Two-step convolutional neural network classification of plant disease

Abstract

Indonesia is primarily an agricultural country, with farming being the primary source of income for most of its people. Unfortunately, crop production is vulnerable to plant diseases, which are usually caused by plant pests, resulting in a reduction in both the quantity and quality of the expected harvest. In addition to the large number of classes to predict, detecting and accurately classifying each disease on different plants can be difficult. We believe that limiting the number of classes to identify may improve classification accuracy. Thus, in this research, we propose a new approach, two-step convolutional neural network (CNN), which reduces the number of classes with a two-step classification approach. To begin, we identify the number of classes that can be reduced by categorizing them into different characteristics, namely, plant type classification and plant condition classification. Second, we deal with unbalanced datasets, which can result in poor performance, if overlooked. Finally, we compare the proposed two-step CNN to baseline CNN in terms of efficiency and effectiveness. Extensive experiments show that the two-step CNN outperforms the baselines, CNN and jellyfish-residual network (JF-ResNet), increasing accuracy by 4% and 2% to 99%, respectively. In addition, we also provide a simulation evaluation to ensure that this approach is applicable.