It is the study of diseases in plants and their causal factors such as environmental conditions, pathogens, etc. that affect the overall plant growth.
Published in Chapter:
Deep Learning-Based Mobile Application for Plant Disease Diagnosis: A Proof of Concept With a Case Study on Tomato Plant
Shradha Verma (GGSIP University, India), Anuradha Chug (GGSIP University, India), Amit Prakash Singh (GGSIP University, India), Shubham Sharma (GGSIP University, India), and Puranjay Rajvanshi (VIT University, India)
Copyright: © 2019
|Pages: 30
DOI: 10.4018/978-1-5225-8027-0.ch010
Abstract
With the increasing computational power, areas such as machine learning, image processing, deep learning, etc. have been extensively applied in agriculture. This chapter investigates the applications of the said areas and various prediction models in plant pathology for accurate classification, identification, and quantification of plant diseases. The authors aim to automate the plant disease identification process. To accomplish this objective, CNN has been utilized for image classification. Research shows that deep learning architectures outperform other machine learning tools significantly. To this effect, the authors have implemented and trained five CNN models, namely Inception ResNet v2, VGG16, VGG19, ResNet50, and Xception, on PlantVillage dataset for tomato leaf images. The authors analyzed 18,160 tomato leaf images spread across 10 class labels. After comparing their performance measures, ResNet50 proved to be the most accurate prediction tool. It was employed to create a mobile application to classify and identify tomato plant diseases successfully.