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

Sustainability, Год журнала: 2025, Номер 17(7), С. 3190 - 3190

Опубликована: Апрель 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.

Язык: Английский

Efficient detection and counting method for maize seedling plots

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100914 - 100914

Опубликована: Апрель 1, 2025

Язык: Английский