Deep Learning for Early Disease Identification in Leaves: A Pathway to Sustainable Mango Production

Abstract: Today, the challenges in agriculture are so big and complicated that agricultural professionals can't just look at them and figure them out. This study looks at the newest deep learning and computer vision techniques for finding and sorting mango leaf diseases. Leaf diseases cut the quality and quantity of mango crops by 30–80%, which costs the worldwide mango industry billions of dollars. We meticulously examine numerous scholarly articles on deep neural networks and image processing. We categorize current methodologies into three technical generations: conventional image processing systems, first deep learning applications, and advanced hybrid architectures that incorporate transfer learning, attention mechanisms, and explainable AI. We systematically assess performance metrics, dataset characteristics, and implementation challenges across diverse geographical regions. Our comprehensive evaluation framework and prospective research trajectories underscore sustainable, scalable, and farmer-centric solutions. This review is a complete guide for researchers studying tropical fruit and people who make agricultural technologies. This study fills in the gaps in research on dataset standardization, model interpretability, real-time deployment, and the integration of precision agriculture systems. Keywords: Smart Farming, Deep Learning, Mango Diseases, Computer Vision, Precision Agriculture, Transfer Learning. Title: Deep Learning for Early Disease Identification in Leaves: A Pathway to Sustainable Mango Production Author: Shivam Saini, Rohit Goyal, Rakesh Arya International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 14, Issue 4, October 2026 - December 2026 Page No: 8-13 Research Publish Journals Website: www.researchpublish.com Published Date: 05-October-2026 DOI: https://doi.org/10.5281/zenodo.23159808 Paper Download Link (Source) https://www.researchpublish.com/papers/deep-learning-for-early-disease-identification-in-leaves-a-pathway-to-sustainable-mango-production

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-05
DOI
https://doi.org/10.5281/zenodo.23159807
Primary Topic
Smart Agriculture and AI
Type
article
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article

Deep Learning for Early Disease Identification in Leaves: A Pathway to Sustainable Mango Production

Rakesh Arya, Shivam Saini, Rohit Goyal
Zenodo (CERN European Organization for Nuclear Research)
Smart Agriculture and AI
article

Deep Learning for Early Disease Identification in Leaves: A Pathway to Sustainable Mango Production

Rakesh Arya, Shivam Saini, Rohit Goyal
article en

Abstract

Abstract: Today, the challenges in agriculture are so big and complicated that agricultural professionals can't just look at them and figure them out. This study looks at the newest deep learning and computer vision techniques for finding and sorting mango leaf diseases. Leaf diseases cut the quality and quantity of mango crops by 30–80%, which costs the worldwide mango industry billions of dollars. We meticulously examine numerous scholarly articles on deep neural networks and image processing. We categorize current methodologies into three technical generations: conventional image processing systems, first deep learning applications, and advanced hybrid architectures that incorporate transfer learning, attention mechanisms, and explainable AI. We systematically assess performance metrics, dataset characteristics, and implementation challenges across diverse geographical regions. Our comprehensive evaluation framework and prospective research trajectories underscore sustainable, scalable, and farmer-centric solutions. This review is a complete guide for researchers studying tropical fruit and people who make agricultural technologies. This study fills in the gaps in research on dataset standardization, model interpretability, real-time deployment, and the integration of precision agriculture systems. Keywords: Smart Farming, Deep Learning, Mango Diseases, Computer Vision, Precision Agriculture, Transfer Learning. Title: Deep Learning for Early Disease Identification in Leaves: A Pathway to Sustainable Mango Production Author: Shivam Saini, Rohit Goyal, Rakesh Arya International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 14, Issue 4, October 2026 - December 2026 Page No: 8-13 Research Publish Journals Website: www.researchpublish.com Published Date: 05-October-2026 DOI: https://doi.org/10.5281/zenodo.23159808 Paper Download Link (Source) https://www.researchpublish.com/papers/deep-learning-for-early-disease-identification-in-leaves-a-pathway-to-sustainable-mango-production

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 14%
Smart Agriculture and AI
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Deep Learning for Early Disease Identification in Leaves: A Pathway to Sustainable Mango Production — Rakesh Arya, Shivam Saini, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS