with fresh weight reduction of chicory and ryegrass by 32 % and 28 %, respectively, at the highest dosage. Furthermore, safety assays confirmed that the DCB-PHE cocrystal's safety profile was comparable to that of dicamba in terms of its impact on wheat, and it did not exhibit increased genotoxicity to broad beans. These findings suggest that the DCB-PHE cocrystal is a promising candidate for reducing the environmental impacts of dicamba while maintaining its herbicidal efficacy.

Low-cost and precise traditional Chinese medicinal tree pest and disease monitoring using UAV RGB image only

Accurate and timely pest and disease monitoring during the cultivation process of traditional Chinese medicinal materials is crucial for ensuring optimal growth, increased yield, and enhanced content of effective components. This paper focuses on the essential requirements for pest and disease monitoring in a planting base of Cinnamomum Camphora var. Borneol (CCB) and presents a solution using unmanned aerial vehicle (UAV) images to address the limitations of real-time and on-site inspections. In contrast to existing solutions that rely on advanced sensors like multispectral or hyperspectral sensors mounted on UAVs, this paper utilizes visible light sensors directly. It introduces an ensemble learning approach for pest and disease monitoring of CCB trees based on RGB-derived vegetation indices and a combination of various machine learning algorithms. By leveraging the feature extraction capabilities of multiple algorithms such as RF, SVM, KNN, GBDT, XGBoost, GNB, and ELM, and incorporating morphological filtering post-processing and genetic algorithms to assign weights to each classifier for optimal weight combination, a novel ensemble learning strategy is proposed to significantly enhance the accuracy of pest and disease monitoring of CCB trees. Experimental results validate that the proposed method can achieve precise pest and disease monitoring with reduced training samples, exhibiting high generalization ability. It enables large-scale pest and disease monitoring at a low cost and high precision, thereby contributing to improved precision in the cultivation management of traditional Chinese medicinal materials.

Differentiate Soybean Response to Off-Target Dicamba Damage Based on UAV Imagery and Machine Learning

The wide adoption of dicamba-tolerant (DT) soybean has led to numerous cases of off-target dicamba damage to non-DT soybean and dicot crops. This study aimed to develop a method to differentiate soybean response to dicamba using unmanned-aerial-vehicle-based imagery and machine learning models. Soybean lines were visually classified into three classes of injury, i.e., tolerant, moderate, and susceptible to off-target dicamba. A quadcopter with a built-in RGB camera was used to collect images of field plots at a height of 20 m above ground level. Seven image features were extracted for each plot, including canopy coverage, contrast, entropy, green leaf index, hue, saturation, and triangular greenness index. Classification models based on artificial neural network (ANN) and random forest (RF) algorithms were developed to differentiate the three classes of response to dicamba. Significant differences for each feature were observed among classes and no significant differences across fields were observed. The ANN and RF models were able to precisely distinguish tolerant and susceptible lines with an overall accuracy of 0.74 and 0.75, respectively. The imagery-based classification model can be implemented in a breeding program to effectively differentiate phenotypic dicamba response and identify soybean lines with tolerance to off-target dicamba damage.