• Shreya Rodge
• Ayushi Patani
• Isha Pardikar
• Paluck Doshi
• Shyamal Khachane

Manually detecting disease of a plant can be a wearisome and difficult task. Furthermore, the level of accuracy is something that unintentionally may get compromised. To overcome this issue, an automated leaf disease detection system can be consolidated, via the means of image processing. In this paper, we propose a system using GoogLeNet that scans the leaf, identifies its type, and further recognizes the disease based on image recognition. GoogLeNet is advisable as it can run even on low computational devices. Leaf disease detection using GoogLeNet achieves a good level of accuracy. Furthermore, there can be some error factors which can be corrected using a discussion forum. It will also store information regarding the disease and the discussion taking place. In this forum, forward-looking farmers could take an initiative to spread agricultural information by being an active part of the community, and also help others to do so. Thus, the use of this leaf disease detection system will reduce both the time and effort required.

Leaf Disease detection, Image acquisition, Pre-processing, Features extraction, Classification, GoogLeNet, CNN, Discussion Forum.

"Plant Leaf Recognition And Disease Detection Using GoogLeNet", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.a595-a602, May-2021, Available :http://www.jetir.org/papers/JETIR2105071.pdf

"Plant Leaf Recognition And Disease Detection Using GoogLeNet", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppa595-a602, May-2021, Available at : http://www.jetir.org/papers/JETIR2105071.pdf