Singh, RajeevArya, Sunayana2019-11-202019-11-202019-08http://krishikosh.egranth.ac.in/handle/1/5810135750Agriculture productivity has a big contribution to every nation's economy. Nowadays disease persistence in crops and plants are a major concern for farmers, so the detection process of diseases plays a very effective part in the farm sector and for farmers. To protect productivity, quantity, and quality of plants, proper care is mandatory. The traditional phenomenon requires a huge amount of work, time and continuous monitoring of farm for disease classification and detection. The latest methods in E-Agriculture for identification and detection of diseases like image processing, machine learning, and deep learning have been widely used. Deep learning uses Convolutional Neural Networks (CNN) for image classification as it gives the most accurate results in solving real- world problem. CNN has various pre-trained architecture like AlexNet, GoogleNet, DenseNet, SqueezeNet, ResNet, VGGNet etc. In this thesis, we have used CNN and AlexNet Architecture for detecting the disease in Mango and Potato leaf and compare the accuracy and efficiency between these architectures. The dataset containing 4004 images were used for this work. The images for potato were taken from plantvillage website (which is an open database of images published in 2016), while images for mango were collected from GBPUAT field location. The results show that accuracy achieved from AlexNet is higher than CNN architectureennullThesis