Researchers at Osaka Metropolitan University have developed a method for creating realistic virtual tomato farms that automatically generate data for training agricultural AI systems. Their approach offers a way to overcome one of the most labor-intensive tasks in farming: harvesting the crops.
Currently, farmbots use object detection systems to locate tomatoes and artificial intelligence to decide whether they are ripe. However, the use of these systems in the field has been bottlenecked by difficulty training them.
AI systems require large amounts of labeled images, which must be gathered from real farms. The process is time-consuming as each tomato has to be manually labeled by drawing bounding boxes and assigned a ripeness category. This process is further complicated by natural variations in lighting conditions, plant shapes, and growing environments between each farm and season.
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