JoWUA

Abstract

The adoption of automated image-based classification systems was motivated by the fact that traditional manual approaches are subjective, time-consuming, and often erroneous. This study details the process of illness detection in pigeon pea leaves using the Mendeley Data Pigeon Pea Leaf Illness Dataset, which is accessible to the public. Its ability to distinguish between objects is substantially enhanced by the proposed model, which integrates an optimised convolutional neural network (CNN) architecture with feature extraction layers and attention-based augmentation. To use TensorFlow to run tests on a system with an NVIDIA GPU to see how the suggested model compares to baseline models and typical designs like VGG16, ResNet50, DenseNet121, and then MobileNetV2. When it comes to pigeon pea leaf disease identification, this paper employs a modified CNN to circumvent shortcomings of traditional deep learning constructions like VGG16, ResNet50, and DenseNet121. Common issues with traditional CNNs include their high processing cost, their inability to handle real-world differences in lighting, occlusion, and background noise, their sluggish inference speed, and the likelihood of overfitting on small agricultural datasets. Improved feature extraction, regularisation, and lightweight optimisation are all part of the suggested Modified CNN's architectural overhaul, which fixes these problems and makes the network more efficient and accurate. Quantitative evaluations employing illustrate the proposed model's superior performance, attaining an overall accuracy of 97.86%, exceeding prior work by 5.4%. The results show that the suggested deep learning-based method can be a useful and scalable way to automatically find Pigeon Pea diseases. This will help precision agriculture and smart crop monitoring systems.