An automated system to detect crop diseases using deep learning
Purushottam Sharma, Manoj Kumar, Richa Sharma, Shashi Bhushan, Sunil Gupta
Abstract
Food is one of the necessities for a human being to survive. Moreover, since the population is increasing with each passing day, growing sufficient crops to feed such a vast population becomes evident. Also, the country's economy is based on agricultural production as well. However, there is a significant threat to agricultural crop production in today's times, and hence the analysis of crop diseases becomes essential. Thus, the automatic identification and analysis of plant diseases are highly desired in agricultural information. The main objective of the research is to develop an optimised approach for system automation to detect crop diseases. Here we proposed an approach for building an automated system that primarily detects diseases using leaf images and some other features like recommending the remedy for that disease. We created a model using a convolution neural network algorithm and used the transfer learning approach using Inception v3 and ResNet 50 model. Further, we used this model and collected some data for remedies for the diseased classes and added that feature to our system.