Deep Learning for Sustainable Agriculture: A Systematic Review on Applications in Lettuce Cultivation

Sustainability, Journal Year: 2025, Volume and Issue: 17(7), P. 3190 - 3190

Published: April 3, 2025

Lettuce, a vital economic crop, benefits significantly from intelligent advancements in its production, which are crucial for sustainable agriculture. Deep learning, core technology smart agriculture, has revolutionized the lettuce industry through powerful computer vision techniques like convolutional neural networks (CNNs) and YOLO-based models. This review systematically examines deep learning applications including pest disease diagnosis, precision spraying, pesticide residue detection, crop condition monitoring, growth stage classification, yield prediction, weed management, irrigation fertilization management. Notwithstanding significant contributions, several critical challenges persist, constrained model generalizability dynamic settings, exorbitant computational requirements, paucity of meticulously annotated datasets. Addressing these is essential improving efficiency, adaptability, sustainability learning-driven solutions production. By enhancing resource reducing chemical inputs, optimizing cultivation practices, contributes to broader goal explores research progress, optimization strategies, future directions strengthen learning’s role fostering farming.

Language: Английский

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Farm monitoring and disease prediction by classification based on deep learning architectures in sustainable agriculture

Ecological Modelling, Journal Year: 2022, Volume and Issue: 474, P. 110167 - 110167

Published: Oct. 18, 2022

Language: Английский

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Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

Published: Feb. 28, 2022

Agricultural Big Data is a set of technologies that allows responding to the challenges new data era. In conjunction with machine learning, farmers can use address different problems such as farmers' decision-making, crops, weeds, animal research, land, food availability and security, weather, climate change. The purpose this paper synthesize evidence regarding involved in implementing learning Data. We conducted Systematic Literature Review applying PRISMA protocol. This review includes 30 papers, published from 2015 2020. develop framework summarizes main encountered, techniques, well used. A major challenge design architectures, due need modify adapting volume increases.

Language: Английский

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Unlocking the potential of precision agriculture for sustainable farming

Discover Agriculture, Journal Year: 2024, Volume and Issue: 2(1)

Published: Nov. 7, 2024

Abstract Precision agriculture, a transformative farming approach, has gained prominence due to advancements in digital technologies. This paper explores the multifaceted landscape of precision focusing on its tangible benefits, challenges, and future directions. Purpose Amidst growing interest this aims provide comprehensive analysis various aspects. Specifically, it seeks elucidate benefits agriculture optimizing resource utilization, enhancing crop health, promoting sustainability. Moreover, examines challenges faced implementation proposes directions overcome these obstacles. Findings Through review existing literature case studies, presents nuanced understanding agriculture's impact farming, livestock production, economic outcomes, environmental It identifies key such as data security, costs, regulatory frameworks, while also highlighting innovative solutions promising field. Originality To best our knowledge, represents rigorous attempt comprehensively analyze with focus original contributions By synthesizing research offering insights into directions, adds emerging knowledge base surrounding potential revolutionize modern practices.

Language: Английский

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Comparison of machine learning methods emulating process driven crop models

Environmental Modelling & Software, Journal Year: 2023, Volume and Issue: 162, P. 105634 - 105634

Published: Jan. 26, 2023

Language: Английский

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