Automatic visual estimation of tomato cluster maturity in plant rows

The present paper aims to study image processing algorithms to accelerate and facilitate the evaluation of the harvest condition in tomato farms. In order to achieve this, two different deep learning models are trained and combined with counting methods to produce a harvest monitoring system for embedded applications using an Intel® MovidiusTM and an affordable RGB camera. The first model detects the location of cherry tomato clusters, while the second estimates the fruit’s maturity. The results are compared to a baseline implementation based on segmentation. Next, a multiple counting method based on regions of interest is applied to the detected clusters in videos to count the tomatoes at different maturity stages. In order to produce a more robust counting, a tracking system is implemented which uses temporal information to find the unique tomato clusters in videos. In the evaluation stage, the obtained location results indicate an intersection over union ( $$ IoU $$ ) of about $$89\%$$ when using the MobileNetV1 as a feature extractor and choosing the appropriate location anchors. The maturity estimation results indicate better performance for the trained algorithm as compared to the baseline, providing a root mean square error of $$7.7\%$$ . The best results were obtained when combining the fully learned solution with the tracking system, correctly counting the majority of the tomato clusters at multiple maturity stages.