Manual fruit harvesting is common in agriculture, but the amount of time that pickers spend on nonproductive activities can make it very inefficient. Accurately identifying picking vs. non-picking activity is crucial for estimating picker efficiency and optimizing labor management and the harvest process. In this study, a practical system was developed to calculate the efficiency of pickers in commercial strawberry harvesting. Instrumented picking carts were used to record in real-time the harvested fruit weight, geo-location, and cart movement. A fleet of these carts was 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 accuracy of up to 0.974. The classification results were then used to compute two worker efficiency metrics: the percentage of time spent actively picking, and the time required to fill a tray. Analysis of the season-long harvest data showed that the pickers spent an average of 73.56% of their total harvest time actively picking strawberries, with an average tray fill time of 6.22 minutes. The mean accuracies of these metrics were 96.29% and 95.42%, respectively. When integrated on a commercial scale, the proposed technology could aid growers in automated worker activity monitoring and harvest optimization, ultimately helping to reduce non-productive time and enhance overall harvest efficiency.

Last week, the Brookings Institution published an examination of the "extent, location, and concentration" of AI activity in 400 US metro areas, hailing it as the "next great'general purpose technology,'" with the power to spur economic growth. Key takeaways: Although it already feels like AI is everywhere, the tech is still in its early days--and in the US, AI development and commercialization is mega-concentrated in a handful of mostly coastal locales. But, but, but: Brookings also identified 13 other metro areas with "above-average involvement" in AI, including hubs you may have seen coming--New York, Boston, Seattle, Los Angeles, Washington, D.C., San Diego, Austin, Texas, and Raleigh, North Carolina--as well as smaller metro areas like Boulder, Colorado, Lincoln, Nebraska, Santa Cruz, California, Santa Maria-Santa Barbara, California, and Santa Fe, New Mexico. Zoom out: The above 15 metro areas account for two-thirds of AI activity nationwide--and for that matter, more than 50% of the areas Brookings looked at make up just 5% of AI activity, Wired reported.