• Vikneshwaran.C.R
• Dr.N.S.Kavitha
• Naveen.K.B
• Rajasekar.S

Agriculture contributed to the domestication of today’s major food crops and animals thousands of years agone. Food insecurity, which may be a significant reason behind plant diseases, is one of the most important world issues that humanity faces these days. Per one study, plant diseases account for around 16 % of worldwide crop yield loss. Persecutor losses square measure projected to be 50 % for wheat and 26–29 % for soybeans globally. Fungi, flora-like species, bacteria, viruses, viroid, virus-like organisms, nematodes, protozoa, algae, and parasitic plants square measure the most categories of plant pathogens. Agriculture is the backbone of our nation. Plant diseases contribute to production loss, which may be tackled with continuous observation. Manual disease observation is each punishing and erring. Early detection of plant diseases machine learning will facilitate to scale back the adverse effects of diseases and additionally overcome the shortcomings of continuous human observation. We tend to propose the utilization of a deep learning design supported by a recent convolutional neural network during this work.

Machine learning, deep learning, Plant diseases, Convolutional neural network

"Automatic detection of Plant Disease Using Machine Learning (Plant Doctor)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.e831-e835, May-2022, Available :http://www.jetir.org/papers/JETIR2205596.pdf

"Automatic detection of Plant Disease Using Machine Learning (Plant Doctor)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppe831-e835, May-2022, Available at : http://www.jetir.org/papers/JETIR2205596.pdf