Tea leaf disease detection using multi-objective image segmentation
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Tea leaf disease detection using multi-objective image segmentation Somnath Mukhopadhyay1
· Munti Paul1 · Ramen Pal1 · Debashis De2
Received: 10 December 2019 / Revised: 28 July 2020 / Accepted: 6 August 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Tea leaves’ diseases caused by constant exposure to pathogens lead to significant crop yield loss globally. Diagnosing the tea leave disease at an early stage minimizes the tea yield loss. In this study, a novel approach is presented for automatically detecting tea leaves diseases based on image processing technology. The Non-dominated Sorting Genetic Algorithm (NSGA-II) based image clustering is proposed for detecting the disease area in tea leaves. After that, PCA and multi-class SVM is used for feature reduction and identifying the disease in the tea leaves, respectively. The result shows that the proposed algorithm can detect the type of disease persisting in tea leaves with an average accuracy of 83%. Five different tea leaf diseases are considered here, such as Red Rust, Red Spider, Thrips, Helopeltis, and Sunlight Scorching. Keywords Tea leaf disease detection · NSGA-II based image clustering · Multi-class SVM · PCA · Feature reduction
1 Introduction Identification of diseases in plants is essential to avert the losses in yield and quantity in agriculture. Health monitoring and disease identification of plants is necessary for a sustainable cultivation system. The study of plant diseases means the knowledge of visible patterns Somnath Mukhopadhyay
[email protected] Munti Paul [email protected] Ramen Pal [email protected] Debashis De [email protected] 1
Dept. of Computer Science, Engineering, Assam University Silchar, Silchar, India
2
Dept. of Computer Science, Engineering, West Bengal University of Technology, Kolkata, India
Multimedia Tools and Applications
seen on the crops and vegetation. In the early days, plant diseases were controlled and analyzed by experts in the fields. Introducing technological advancements into the agriculture system can help the farmers identify the conditions well in advance. As a result, the farmers will be able to rectify the yield loss. In India, tea production plays a vital role, and it is an integral part of the Indian economy also. However, several diseases affect the proper growth of tea leaves; as a result, tea production is highly compromised. The problem can be solved if appropriate treatments or trimming are applied at an early stage on the contaminated leaf to stop the further increase of that. It is essential to develop an automated and technological system for detecting diseases as a preventative measure to help the farmers detect the tea leaf disease at the initial stage. In most cases, disease symptoms are visible on the leaves, stems, and fruits. The first symptom that determines that the leaf is diseased is by color changes from a green hue to other colors. A healthy leaf has a distinct color, whereas an unhealthy leaf has drastically different color comp
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