IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i7p3190-d1627600.html
   My bibliography  Save this article

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

Author

Listed:
  • Yi-Ming Qin

    (International College Beijing, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China
    These authors contributed equally to this work.)

  • Yu-Hao Tu

    (College of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China
    These authors contributed equally to this work.)

  • 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
    National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China)

  • Haihua Wang

    (National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China
    College of Information and Electrical Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China)

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.

Suggested Citation

  • Yi-Ming Qin & Yu-Hao Tu & Tao Li & Yao Ni & Rui-Feng Wang & Haihua Wang, 2025. "Deep Learning for Sustainable Agriculture: A Systematic Review on Applications in Lettuce Cultivation," Sustainability, MDPI, vol. 17(7), pages 1-33, April.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3190-:d:1627600
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/7/3190/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/7/3190/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jinzhu Lu & Kaiqian Peng & Qi Wang & Cong Sun, 2023. "Lettuce Plant Trace-Element-Deficiency Symptom Identification via Machine Vision Methods," Agriculture, MDPI, vol. 13(8), pages 1-27, August.
    2. Rui-Feng Wang & Wen-Hao Su, 2024. "The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review," Agriculture, MDPI, vol. 14(8), pages 1-30, July.
    3. Songtao Ban & Minglu Tian & Dong Hu & Mengyuan Xu & Tao Yuan & Xiuguo Zheng & Linyi Li & Shiwei Wei, 2025. "Evaluation and Early Detection of Downy Mildew of Lettuce Using Hyperspectral Imagery," Agriculture, MDPI, vol. 15(5), pages 1-24, February.
    4. Haihua Wang & Xinxin Zhang & Shuli Mei, 2020. "Shannon-Cosine Wavelet Precise Integration Method for Locust Slice Image Mixed Denoising," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-17, July.
    5. Guilherme Lages Barbosa & Francisca Daiane Almeida Gadelha & Natalya Kublik & Alan Proctor & Lucas Reichelm & Emily Weissinger & Gregory M. Wohlleb & Rolf U. Halden, 2015. "Comparison of Land, Water, and Energy Requirements of Lettuce Grown Using Hydroponic vs. Conventional Agricultural Methods," IJERPH, MDPI, vol. 12(6), pages 1-13, June.
    6. Haihua Wang & Shu-Li Mei, 2014. "Shannon Wavelet Precision Integration Method for Pathologic Onion Image Segmentation Based on Homotopy Perturbation Technology," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, March.
    7. Yao Huo & Yongbo Liu & Peng He & Liang Hu & Wenbo Gao & Le Gu, 2025. "Identifying Tomato Growth Stages in Protected Agriculture with StyleGAN3–Synthetic Images and Vision Transformer," Agriculture, MDPI, vol. 15(2), pages 1-15, January.
    8. Sarah Velten & Julia Leventon & Nicolas Jager & Jens Newig, 2015. "What Is Sustainable Agriculture? A Systematic Review," Sustainability, MDPI, vol. 7(6), pages 1-33, June.
    9. Marwan Albahar, 2023. "A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities," Agriculture, MDPI, vol. 13(3), pages 1-22, February.
    10. Mostofa Ahsan & Sulaymon Eshkabilov & Bilal Cemek & Erdem Küçüktopcu & Chiwon W. Lee & Halis Simsek, 2021. "Deep Learning Models to Determine Nutrient Concentration in Hydroponically Grown Lettuce Cultivars ( Lactuca sativa L.)," Sustainability, MDPI, vol. 14(1), pages 1-16, December.
    11. José Escorcia-Gutierrez & Margarita Gamarra & Roosvel Soto-Diaz & Meglys Pérez & Natasha Madera & Romany F. Mansour, 2022. "Intelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques," Agriculture, MDPI, vol. 12(7), pages 1-16, July.
    12. Haiqing Wang & Shuqi Shang & Dongwei Wang & Xiaoning He & Kai Feng & Hao Zhu, 2022. "Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model," Agriculture, MDPI, vol. 12(7), pages 1-23, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Georgieva, Vanya & Guerov, Gueorgui & Blagoeva, Nadezhda, 2024. "Impact of economic and environmental factors on agricultural product pricing in the EU," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 10(4), December.
    2. Agnieszka Wojewódzka-Wiewiórska & Anna Kłoczko-Gajewska & Piotr Sulewski, 2019. "Between the Social and Economic Dimensions of Sustainability in Rural Areas—In Search of Farmers’ Quality of Life," Sustainability, MDPI, vol. 12(1), pages 1-26, December.
