Detection and identification of healthy and unhealthy sugarcane leaf using convolution neural network system

• Autor: Aakash Kumar, P ; Nandhini, D ; Amutha, S ; Syed Ibrahim, S P
• Materias: Engineering
• Es parte de: Sadhana (Bangalore), 2023-11, Vol.48 (4), Article 251
• Descripción: Agriculture is the backbone of the country’s economy. Sugarcane is a globally important crop and a major source of sugar, ethanol and jaggery. One of the problems faced by the sugar cane industry is the diseases that attack the crops. If these diseases are not identified early, they may result in exterminating the whole crops surrounding them. Manually checking each and every corner of a large farm is physically impossible. Machine learning is the contemporary solution to the problem, which can be resolved using the Convolution Neural Network (CNN) techniques. The drone images of all corners of the farm can be fed into the trained model for distinguishing the health status. For training the model the secondary data is taken from Kaggle, which includes both healthy and unhealthy sugarcane plant images, with various diseases in the unhealthy class. Machine learning models effectively identify early-stage crop diseases. This helps the farmer to treat that part of the affected farm quickly in order to avoid the spread of the disease to the remaining parts of the farm. This study deals with sugarcane disease prediction using the CNN model. Two different layered CNN models (VGG-16 and VGG-19) that were tested. The models were trained based on the images of 2165 containing both healthy and unhealthy leaves. The whole dataset is divided into three parts, validating data, testing data, and training data. The selected model – VGG-19 performed better and was found to be analysing the image up to an accuracy of 92% with 90% precision.
  
• Editor: New Delhi: Springer India
  
• Idioma: Inglés