Agriculture constitutes a sector with a considerable environmental impact, a concern that is poised to increase with the projected growth in population, thereby amplifying implications for public health. Effectively mitigating and managing this impact demands the implementation of intelligent technologies and data-driven methodologies collectively called precision agriculture. While certain methodologies enjoy widespread acknowledgement, others, despite their lesser prominence, contribute meaningfully. This mini-review report discusses the prevalent AI technologies within precision agriculture over the preceding five years, with a specific emphasis on crop yield prediction and disease detection domains extensively studied within the current literature. The primary objective is to give a comprehensive overview of AI applications in agriculture, spanning machine learning, deep learning, and statistical methods. This approach aims to address a notable gap wherein existing reviews predominantly focus on singular aspects rather than presenting a unified and inclusive perspective

A mini-review on data science approaches in crop yield and disease detection / L. Valleggi, F.M. Stefanini. - In: FRONTIERS IN AGRONOMY. - ISSN 2673-3218. - 6:(2024 Apr), pp. 1352219.1-1352219.8. [10.3389/fagro.2024.1352219]

A mini-review on data science approaches in crop yield and disease detection

F.M. Stefanini
Ultimo
2024

Abstract

Agriculture constitutes a sector with a considerable environmental impact, a concern that is poised to increase with the projected growth in population, thereby amplifying implications for public health. Effectively mitigating and managing this impact demands the implementation of intelligent technologies and data-driven methodologies collectively called precision agriculture. While certain methodologies enjoy widespread acknowledgement, others, despite their lesser prominence, contribute meaningfully. This mini-review report discusses the prevalent AI technologies within precision agriculture over the preceding five years, with a specific emphasis on crop yield prediction and disease detection domains extensively studied within the current literature. The primary objective is to give a comprehensive overview of AI applications in agriculture, spanning machine learning, deep learning, and statistical methods. This approach aims to address a notable gap wherein existing reviews predominantly focus on singular aspects rather than presenting a unified and inclusive perspective
machine learning; precision agriculture; statistical learning; crop yield; disease detection; sustainability
Settore SECS-S/01 - Statistica
apr-2024
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1044248
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