CRUN-Based Leaf Disease Segmentation and Morphological-Based Stage Identification

Author:

Sujithra J.1ORCID,Ukrit M. Ferni2ORCID

Affiliation:

1. Research Scholar School of Computing, SRM Institute of Science and Technology, Chennai-603203, Tamilnadu, India

2. School of Computing, SRM Institute of Science and Technology, Chennai 603203, Tamilnadu, India

Abstract

Natural growth is eliminated by the process of globalization in today’s globe owing to the development of technology and landscapes. The majority of today’s youngsters, as well as our seniors, lack an appropriate understanding of natural species such as plant names, tree names, and medicinal plants. This is attributed to technological advancements and a decline in gardening interest. To close this gap, horticulture may employ technology that aids in the improvement of plant understanding and growth. This method is implemented in the existing system for diagnosing leaf diseases using image processing and machine learning techniques. In the existing process, the classification of leaf disease is performed using image processing steps, such as preprocessing, segmentation, feature extraction, feature reduction, and classification. Even though it utilizes multiple processing steps and region-based classification, it identifies only the type of disease. In this paper, a combined approach of regional-based convolutional neural networks and U-Net (CRUN) is proposed for segmenting the leaf diseases from the augmented leaf dataset. Then, the segmented images are subjected to a morphological process to identify the level of disease in the leaf. This identification helps to identify the leaf’s nature and suggests a process to reduce the disease’s spread to other leaves through the proper use of fertilizers. The proposed method is applied to real-time images of sugarcane leaf diseases, such as bacterial blight and red rot, and banana leaf diseases, such as yellow and black sigatoka. This method is also applied to public sugarcane and banana leaf datasets from the Kaggle website. The proposed CRUN algorithm effectively segments the disease region. The morphological process helps to identify the disease level and protect the plant from further spread of disease. As a result, the proposed CRUN and morphological tests are most effective for automating leaf disease detection and prevention.

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

Cited by 12 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Sugarcane Disease Detection Using Data Augmentation;Advances in Web Technologies and Engineering;2024-05-16

2. Design and Development of a Plant Leaf Disease Identification System using Improved Deep Learning Strategy;2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM);2024-04-04

3. Automated Drone-Based Imaging Systems For Plant Health Monitoring Using Deep Learning Techniques;2023 Intelligent Computing and Control for Engineering and Business Systems (ICCEBS);2023-12-14

4. A nightshade crop leaf disease detection using enhance-nightshade-CNN for ground truth data;The Visual Computer;2023-10-27

5. An Enhanced Deep Learning Algorithms for Image Recognition and Plant Leaf Disease Detection;2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS);2023-08-23

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