• Shivani sudam gandhale
• Chhaya nayak
• Samiksha surywanshi
• Nutan deshmukh
• dipali pandit

 Abstract— Plant leaf disease detection plays an important role in the field of agriculture. Early detection of the disease can prevent the loss of formers and help in the increasing the productivity of the crop. Diseases can be detected by different image processing and machine learning algorithms and pattern recognition. It is not a simple task to manually observe and classify leaf diseases, since it requires a lot of time, resources, commitment, etc. So, with an automated image processing and machine learning system, it's easier to identify diseases. Plant leaf disease detection consist of five basic steps; image acquisition, preprocessing, segmentation, feature extraction and classification. the denoising step can be achieved by application of different filters. This paper presents an analysis of various methods for detecting image processing plant leaf diseases. Keywords—agriculture, deep learning, image processing, machine learning, plant leaf disease detection.

"Plant leaf disease detection using machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 4, page no.713-715, April-2021, Available :http://www.jetir.org/papers/JETIR2104192.pdf

"Plant leaf disease detection using machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 4, page no. pp713-715, April-2021, Available at : http://www.jetir.org/papers/JETIR2104192.pdf