Paper Info
Title
Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network
Abstract
Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease's effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.
Year
DOI
Venue
2021
10.3390/s21237987
SENSORS
Keywords
DocType
Volume
image processing, convolution neural network, plant leaf disease, deep learning, artificial intelligence
Journal
21
Issue
ISSN
Citations 
23
1424-8220
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Naresh K Trivedi100.34
Vinay Gautam200.34
Abhineet Anand300.34
Hani Moaiteq Aljahdali411.16
Santos Gracia Villar500.34
Divya Anand622.20
Nitin Goyal700.34
Seifedine Kadry8149.36