Neural Greenery: Advancement in Plant Leaf Diseases Recognition Using Deep Learning

Abstract

Agriculture is the backbone of our country. Large section in our country is highly dependent on agriculture. High production of crop provides global food security. High production largely depends on health of plant. Plant health is greatly affected by pest, climatic condition etc. Thus, early detection of these disease is very necessary to stop further spread of the disease. In this paper, we have proposed a real-time plant leaf disease detection system exploring deep learning techniques. This model is capable of recognizing several types of plant disease using real time data of leave. Our approach utilizes convolutional neural networks (CNNs) to automatically extract relevant features from leaf images, enabling accurate classification of healthy and diseased leaves. Firstly, all enlargement is applied on dataset to increase the sample size. Convolution Neural Network (CNN) is used with several convolution and pooling layers. Botanical community dataset is used to train the model. After training the model, it is examined properly to validate the results. Keywords: Plant Disease, Convolution Neural Network (CNN), Deep Learning, Agriculture, and Botanical community.