Open Access

ARTICLE

MDCN: Modified Dense Convolution Network Based Disease Classification in Mango Leaves

Chirag Chandrashekar¹, K. P. Vijayakumar^1,*, K. Pradeep¹, A. Balasundaram^1,2

1 School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, India
2 Center for Cyber Physical Systems, School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Chennai, India

* Corresponding Author: K. P. Vijayakumar. Email:

(This article belongs to the Special Issue: Machine Vision Detection and Intelligent Recognition)

Computers, Materials & Continua 2024, 78(2), 2511-2533. https://doi.org/10.32604/cmc.2024.047697

Received 14 November 2023; Accepted 14 December 2023; Issue published 27 February 2024

Abstract

The most widely farmed fruit in the world is mango. Both the production and quality of the mangoes are hampered by many diseases. These diseases need to be effectively controlled and mitigated. Therefore, a quick and accurate diagnosis of the disorders is essential. Deep convolutional neural networks, renowned for their independence in feature extraction, have established their value in numerous detection and classification tasks. However, it requires large training datasets and several parameters that need careful adjustment. The proposed Modified Dense Convolutional Network (MDCN) provides a successful classification scheme for plant diseases affecting mango leaves. This model employs the strength of pre-trained networks and modifies them for the particular context of mango leaf diseases by incorporating transfer learning techniques. The data loader also builds mini-batches for training the models to reduce training time. Finally, optimization approaches help increase the overall model’s efficiency and lower computing costs. MDCN employed on the MangoLeafBD Dataset consists of a total of 4,000 images. Following the experimental results, the proposed system is compared with existing techniques and it is clear that the proposed algorithm surpasses the existing algorithms by achieving high performance and overall throughput.

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Keywords

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