A Survey on Sugarcane Leaf Disease Identification Using Deep Learning Technique(CNN)

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G. Mahesh Reddy
P. Hema Venkata Ramana
Ponnuru Anusha
Battula Kalyan Chakravarthy
Aravinda Kasukurthi
Vaddempudi Sujatha Lakshmi


The management of plant diseases is vital for the economical production of food and poses important challenges to the employment of soil, water, fuel and alternative inputs for agricultural functions. In each natural and cultivated populations, plants have inherent sickness tolerance, however there also are reports of devastating impacts of plant diseases. The management of diseases, however, within reason effective for many crops. sickness management is allotted through the employment of plants that square measure bred permanently resistance to several diseases and thru approaches to plant cultivation, like crop rotation, the employment of pathogen-free seeds, the given planting date and plant density, field wetness management, and therefore the use of pesticides. so as to enhance sickness management and to stay up with changes within the impact of diseases iatrogenic by the continued evolution and movement of plant pathogens and by changes in agricultural practices, continued progress within the science of soil science is required. Plant diseases cause tremendous economic losses for farmers globally. it's calculable that in additional developed settings across massive regions and lots of crop species, diseases usually cut back plant yields by ten percent per annum, however yield loss for diseases usually exceeds twenty percent in less developed settings. Around twenty-five percent of crop losses square measure caused by pests and diseases, the Food and Agriculture Organization estimates. to unravel this, new strategies for early detection of diseases and pests square measure required, like novel sensors that sight plant odours and spectrographic analysis and bio photonics that may diagnose plant health and metabolism. In artificial neural networks, deep learning is an element of a broader family of machine learning approaches supported realistic learning. Learning is often controlled, semi-supervised or unmonitored. to handle several real-world queries, Deep Learning Approaches are normally used. so as to differentiate pictures and acknowledge their options, coevolutionary neural networks have had a larger result. This article will do a Leaf Disease Identification Survey with Deep Learning Methods. It takes Sugarcane leaf as an instance to our paper.

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Reddy, G. M. ., Ramana, P. H. V. ., Anusha, P. ., Chakravarthy, B. K. ., Kasukurthi, A. ., & Lakshmi, V. S. . (2023). A Survey on Sugarcane Leaf Disease Identification Using Deep Learning Technique(CNN). International Journal on Recent and Innovation Trends in Computing and Communication, 11(5), 248–254. https://doi.org/10.17762/ijritcc.v11i5.6611


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