Abstract:
Paddy leaf diseases pose a significant threat to crop fields and agricultural productivity,
necessitating efficient detection methods for timely intervention. This study provides a
comprehensive overview of recent advancements in paddy leaf disease detection,
introducing an innovative approach utilizing deep learning models. Several integrated
models for image classification were evaluated in the research, including VGG-19, VGG16, MobileNetV2, DenseNet, ResNet-50, Xception, Inception, and the customized
Inception-V3. The findings of the study revealed that MobileNetV2 emerged as the top performer,
achieving the highest accuracy of 95%. This model demonstrated exceptional performance
in accurately identifying paddy leaf diseases. On the other end of the spectrum, Xception
exhibited the lowest accuracy at 85%. The remaining models, including VGG-19, VGG16, ResNet-50, DenseNet, Inception, and the customized Inception-V3, showcased varying
degrees of accuracy falling between these two extremes. This research underscores the potential of transfer learning models, including the
customized Inception-V3, in enhancing disease detection accuracy. It emphasizes the
significance of continuous innovation and improvement in disease detection
methodologies to support sustainable and cost-effective farming practices. With the
customized Inception-V3 model achieving an accuracy of 93%, this study highlights its
promising performance in contributing to the advancement of agricultural technology.
