Deep learning based real-time detection of northern corn leaf blight crop disease using YoloV4

Abstract

Prior work leveraging neural networks in agriculture have been proposed and achieved significant results in autonomous classification of diseases in plants. One notable complication for classification using neural networks, however, is inability to acknowledge classes the model was not trained to identify. With the recent advancements in computer vision, we develop a convolutional neural network for the specific intent of detection of Northern Corn Leaf Blight via segmentation – resulting in a network which is resistant to diseases the model is not capable of classifying, and thus also reducing occurrences of Type I and Type II error. The model is trained on a publicly available dataset of maize images with Northern Corn Leaf Blight and annotations documenting precise locations of the disease in each image. We report the mean average precision (mAP) of the developed model and its effectiveness in real time detection with its latency and computational overhead. The impact of this research is a reliable means of identifying specific diseases in plants, reducing misclassification due to inability to classify, and facilitating the development of products that incorporate microcontrollers while demonstrating their ability to be used in real time disease detection.