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.

翻译：人工水果采摘在农业中十分普遍，但采摘者在非生产性活动上花费的时间可能导致效率低下。准确识别采摘与非采摘活动对于估计采摘者效率、优化劳动力管理和收获流程至关重要。本研究开发了一套实用系统，用于计算商业草莓采摘中采摘者的效率。我们开发了配备仪器的采摘车（iCarritos），以实时记录采摘的水果重量、地理位置及采摘车运动轨迹。这些iCarritos在美国加利福尼亚州圣玛丽亚市的商业草莓收获季期间投入使用。收集的数据随后用于训练一个基于CNN-LSTM的深度神经网络，将采摘者的活动分类为"采摘"和"非采摘"两类。实验评估表明，CNN-LSTM模型展现出良好的活动识别性能，F1分数达到0.97。识别结果随后被用于计算采摘者效率及装满一个托盘所需的时间。对整个收获季数据的分析显示，平均采摘者效率为75.07%，估计准确率为97.23%。此外，平均托盘填装时间为6.85分钟，估计准确率为96.78%。当该技术整合到商业采摘中时，可帮助种植者监测自动化工人活动，优化收获流程，减少非生产性时间，从而提升整体收获效率。