Enhanced Classification of Sugarcane Diseases Through a Modified Learning Rate Policy in Deep Learning

Abstract

Motivated by the agriculture-centric economy of India, and specifically the challenges experienced in the sugarcane sector due to reduced yields from diseases including rust, red rot, yellow leaf, and mosaic, this study aims to harness effective deep-learning technologies for improved plant disease monitoring. The challenge of mitigating over-fitting, particularly when dealing with small datasets, is addressed through hyper-parameter tuning. In this study, we introduce an innovative modification to the learning rate decay policy, tested on a uniquely constructed small-sized database of sugarcane leaf images. This database encompasses five classes: healthy, rust, red rot, yellow leaf, and mosaic. To evaluate the effectiveness of the proposed learning rate policy, it was compared against multiple benchmark datasets and found to surpass established methods in performance metrics. This study introduces an additional exponential component into the learning rate policy to facilitate model convergence within the same number of epochs, thereby enhancing its performance over step, exponential, cosine, and exponential sine methods. A marginal improvement in scores was observed with the integration of the proposed learning rate policy and MobileNet-V2 as the backbone architecture. Remarkably, the MNIST dataset achieved a score of 99.9%, CIFAR-10 scored 92%, and the newly introduced database secured a score of 89%. These results underscore the efficacy of the proposed approach in enhancing the classification of sugarcane diseases.