Presenters:
Yash Kishorbhai Pansheriya
California State University, Northridge
Mango is a very essential fruit in the Indian agricultural economy, given the demand locally, as well as the export market. But diseases affecting the mango leaves as under moot significant perils to the mango yield and the quality and therefore there is need for constant disease detection and appropriate means to address it. This paper contributes an artificial intelligence system for mango leaf disease diagnosis and categorization using deep learning approaches. With the dataset of 4,000 high-resolution images downloaded from MangoLeafBD, the reality investigates the performance of MobileNet, VGG16, InceptionV3, DenseNet121, AlexNet, ResNet50, and EfficientNetB7.
The system actually helps in terms of image analysis, disease feature extraction and classification in order to give an accurate diagnosis to the farmers for timely action to be taken. The preprocessing of data, effective training practices and hard testing of the system makes it functional in real world problems. Some of the advantages of the proposed system include: Decreased manual detection of pest and use of pest control chemicals. The overall implication is that it will make sustainable farming practices and minimizes economic losses.
The research avails itself in explaining the possible utility of artificial intelligence as a revolutionary tool in altering aged overdue agricultural liberty. It provides farmers with ways of making appropriate decisions using the data on crop management and productivity levels. It also sets up an efficient and scalable approach to protecting the mango farming via incorporating the new neural network technologies. This contribution seeks to improve the food security and promote good practices in mangos farming business.
