Improvement of the YOLOv5 Model in the Optimization of the Brown Spot Disease Recognition Algorithm of Kidney Bean.

The kidney bean is an important cash crop whose growth and yield are severely affected by brown spot disease. Traditional target detection models cannot effectively screen out key features, resulting in model overfitting and weak generalization ability. In this study, a Bi-Directional Feature Pyramid Network (BiFPN) and Squeeze and Excitation (SE) module were added to a YOLOv5 model to improve the multi-scale feature fusion and key feature extraction abilities of the improved model. The results show that the BiFPN and SE modules show higher heat in the target location region and pay less attention to irrelevant environmental information in the non-target region. The detection Precision, Recall, and mean average Precision (mAP@0.5) of the improved YOLOv5 model are 94.7%, 88.2%, and 92.5%, respectively, which are 4.9% higher in Precision, 0.5% higher in Recall, and 25.6% higher in the mean average Precision compared to the original YOLOv5 model. Compared with the YOLOv5-SE, YOLOv5-BiFPN, FasterR-CNN, and EfficientDet models, detection Precision improved by 1.8%, 3.0%, 9.4%, and 9.5%, respectively. Moreover, the rate of missed and wrong detection in the improved YOLOv5 model is only 8.16%. Therefore, the YOLOv5-SE-BiFPN model can more effectively detect the brown spot area of kidney beans.

Non-destructive monitoring of maize LAI by fusing UAV spectral and textural features.

Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R2 = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.

Modeling of the potential geographical distribution of naked oat under climate change.

Introduction: Naked oat (Avena sativa L.), is an important miscellaneous grain crop in China, which is rich in protein, amino acids, fat and soluble dietary fiber. The demand for functional foods is gradually increasing as living standards rise, and the output of minor cereals in China is increasing annually. The planting layout of naked oat is scattered and lacks planning, which seriously restricts the development of the naked oat industry. The increase in miscellaneous grain production will not only be impacted by cultivation methods and management techniques, but the potential impact of global climate change needs to be considered. North China is the main area for naked oat production, worldwide. Methods: In this study, the potential distribution range of naked oat in North China was forecast based on historical distribution data and the Maxent model under climate change conditions. The performance of the model was relatively high. Results: The results indicated that the most suitable area for the potential geographic distribution of naked oat in North China was 27.89×104 km2, including central and northeastern Shanxi, and northeastern and western Hebei and Beijing, gradually moving northward. The core suitable area increased, and the distribution of naked oat had an obvious regional response to climate warming; the main environmental factors affecting the potential geographic distribution were precipitation factor variables (precipitation seasonality (variation coefficient)), terrain factor variables (elevation) and temperature factor variables (temperature seasonality (Standard Deviation*100)). Discussion: In this study, the Maxent model was used to analyze and predict suitable areas for naked oat in North China, and the distribution of suitable areas was accurately divided, and the main climatic factors affecting the distribution of naked oat were identified. This research provides data support and theoretical support for the optimal planting zone of naked oat in North China.