• Mr. T.V.VS.Durga Prasad
• Mr.G.Ravi Kumar (Ph.D)

Leaf diseases are generally caused by pests, insects, pathogens and the productivity to a large scale if not controlled within time. Agriculturists are facing loss due to various crop diseases. It becomes tedious to the cultivators to know whether the plant is healthy or diseased. Various researches have taken place under the field of Machine Learning for plant disease detection such as traditional machine learning approach being Random Forest, Artificial Neural Network, Support Vector Machine, Fuzzy Logic, K-Means Method. These techniques consume high time and gives less accuracy. This problem can be resolved by Machine Learning in the field of agriculture. The objective of the proposed system is the early detection of disease before it spreads to the other leaves of the plant. The solution is to check and detect the plant diseases using tensorflow, an Open source and reliable software for Machine Learning applications which provides high accuracy, which is its main advantage. This system works in two phases: the first phase deals with training the dataset using Convolutional Neural Network algorithm. This includes training both healthy as well as diseased leaves. The second phase deals with checking the leaves with the test dataset and thereby identifying the disease.

Pathogens, Random Forest, Artificial Neural Network, Support Vector Machine, Fuzzy Logic, K-Means Method

"Leaf Disease Detection and Classification Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.e290-e294, February-2024, Available :http://www.jetir.org/papers/JETIR2402440.pdf

"Leaf Disease Detection and Classification Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppe290-e294, February-2024, Available at : http://www.jetir.org/papers/JETIR2402440.pdf