Tomato crop disease classification using pre-trained deep learning algorithm
Year: 2018
Type: conference-paper
Abstract: The wide scale prevalence of diseases in tomato crop affects the production quality and quantity. In order to counteract the problem early diagnosis of diseases using a fast reliable nondestructive method will benefit the farmers. In this study images of tomato leaves (6 diseases and a healthy class) obtained from PlantVillage dataset is provided as input to two deep learning based architectures namely AlexNet and VGG16 net. The role of number of images and significance of hyperparameters namely minibatch size, weight and bias learning rate in the classification accuracy and execution time have been analyzed.
Source: Procedia Computer Science
Authors: Aravind Krishnaswamy Rangarajan, Raja Purushothaman, Aniirudh Ramesh
Institution: SASTRA University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W2884416373