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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Pakle, Ganesh
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Transfer learning based leaf disease detection model using convolution neural network Raut, Rahul; Bidve, Vijaykumar; Sarasu, Pakiriswamy; Kakade, Kiran Shrimant; Shaikh, Ashfaq; Kediya, Shailesh; Borde, Santosh; Pakle, Ganesh
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1857-1865

Abstract

The plants are attacked from various micro-organisms, bacterial illnesses, and pests. The signs are normally identified via leaves, stem, or fruit inspection. Illnesses that generally appeared on vegetation are from leaves and causes big harm if not managed in the early ranges. To stop this huge harm and manipulate the unfold of disorder this work implements a software system. This research work customs deep neural network to gain knowledge of probable illnesses on leaves within the early phases so it can be stopped early. Deep neural network (DNN) used for image classification. This work mainly focuses a neural network model of leaves ailment detection. The commonly available plant leaves dataset is undertaken with a dataset having special training of disease detection. In this work VGG16, ResNet50, Inception V3 and Inception ResNetV2 architectural techniques are implemented to generate and compare the results. Results are compared on the factors like precision, accuracy, recall and F1-Score. The results lead to the conclusion, that the convolution neural network (CNN) is more impactful technique to perceive and predict plant diseases.
Co-Authors Bidve, Vijaykumar Borde, Santosh Kakade, Kiran Shrimant Kediya, Shailesh Raut, Rahul Sarasu, Pakiriswamy Shaikh, Ashfaq