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http://dx.doi.org/10.5909/JBE.2022.27.6.923

Detection and Classification of Leaf Diseases for Phenomics System  

Gwan Ik, Park (Hanyang University, Department of Computer Science)
Kyu Dong, Sim (Hanyang University, Department of Computer Science)
Min Su, Kyeon (Hanyang University, Department of Computer Science)
Sang Hwa, Lee (Seoul National University, Inst. of New Media & Communications)
Jeong Hyun, Baek (KMCROBOTICS INC)
Jong-Il, Park (Hanyang University, Department of Computer Science)
Publication Information
Journal of Broadcast Engineering / v.27, no.6, 2022 , pp. 923-935 More about this Journal
Abstract
This paper deals with detection and classification of leaf diseases for phenomics systems. As the smart farm systems of plants are increased, It is important to determine quickly the abnormal growth of plants without supervisors. This paper considers the color distribution and shape information of leaf diseases, and designs two deep leaning networks in training the leaf diseases. In the first step, color distribution of input image is analyzed for possible diseases. In the second step, the image is first partitioned into small segments using mean shift clustering, and the color information of each segment is inspected by the proposed Color Network. When a segment is determined as disease, the shape parameters of the segment are extracted and inspected by proposed Shape Network to classify the leaf disease types in the third step. According to the experiments with two types of diseases (frogeye/rust and tipburn) for apple leaves and iceberg, the leaf diseases are detected with 92.3% recall for a segment and with 99.3% recall for an input image where there are usually more than two disease segments. The proposed method is useful for detecting leaf diseases quickly in the smart farm environment, and is extendible to various types of new plants and leaf diseases without additional learning.
Keywords
Phenomics system; Leaf disease; Deep learning network; Mean shift color clustering; Shape parameters;
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