Identification of Mango Leaf Disease Using Deep Learning

Manoharan, Nimisha; Thomas, Vinod J; Dhas, D. Anto Sahaya

doi:10.1109/asiancon51346.2021.9544689

Cited by 18 publications

(1 citation statement)

References 17 publications

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“…The research was conducted in the Google Colab Environment. Agriculture, essential to countries with a 70% population dependent on it like some in Asia, goes beyond mere sustenance [32]. It is pivotal for feeding vast populations.…”

Section: Literature Surveymentioning

confidence: 99%

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

Chandrashekar,

Vijayakumar,

Pradeep

et al. 2024

CMC

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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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Section: Literature Surveymentioning

confidence: 99%

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

Chandrashekar,

Vijayakumar,

Pradeep

et al. 2024

CMC

View full text Add to dashboard Cite

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Image‐based detection and classification of plant diseases using deep learning: State‐of‐the‐art review

Bagga,

Goyal

2024

Urban Agric & Regnl Food Sys

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Plant diseases are assumed to be one of the primary causes regulating food manufacturing and reducing deficits in crop yield, and it is crucial that plant diseases have rapid spotting and identification. Since the demand for food is rising quickly due to population growth, the main issue for any country is plant disease mechanization in agricultural science. Recent developments in deep learning techniques have found use in plant disease recognition, offering a robust tool with exceedingly precise results. Our main goal is to employ deep learning mechanisms to better understand diseases and how to identify them quickly. In this regard, we analyzed 94 publications chosen from the last 7 years (2016–2023) that show how CNN's philosophy has evolved over this period with various approaches to treating plant diseases. Furthermore, a full description of numerous crops, diseases connected to them, various datasets relating to plant diseases, existing CNN models, and customized CNN architectures is provided. The results of this state‐of‐the‐art review can be implemented to comprehend the cutting‐edge trends in the application of deep learning (CNNs) to detect plant diseases as well as pinpoint any research gaps that require the scientific community's attention.

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Benchmarking ML and DL Models for Mango Leaf Disease Detection: A Comparative Analysis

Ghosh,

Rahat,

Lenka

et al. 2024

Communications in Computer and Information Science

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Identification of Mango Leaf Disease Using Deep Learning

Cited by 18 publications

References 17 publications

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

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

Image‐based detection and classification of plant diseases using deep learning: State‐of‐the‐art review

Benchmarking ML and DL Models for Mango Leaf Disease Detection: A Comparative Analysis

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