DEEP LEARNING ALGORITHM FOR PLANT DISEASE WITH IOT MONITORING

Abstract

The agricultural sector plays a key role in supplying quality food and makes the greatest contribution to growing economies and populations.Plant disease may cause significant losses in food production and eradicate diversity in species.Early diagnosis of plant diseases using accurate or automatic detection techniques can enhance the quality of food production and minimize economic losses.In recent years, deep learning has brought tremendous improvements in the recognition accuracy of image classification and object detection systems.Hence, in this paper, we utilized convolutional neural network (CNN)-based pre-trained models for efficient plant disease identification.Smart farming plays a vital role in the Indian economy.But nowadays, farming agriculture in Indian is undergoing a structural change leading to a crisis.The only remedy to the crisis is to do all that is possible to make agriculture a profitable enterprise and attract the farmers to continue the crop production activities so our proposed model focuses on cultivation's with the help of machine learning predicting the appropriate crop based on the climatic situations and determine the crop disease based on the historic data.

Abstract from the paper’s OpenAlex record; open copy licensed other-oa. Read at the source ↗

The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.