Leveraging Transfer Learning with MobileNetV2 for Amla Leaf Spot Disease Detection in Agricultural Imagery

The detection of leaf spot disease in Amla (Indian gooseberry) has been a challenging task due to limited availability of large, high-quality datasets, varied environmental conditions, and the need for real-time monitoring. Traditional methods of disease detection rely heavily on manual inspection, which can be time-consuming, subjective, and prone to errors. Furthermore, the high variability in disease symptoms under different light and climate conditions makes it difficult to deploy a consistent solution across different regions. Current machine learning approaches often struggle with generalization, leading to poor performance in diverse agricultural environments. This work proposes a deep learning-based approach using MobileNetV2, a lightweight convolutional neural network (CNN), to address the challenges in detecting Amla leaf spot disease. The methodology leverages the AmlaLeafSpot-AgriVision and AmlaLeaf-SpotNet datasets for training and validation. Data augmentation techniques such as rotation, flipping, and zooming are applied to enhance the model’s robustness. MobileNetV2 is chosen for its efficiency in handling real-time applications on mobile and edge devices, providing a balance between performance and computational cost. Key features of this work include high accuracy in disease detection, low inference time suitable for deployment in mobile and IoT devices, and scalability for different field conditions. The system also offers automated disease monitoring, reducing the need for manual inspection and allowing for timely intervention in disease management.