Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique
Source abstract
Abstract
Mango fruit is in high demand. So, the timely control of mango plant diseases is necessary to gain high returns. Automated recognition of mango plant leaf diseases is still a challenge as manual disease detection is not a feasible choice in this computerized era due to its high cost and the non-availability of mango experts and the variations in the symptoms. Amongst all the challenges, the segmentation of diseased parts is a big issue, being the pre-requisite for correct recognition and identification. For this purpose, a novel segmentation approach is proposed in this study to segment the diseased part by considering the vein pattern of the leaf. This leaf vein-seg approach segments the vein pattern of the leaf. Afterward, features are extracted and fused using canonical correlation analysis (CCA)-based fusion. As a final identification step, a cubic support vector machine (SVM) is implemented to validate the results. The highest accuracy achieved by this proposed model is 95.5%, which proves that the proposed model is very helpful to mango plant growers for the timely recognition and identification of diseases.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Leaf Properties and Growth Measurement · Date Palm Research Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad 45550, Pakistan
Department of Computer Science & Engineering, Ewha Womans University, Seoul 120-750, Korea
Department of computer Science, Hanyang University, Seoul 04763, Korea
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