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

Yi-Ming Qin, Yu-Hao Tu, Tao Li, Yao Ni (), Rui-Feng Wang and Haihua Wang ()
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Yi-Ming Qin: International College Beijing, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China
Yu-Hao Tu: College of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China
Tao Li: College of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China
Yao Ni: School of Integrated Circuits, Guangdong University of Technology, Guangzhou 510006, China
Rui-Feng Wang: College of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China
Haihua Wang: National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China

Sustainability, 2025, vol. 17, issue 7, 1-33

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

Keywords: lettuce; deep learning; sustainable agriculture; pest and disease control; weed management; monitoring; yield prediction; irrigation and fertilization management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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