YOLO-SDW: A method for detecting infection in corn leaves

Corn is an important source of renewable energy, which can be converted into ethanol through fermentation and distillation. Ethanol, as a clean and renewable energy source, can not only be used as an additive and alternative to gasoline but also can be used to manufacture chemicals such as acetaldehyde, ethylene glycol, ethylamine, ethyl acetate, acetic acid, chloroethane, etc. However, after infection, corn leaves may rot, turn yellow, and produce a large number of viruses, leading to a decrease in corn yield. Timely and accurate detection of infected corn leaves is an important measure for the prevention and treatment of corn leaf infection. The existing target detection algorithms have unsatisfactory effects on the detection and classification of infected corn leaves. To quickly and accurately detect corn leaves and classify disease-infected leaves to achieve ideal detection results in practical corn leaf detection situations, this paper proposes a new algorithm called yolo-SDW based on the yolov5 algorithm. The yolo-SDW algorithm introduces a spatial depth conversion convolution (SPD-Conv) into the backbone network of the original yolov5 algorithm, replacing the traditional stride convolution with SPD-Conv. Very paramount for operational efficiency, this will enhance the adaptability and usability of the model. A vision Transformer with deformable attention (DAT) is introduced. This attention automatically adjusts the attention distribution according to the data needs when processing images of different scales, thereby improving the accuracy and performance of the model. Meanwhile, a novel Wise-IOU V3 loss function is used as the bounding box loss function, resulting in a lower false positive rate when dealing with dense targets. The experimental results show that the improved algorithm has a 6.4 % increase in average precision mAP compared to the original yolov5 algorithm, reaching 83.5 %. The speed has increased by 3.2 %, while precision and recall rates have also been significantly improved.