Enhanced radial basis function neural network for tomato plant disease leaf image segmentation

Title

Enhanced radial basis function neural network for tomato plant disease leaf image segmentation

Creator

Patil M.A.; M M.

Description

Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work. 2022 Elsevier B.V.

Source

Ecological Informatics, Vol-70

Date

2022-01-01

Publisher

Elsevier B.V.

Subject

Enhanced radial basis function; Feature extraction; Modified sunflower optimization; Segmentation; Tomato leaf disease

Coverage

Patil M.A., Research Scholar, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India, Assistant Professor, Department of Information Technology, Vasavi College of Engineering, Ibrahimbagh, Telangana, Hyderabad, 500031, India; M M., Associate Professor, Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India

Rights

Restricted Access

Relation

ISSN: 15749541

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1016/j.ecoinf.2022.101752

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