Enabling Technologies for Future Robotic Agriculture Systems: A Case Study in Indian Scenario

Mary, X. Anitha; Kannan, Mani; Raimond, Kumudha; Johnson, I.; Kumar, Praveen

doi:10.1002/9781119823469.ch4

Cited by 2 publications

References 30 publications

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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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AI-Driven Applications in High-Tech Agriculture

Suma

Aswini

Sunitha³

et al. 2023

Advances in Environmental Engineering and Green Technologies

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As the global population continues to grow, the demand for food is also increasing, and high-tech agriculture has emerged as a key solution to meet this demand. AI technologies have the potential to revolutionize high-tech agriculture by optimizing farming practices. This chapter outlines the mission and concerns related to the use of AI-driven applications for high-tech agriculture. The primary objective of this chapter is to outline the mission and address concerns pertaining to the utilization of AI-driven applications in high-tech agriculture. This chapter provides insights into the extensive range of AI-driven applications in high-tech agriculture, spanning crop monitoring, seed selection, planting, harvesting, post-harvest processes, and distribution logistics. By providing a deep understanding of the state-of-the-art AI technologies in high-tech agriculture, this chapter also offers valuable recommendations for future directions and collaboration to fully capitalize on the benefits of AI in the agricultural sector.

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Enabling Technologies for Future Robotic Agriculture Systems: A Case Study in Indian Scenario

Cited by 2 publications

References 30 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

AI-Driven Applications in High-Tech Agriculture

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