1Pacific Academy of Higher Education and Research University, Udaipur-313 024. Rajasthan, India
2Ramrao Adik Institute of Technology, D Y Patil Deemed to be University, Nerul, Navi Mumbai-400 706, Maharashtra, India
*Corresponding Author: Anuja A. Gharpure,Pacific Academy of Higher Education and Research University, Udaipur-313 024. Rajasthan, India. Email: aagharpure@gmail.com
Mango is a commercially important fruit. Diseases affecting mangoes may lead to commercial losses. Early detection is needed for better production. The traditional method of disease detection is time-consuming and labour-intensive, affecting quality and yield. To accelerate the disease detection process, the machine learning algorithms will contribute to substantial development in disease detection in mango fruits.
The datasets used in this research work are publicly available, viz. mango disease dataset and mango leaf dataset based on diseases found in mangoes. The proposed work focused on the convolutional neural network (CNN) and vision transformer (ViTs) for the detection of diseases found in the mangoes. The lightweight models, such as ShuffleNet, MobileNet, ResNet were used in the research work to extract features. Further, ViT was used to detect and classify diseases. From the results, perfect accuracy was obtained for leaf diseases for all tested models.
As compared to the leaf dataset used in the research work, the mango disease dataset gave lower accuracy; 89.17% for ShuffleNet and MobileNet and 86.23% for the ResNet model. These findings indicate that lightweight CNN models can provide high accuracy while being computationally efficient. Automation in disease detection will set a promising pathway in real-world mango production environments, supporting precision agriculture and reducing commercial losses.
Agriculture AI, Convolutional neural networks, Deep learning, Machine learning, Mango disease detection, Vision transformers