    3. Phélinas, Pascale & Choumert, Johanna, 2017. "Is GM Soybean Cultivation in Argentina Sustainable?," World Development, Elsevier, vol. 99(C), pages 452-462.
    4. Muhammed Yasin Taskesenlioglu & Sezai Ercisli & Muhammed Kupe & Nazan Ercisli, 2022. "History of Grape in Anatolia and Historical Sustainable Grape Production in Erzincan Agroecological Conditions in Turkey," Sustainability, MDPI, vol. 14(3), pages 1-16, January.
    5. Matteo Zavalloni & Meri Raggi & Davide Viaggi, 2016. "Assessing Collective Measures in Rural Policy: The Effect of Minimum Participation Rules on the Distribution of Benefits from Irrigation Infrastructure," Sustainability, MDPI, vol. 9(1), pages 1-19, December.
    6. Cecilia M. V. B. Almeida & Biagio F. Giannetti & Feni Agostinho & Gengyuan Liu & Zhifeng Yang, 2021. "What Are the Stimuli to Change to a Sustainable Post-COVID-19 Society?," Sustainability, MDPI, vol. 13(23), pages 1-13, November.
    7. Emilia Schmitt & Daniel Keech & Damian Maye & Dominique Barjolle & James Kirwan, 2016. "Comparing the Sustainability of Local and Global Food Chains: A Case Study of Cheese Products in Switzerland and the UK," Sustainability, MDPI, vol. 8(5), pages 1-20, April.
    8. Pereira, J. & Gomes, M. Glória, 2025. "Lighting strategies in vertical urban farming for enhancement of plant productivity and energy consumption," Applied Energy, Elsevier, vol. 377(PD).
    9. Monika Hejna & Elisabetta Onelli & Alessandra Moscatelli & Maurizio Bellotto & Cinzia Cristiani & Nadia Stroppa & Luciana Rossi, 2021. "Heavy-Metal Phytoremediation from Livestock Wastewater and Exploitation of Exhausted Biomass," IJERPH, MDPI, vol. 18(5), pages 1-16, February.
    10. Onwuadiochi, I. C & Onyeanusi, C. C & Mage, J. O., 2021. "Assessment Of Rainfall Variability For Sustainable Agriculture In Owerri, Imo State, Nigeria," Journal Clean WAS (JCleanWAS), Zibeline International Publishing, vol. 5(2), pages 39-46, July.
    11. Jacqueline Loos & Henrik Von Wehrden, 2018. "Beyond Biodiversity Conservation: Land Sharing Constitutes Sustainable Agriculture in European Cultural Landscapes," Sustainability, MDPI, vol. 10(5), pages 1-11, May.
    12. Thanh Ngo & Hai‐Dang Nguyen & Huong Ho & Vo‐Kien Nguyen & Thuy T. T. Dao & Hai T. H. Nguyen, 2021. "Assessing the important factors of sustainable agriculture development: An Indicateurs de Durabilité des Exploitations Agricoles‐Analytic Hierarchy Process study in the northern region of Vietnam," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(2), pages 327-338, March.
    13. Zejin Sun & Hui Yang & Zhifu Zhang & Junxiao Liu & Xirui Zhang, 2022. "An Improved YOLOv5-Based Tapping Trajectory Detection Method for Natural Rubber Trees," Agriculture, MDPI, vol. 12(9), pages 1-19, August.
    14. Feng, Yongbing & Gao, Guohua & Wang, Pengyu & Zhang, Zihua, 2024. "Integrating stakeholder value network with strategic issue management for multi-stakeholder needs and requirements analysis of vertical farming systems," Agricultural Systems, Elsevier, vol. 221(C).
    15. Stan Selbonne & Loïc Guindé & François Causeret & Pierre Chopin & Jorge Sierra & Régis Tournebize & Jean-Marc Blazy, 2023. "How to Measure the Performance of Farms with Regard to Climate-Smart Agriculture Goals? A Set of Indicators and Its Application in Guadeloupe," Agriculture, MDPI, vol. 13(2), pages 1-21, January.
    16. Hambur Wang, 2024. "Can education correct appearance discrimination in the labor market?," Papers 2411.01621, arXiv.org.
    17. Yi-Xuan Lu & Si-Ting Wang & Guan-Xin Yao & Jing Xu, 2023. "Green Total Factor Efficiency in Vegetable Production: A Comprehensive Ecological Analysis of China’s Practices," Agriculture, MDPI, vol. 13(10), pages 1-25, October.
    18. Ehsan Daneshyar, 2024. "Residential Rooftop Urban Agriculture: Architectural Design Recommendations," Sustainability, MDPI, vol. 16(5), pages 1-34, February.
    19. Lin Wang & Guofang Hu & Yaojie Yue & Xinyue Ye & Min Li & Jintao Zhao & Jinhong Wan, 2016. "GIS-Based Risk Assessment of Hail Disasters Affecting Cotton and Its Spatiotemporal Evolution in China," Sustainability, MDPI, vol. 8(3), pages 1-20, February.
    20. Mutaz Alshafeey & Asefeh Asemi & Omar Rashdan, 2018. "Industrial revolution 4.0, renewable energy: A content analysis," Proceedings of FIKUSZ 2018, in: Proceedings of FIKUSZ '18, pages 23-31, Óbuda University, Keleti Faculty of Business and Management.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3190-:d:1627600. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.