• Shraddha
• Dr.K.R.Shailaja

Agriculture is one subject which has a excessive effect on existence and financial fame of human beings. Improper control ends in loss in agricultural products. Farmers lack the expertise of disorder and for this reason they produce much less production. Kisan name facilities are to be had however do now no longer provide provider 24*7 and on occasion conversation too fail. Farmers are not able to give an explanation for disorder well on name want to evaluation the photograph of affected vicinity of disorder. Identification of the plant sicknesses is the important thing to stopping the losses withinside the yield and amount of the rural product. The research of the maze leaf sicknesses suggest the research of visually observable styles visible at the maze leaf. Due to the development and improvement in era wherein gadgets are clever sufficient to understand and locate plant sicknesses. Recognizing infection can spark off quicker remedy which will reduce the terrible affects on harvest. This paper consequently cognizance upon maze leaf disorder detection the usage of photograph processing approach.This paintings makes use of an open dataset of 5000 snap shots of bad and solid plants, wherein convolution machine and semi supervised strategies are used to symbolize crop species and locate the illness fame of four wonderful classes.

MAIZE LEAF DISEASES DETECTION AND YIELD PREDICTION USING DEEP NEURAL NETWORKS

"MAIZE LEAF DISEASES DETECTION AND YIELD PREDICTION USING DEEP NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 10, page no.3619-3623, October-2020, Available :http://www.jetir.org/papers/JETIR2010471.pdf

"MAIZE LEAF DISEASES DETECTION AND YIELD PREDICTION USING DEEP NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 10, page no. pp3619-3623, October-2020, Available at : http://www.jetir.org/papers/JETIR2010471.pdf