• Mrs. M. Angel shalini
• Mr. E. Karthikeyan

In India, most of the people are dependent on agriculture. The raw materials obtained from the agriculture are served as food for many people. The crop plantations are being destroyed because of the two main reasons: (i) The natural destructions such as drought, flood, famine, and earthquake. (ii) Pest and pathogens. About 98% of the destruction in crops are caused by pathogens and pests. The remaining 2% of the destruction is due to natural disaster in the surroundings. The rural farmers are severely affected by the crop production problems. In crop's life cycle, leaf plays a major role in getting the information about the growth and production of the plant. Previously we discussed the methods used for the detection of plant diseases using their leaves images. This paper also discussed some segmentation and feature extraction algorithm used in the plant disease detection. In this paper, the proposed system works on the preprocessing of the dataset. The leaf images are collected from the plant village dataset. The feature extraction is applied to the images during the data preprocessing stage. Convolution neural network (CNN) is used for the classification and detection of diseases. The recommendation of pesticides and fertilizers is done by using TensorFlow technique. The convolution neural network with various number of layers is used for training the model, and GUI screen serves as a user interface.

PLANT LEAF DISEASE DETECTION USING CNN WITH TENSORFLOW TECHNIQUE

"PLANT LEAF DISEASE DETECTION USING CNN WITH TENSORFLOW TECHNIQUE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.i433-i440, May-2022, Available :http://www.jetir.org/papers/JETIR2205965.pdf

"PLANT LEAF DISEASE DETECTION USING CNN WITH TENSORFLOW TECHNIQUE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppi433-i440, May-2022, Available at : http://www.jetir.org/papers/JETIR2205965.pdf