Abstract: Agriculture and related industries contribute greatly to the Indian economy. One of our goals is to make our Indian farmers more aware of the variety of E farming features that can be of great use to them. Food security is seriously threatened by crop diseases, but due to a lack of the essential infrastructure in many places throughout the world, early detection of these diseases is still challenging. The ability to diagnose sickness using a smartphone has been made conceivable by recent a breakthrough in computer vision enabled by deep learning and rising smartphone adoption worldwide. Using a public dataset of larger images of sick and healthy plant leaves taken under controlled conditions, we train a deep convolutional neural network to recognise several crop species and diseases . Overall a quick way to get there is to use the technique of training deep learning models using progressively sizable and freely available image datasets to massively worldwide illness diagnosis in smartphone-aided cropping. A web application built with Python and Machine Learning and packed with capabilities is used to support the E-Agro sector.
Keywords: Agriculture, Farming, Crops, Climate, Deep Learning, Neural network, Image Processing.
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DOI:
10.17148/IJARCCE.2023.12221
