Jul 2026· International Journal for Research in Applied Science and Engineering Technology· Vol 14, pp. 1665-1671· 0 citations
TL;DR
A method that which is detecting the disease of a tomato plant from their leaf images is proposed with the deep learning algorithms Convolutional Neural Network (CNN), and MobileNet which is a one of the transfer learning method of CNN.
Abstract
Agriculture plays a crucial role in the Indian economy. Early detection of plant diseases is very much essential to
prevent crop loss and further spread of diseases. Most plants such as apple, tomato, cherry, grapes show visible symptoms of the
disease on the leaf. These visible patterns can be identified to correctly predict the disease and take early actions to prevent it.
This can be overcome by the use of machine learning and deep learning algorithms. Hence, we are proposing a method that
which is detecting the disease of a tomato plant from their leaf images. Here the process is performed with the deep learning
algorithms Convolutional Neural Network (CNN), and MobileNet which is a one of the transfer learning method of CNN. Once
after training the dataset with the algorithms, the accuracy of algorithms is compared and the images are classified. And the
precautions are also provided for the classified plant.
The Plant Disease Detecting System leverages advances in artificial intelligence and deep learning to provide an automated, efficient, and reliable solution for identifying plant diseases at an early stage and contributes to increased crop productivity, reduced chemical usage, and sustainable farming practices.
The proposed automated leaf disease detection system using image processing and deep learning techniques can detect leaf diseases effectively and efficiently, making it a useful and cost-effective solution for supporting farmers and agricultural experts in early disease diagnosis.
Shilpa T. S., K. U, Anusha Jajur J· World Journal of Advanced En...· 0 citations
Performance of four transfer learning models based on pretrained CNN architecture, VGG16, VGG19, ResNet50 and MobileNetV2, are shown under identical preprocessing and training conditions to analyze the influence of CLAHE-based contrast enhancement on disease feature representation and classification performance.
V. Nemade, V. Fegade, Deepti Barhate et al.· Indian Journal of Agricultur...· 0 citations
Plant leaf diseases are known to affect agricultural productivity and food security on a global level. "Therefore, the detection and diagnosis of diseases are important aspects of maintaining the health of crops on a sustainable level. Traditionally, the detection of diseases in plants is performed manually by experts....
The state-of-the-art deep learning methods for detection and classification are applied on banana leaf dataset and healthy and two common diseases of banana leaves are classified in this work.
N. Vidhya, R. Priya· AI Computer Science and Robo...· 0 citations
This work proposes Convolutional Neural Network with random forest (RF) (CNN-RF) trained on a single plant which is tomato, which aims to solve the problem by using CNN and Random forest classifier to leverage machine learning algorithms trained on extensive datasets of tomato leaves to identify symptoms and patterns i...
Isaac Phiri, Regi Anbumozhi Y., Esther J.· International Journal of Dat...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
