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.

The artificial intelligence lab had exhausted every reservoir of reputable English-language text on the internet as it developed its latest AI system. It needed more data to train the next version of its technology -- lots more. So OpenAI researchers created a speech recognition tool called Whisper. It could transcribe the audio from YouTube videos, yielding new conversational text that would make an AI system smarter. Some OpenAI employees discussed how such a move might go against YouTube's rules, three people with knowledge of the conversations said. YouTube, which is owned by Google, prohibits use of its videos for applications that are "independent" of the video platform.

Although the quest for Artificial Intelligence (AI), equipping trading algorithms with human qualities such as self-learning, continues to fascinate, it will be the explosion of the Internet of Things that will soon re-energize trading in capital markets. The Internet of Things (IoT) is rapidly growing through the addition of sensors to machines that allow them to "feel." Once they are equipped with feelings-- particularly sight, sound and touch-- machines can behave more intelligently, for example optimizing operations to use less fuel or predicting when they need maintenance. However, an interesting side effect is that the data from the IoT could be a new source of "insider" data for trading firms. For example, if combine harvesters (accessorized with sensors) signal a bumper wheat cropin the U.S. grain belt, traders can take advantage of this information before the crop report is issued.