• Aravind Mohan
• A. Sharmila Agnal
• P. Karthik
• Ajay Rana

Today, the farmers find it difficult to identify the crop diseases at the early stages due to lack of knowledge of the diseases that can attack the crop. Image processing is one of the technique that can be effective for analysis in various domains. The correct and timely identification of diseases in crops is the basis for integrated management of a farm.Machine learning can be used for detection of crop diseases which can help to detect and treat the crop diseases in its early stages.It is difficult to find out the diseases with naked eye at the same time it is ineffective as well.With the rapid development of artificial intelligence and image processing technology, it is possible to develop an automated system to make the conventional agriculture work a lot easier and at the same time accurate and effecient, such as pest detection and early warning. The symptoms of diseases found on crops differ in colour, shape, and size according to the cause. Image processing techniques are used in this to solve problems of features extraction and analysis which include features of colour, size, shape, and surface texture.This project allows us to detect defects in crops just by taking a picture of it, the picture through image analysis and machine learning is converted to a classified vector on whether the crop in the image is diseased or not and then one hot matrix is developed of 1s and 0s.1’s indicate that the crop is diseased and 0’s indicate that the crop is not diseased .The dataset of diseased crops is already available or can be created over time which can be used to compare and arrive whether the crop is diseased or not.

Machine learning,Agriculture,Technology,Automation

"Crop Defect Detection using machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.49-52, November-2018, Available :http://www.jetir.org/papers/JETIR1811097.pdf

"Crop Defect Detection using machine Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp49-52, November-2018, Available at : http://www.jetir.org/papers/JETIR1811097.pdf