Open AccessLeaf Disease Classification Using Convolutional Neural Network
Author(s) -
Mitali V. Shewale,
Rohin Daruwala
Publication year - 2021
Publication title -
aijr proceedings
Language(s) - English
Resource type - Conference proceedings
ISSN - 2582-3922
DOI - 10.21467/proceedings.114.4
Subject(s) - convolutional neural network , automation , computer science , domain (mathematical analysis) , artificial intelligence , deep learning , artificial neural network , agriculture , the internet , architecture , machine learning , engineering , world wide web , geography , mathematics , mechanical engineering , mathematical analysis , archaeology
Agriculture is a major domain that contributes a lot for building up the country’s Economy; contributing to the GDP area synthesis of 17.9%. India stands second in production of agricultural products. Promising technologies such as Internet of Things, Machine Learning, Deep learning, Artificial neural networks contributes towards the most effective and reliable solutions by providing the most feasible solutions in making of different domain modernization through automation in monitoring and maintenance of agricultural fields with minimum human intervention. This paper presents a convolutional neural network based customized VGG framework and a lightweight architecture for the classification of tomato leaves affected with various diseases. Experimental analysis is performed on publically available PlantVillage dataset. After rigorous experiment we fined tuned the CNN model to obtain mAP of 83.33%.