3 results about "Brown blotch disease" patented technology

The invention relates to a rice disease detection method based on a YOLO algorithm, in particular to high-precision detection achieved by improving a YOLOv11 model. The method combines a plurality of innovative modules, and aims to improve the detection accuracy of rice diseases (such as bacterial leaf spot disease, brown spot disease and leaf tumor disease). By introducing a CPA-Enhancer image enhancement module, a bidirectional feature pyramid network (BiFPN), a CARAFE content perception feature recombination module and a CBAM double attention mechanism, the small target identification problem of rice leaf scab can be effectively solved, and the challenges of background interference and multi-scale feature fusion in a complex field environment are overcome. According to the method, through multi-stage optimization, the recognition precision and the detection speed of the model on rice diseases are remarkably improved, and compared with a traditional method, the method has obvious advantages in the aspects of small target detection and scab boundary detail recovery. Experimental results show that the model of the invention is superior to the existing YOLO series and other common deep learning models in multiple performance indexes, and is suitable for large-scale field real-time monitoring and intelligent agriculture application.

The invention belongs to the technical field of pesticide compounding, and particularly relates to a bactericidal composition containing sulfatrione and benziothiazolinone and application of the bactericidal composition. When the mass ratio of the effective components, namely, the prosulfuron (A) and the benziothiazolinone (B) is (1: 25)-(25: 1), the action mechanisms of the two effective components are complementary, synergistic interaction is achieved, the obvious control effect on wheat leaf blight, sclerotinia rot of colza, watermelon leaf blight and beet brown spot is achieved, and the lasting period is long.

This invention discloses a method and system for identifying rice leaf diseases based on an improved ResNet18 model, belonging to the fields of agricultural artificial intelligence and computer vision technology. The method uses ResNet18 as the backbone network and innovatively embeds convolutional block attention modules (CBAM) serially at the feature output of each residual basic unit. It utilizes a dual channel and spatial attention mechanism to recalibrate the feature map, effectively suppressing background noise and focusing on minute lesion features. Simultaneously, it constructs a cross-entropy loss function (L2-CE Loss) with L2 norm regularization constraints, improving training stability and generalization ability by constraining the magnitude of the output layer Logits vector. Experiments show that the method of this invention achieves an average identification accuracy of 99.67% for rice bacterial blight, rice blast, brown spot disease, and healthy leaves under complex backgrounds, significantly outperforming the traditional ResNet18 model. It exhibits higher generalization ability and robustness, making it suitable for real-time disease monitoring in the field.