EEE Department, Nanotechnology, IoT and Applied Machine Learning Research Group, Brac University, Dhaka, Bangladesh.
Department of Horticulture, Sher-e-Bangla Agricultural University (SAU), Dhaka, Bangladesh.
PLoS One. 2024 Jun 21;19(6):e0304657. doi: 10.1371/journal.pone.0304657. eCollection 2024.
To address the growing demand for sustainable agriculture practices, new technologies to boost crop productivity and soil health must be developed. In this research, we propose designing and building an agricultural rover capable of autonomous vegetable harvesting and soil analysis utilizing cutting-edge deep learning algorithms (YOLOv5). The precision and recall score of the model was 0.8518% and 0.7624% respectively. The rover uses robotics, computer vision, and soil sensing technology to perform accurate and efficient agricultural tasks. We go over the rover's hardware and software, as well as the soil analysis system and the tomato ripeness detection system using deep learning models. Field experiments indicate that this agricultural rover is effective and promising for improving crop management and soil monitoring in modern agriculture, hence achieving the UN's SDG 2 Zero Hunger goals.
为了满足可持续农业实践日益增长的需求，必须开发新技术来提高作物生产力和土壤健康。在这项研究中，我们提出设计和构建一个农业漫游者，能够利用先进的深度学习算法（YOLOv5）自主进行蔬菜收割和土壤分析。该模型的精确率和召回率分别为 0.8518%和 0.7624%。漫游者使用机器人技术、计算机视觉和土壤感应技术来执行精确和高效的农业任务。我们回顾了漫游者的硬件和软件，以及使用深度学习模型的土壤分析系统和番茄成熟度检测系统。田间试验表明，这种农业漫游者对于提高现代农业的作物管理和土壤监测非常有效和有前途，从而实现联合国的可持续发展目标 2 零饥饿目标。
