JIITA, vol.8 no.4 p.991-1000 2024, DOI: 10.22664/ISITA.2024.8.4.991
T. Angamuthu, A.S. Arunachalam
Technology and Advanced Studies
Abstract: The primary source of both sugar and ethanol, sugarcane is an essential crop in the globe. One issue facing the sugar business is sugarcane illnesses, which cause growing crops infected with the disease to be eradicated. If these diseases are not treated and diagnosed early, small-scale farmers would suffer financial losses. The reason for undertaking this study was the rapidly expanding categories of diseases and farmers’insufficient knowledge of disease diagnosis and recognition. This issue can be resolved by using deep learning techniques to computer vision and machine learning. Using 13,842 sugarcane picture datasets with disease-infected and healthy leaves, this study trained and tested a deep learning model that achieved a 95% accuracy rate. The aim of the trained model was fulfilled by. so, finally I, take the research to CNN and other CNN model.
Keywords: sugarcane leaf disease recognition, deep learning, image classification, convolutional neural network
Fullpaper: