用传感器和深度学习识别果农采摘行为，提升果园管理效率。
Data-Driven Worker Activity Recognition and Efficiency Estimation in Manual Fruit Harvesting
- 通过智能采摘车采集重量、位置和运动数据，训练CNN-LSTM模型分类采摘与非采摘行为。
- 模型F1得分0.97，果农平均效率75.07%，托盘填满时间估算误差仅2.77%。
- 适合果园管理者用于监控人工效率，优化采摘流程减少无效时间。
人工采摘在农业中仍普遍，但采摘者花在非生产性活动上的时间严重影响效率。准确区分采摘与非采摘行为对评估采摘效率和优化人力管理至关重要。本研究开发了一套实用系统，用于计算商业草莓采摘中果农的效率。通过部署配备传感器的智能采摘车（iCarritos），实时记录果实重量、地理位置及车辆运动数据。该系统在加州圣玛丽亚市的草莓采收季进行了应用。收集的数据用于训练基于CNN-LSTM的深度神经网络，以将果农行为分类为“采摘”或“不采摘”。实验评估显示，该模型在活动识别上表现优异，F1分数达0.97。识别结果被用于计算采摘效率及填满一个托盘所需时间。季节性数据分析表明，果农平均效率为75.07%，估算精度达97.23%；平均托盘填满时间为6.85分钟，估算精度为96.78%。该技术集成至商业采摘流程后，可帮助种植者监测工人活动，减少非生产时间，显著提升整体采收效率。
原文摘要 · Abstract (English)
Manual fruit harvesting is common in agriculture, but the amount of time pickers spend on non-productive activities can make it very inefficient. Accurately identifying picking vs. non-picking activity is crucial for estimating picker efficiency and optimising labour management and harvest processes. In this study, a practical system was developed to calculate the efficiency of pickers in commercial strawberry harvesting. Instrumented picking carts (iCarritos) were developed to record the harvested fruit weight, geolocation, and iCarrito movement in real time. The iCarritos were deployed during the commercial strawberry harvest season in Santa Maria, CA. The collected data was then used to train a CNN-LSTM-based deep neural network to classify a picker's activity into "Pick" and "NoPick" classes. Experimental evaluations showed that the CNN-LSTM model showed promising activity recognition performance with an F1 score of 0.97. The recognition results were then used to compute picker efficiency and the time required to fill a tray. Analysis of the season-long harvest data showed that the average picker efficiency was 75.07% with an estimation accuracy of 97.23%. Furthermore, the average tray fill time was 6.85 minutes with an estimation accuracy of 96.78%. When integrated into commercial harvesting, the proposed technology can aid growers in monitoring automated worker activity and optimising harvests to reduce non-productive time and enhance overall harvest efficiency.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。