<ul><li>A practical system was developed to calculate the efficiency of pickers in commercial strawberry harvesting.</li><li>Instrumented picking carts were used to record real-time data of harvested fruit weight, geo-location, and cart movement.</li><li>A CNN-LSTM-based deep neural network was trained to classify a picker's activity into 'Pick' and 'NoPick' classes.</li><li>The technology could aid growers in automated worker activity monitoring and harvest optimization, ultimately enhancing overall harvest efficiency.</li></ul>

Data-driven worker activity recognition and picking efficiency estimation in manual strawberry harvesting

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