Semi physical and machine learning approach for yield estimation of pearl millet crop using SAR and optical data products

Kshetrimayum, Arvindd; Goyal, Akash; H, Ramesh; Bhadra, B. K

doi:10.1080/14498596.2023.2259857

Journal of Spatial Science

2023

DOI: 10.1080/14498596.2023.2259857

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Semi physical and machine learning approach for yield estimation of pearl millet crop using SAR and optical data products

Arvindd Kshetrimayum,

Akash Goyal,

Ramesh H

et al.

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2024

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Deep-millet: a deep learning model for pearl millet disease identification to envisage precision agriculture

Johnson,

Mary,

Winifred Raj

et al. 2024

Environ. Res. Commun.

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Plants are integral to human sustenance, serving as fundamental sources of sustenance, materials, and energy, crucial for economic prosperity. However, their productivity and yield are increasingly threatened by pests and diseases, exacerbated by shifting climatic conditions. Pearl millet, a vital crop in Africa and Asia, is particularly susceptible to a range of diseases including downy mildew, rust, ergot, smut, and blast, posing significant risks to crop yield and quality. Timely and accurate disease identification is paramount for effective management strategies. Traditional methods of disease detection relying on visual identification are laborious, costly, and often require specialized expertise, presenting formidable challenges for farmers. In this study, we propose a novel mobile application integrating a robust Deep Learning (DL) model for the automated identification of pearl millet leaf diseases, employing advanced computer vision techniques. A Convolutional Neural Network (CNN) architecture, named Deep Millet, was trained on a comprehensive dataset comprising 3441 field images depicting pearl millet leaves in both healthy and diseased states. It consists of fewer but more effective layers, which are optimized to extract the most pertinent features from the RGB images Comparative analysis against pre-trained models, including AlexNet, ResNet50, InceptionV3, Xception, NasNet mobile, VGG16, and VGG19, was conducted to evaluate the performance of the proposed model. Results demonstrate that Deep Millet achieved superior accuracy, completing training in a mere 240 seconds and yielding an impressive accuracy rating of 98.86%, surpassing current state-of-the-art models.

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Deep-millet: a deep learning model for pearl millet disease identification to envisage precision agriculture

Johnson,

Mary,

Winifred Raj

et al. 2024

Environ. Res. Commun.

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Semi physical and machine learning approach for yield estimation of pearl millet crop using SAR and optical data products

Cited by 1 publication

References 20 publications

Deep-millet: a deep learning model for pearl millet disease identification to envisage precision agriculture

Deep-millet: a deep learning model for pearl millet disease identification to envisage precision agriculture

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