Lightweight deep and cross residual skip connection separable CNN for plant leaf diseases classification

Abstract

Crop diseases have an adverse effect on the yield, productivity, and quality of agricultural produce, which threatens the safety and security of the global feature of food supply. Addressing and controlling plant diseases through implementation of timely disease management strategies to reduce their transmission are essential for ensuring minimal crop loss, and addressing the increasing demand for food worldwide as the population continues to increase in a steadfast manner. Crop disease mitigation measures involve preventive monitoring, resulting in early detection and classification of plant diseases for effective agricultural procedure to improve crop yield. Early detection and accurate diagnosis of plant diseases enables farmers to deploy disease management strategies, such interventions are critical for better management contributing to higher crop output by curbing the spread of infection and limiting the extent of damage caused by diseases. We propose and implement a deep and cross residual skip connection separable convolutional neural network (DCRSCSCNN) for identifying and classifying leaf diseases for crops including apple, corn, cucumber, grape, potato, and guava. The significant feature of DCRSCSCNN includes residual skip connection and cross residual skip connection separable convolution block. The usage of residual skip connections assists in fixing the gradient vanishing issue faced by network architecture. The employment of separable convolution decreases the number of parameters, which leads to a model with a reduced size. So far, there has been limited exploration or investigation of leveraging separable convolution within lightweight neural networks. Extensive evaluation of several training and test sets using distinct datasets demonstrate that the proposed DCRSCSCNN outperforms other state-of-the-art approaches. The DCRSCSCNN achieved exceptional classification and identification accuracy rates of 99.89% for apple, 98.72% for corn, 100% for cucumber, 99.78% for grape, 100% for potato, 99.69% for guava1, and 99.08% for guava2 datasets.