MaizeNet: A Deep Learning Approach for Effective Recognition of Maize Plant Leaf Diseases

Abstract

The presence of various maize plant leaf diseases has significantly decreased both the quality and quantity of crop production. In order to take the appropriate steps to prevent the occurrence of plant leaf diseases, it is essential to track and recognize such infections during the planting period. However, correct recognition of numerous maize diseases is difficult to achieve because the currently employed automated solutions are operationally complicated or only effective on samples with plain backgrounds. While real-world scenarios are suffering from huge sample distortions like the effect of noise, clutter in the background, and blurring of the leaf regions that increase the complexity of the recognition procedure. To alleviate the above-listed problems, a deep learning (DL) approach called the MaizeNet is proposed for the correct localization and classification of various maize crop leaf disorders. We have presented an improved Faster-RCNN approach that utilizes the ResNet-50 model with spatial-channel attention as its base network for the computation of deep keypoints which are then localized and categorized into various classes. The proposed work is tested on a standard database named Corn Disease and Severity that contains samples from three different classes of maize plant diseases which are captured under diverse conditions such as complex background, brightness, and color and size variations. The MaizeNet model attains an average accuracy score of 97.89% along with mAP value of 0.94, which is showing the effectiveness of our approach for locating and classifying the numerous types of maize leaf infections.

Keywords

• Attention
• classification
• deep learning
• faster-RCNN
• localization
• Maize disease