In order to promote agricultural automatic picking and yield estimation technology, this project designs a set of automatic detection, positioning and counting algorithms for grape bunches, and applies it to agricultural robots. The Yolov3 detection network is used to realize the accurate detection of grape bunches, and the local tracking algorithm is added to eliminate relocation. Then it obtains the accurate 3D spatial position of the central points of grape bunches using the depth distance and the spatial restriction method. Finally, the counting of grape bunches is completed. It is verified using the agricultural robot in the simulated vineyard environment. The project code is released at: https://github.com/XuminGaoGithub/Grape_bunches_count_using_robots.

Mobile robots will play a crucial role in the transition towards sustainable agriculture. To autonomously and effectively monitor the state of plants, robots ought to be equipped with visual perception capabilities that are robust to the rapid changes that characterise agricultural settings. In this paper, we focus on the challenging task of segmenting grape bunches from images collected by mobile robots in vineyards. In this context, we present the first study that applies surgical fine-tuning to instance segmentation tasks. We show how selectively tuning only specific model layers can support the adaptation of pre-trained Deep Learning models to newly-collected grape images that introduce visual domain shifts, while also substantially reducing the number of tuned parameters.

By adopting precision agriculture it is possible to increase productivity while reducing the amount of treatment on crops, eventually increasing availability of safer food at lower costs. This revolution is based on a systematic use of technology, including the widespread adoption of sensors, both infield and in-lab for quality control processes. In addition to the expensive and highly accurate instruments used in lab, sensors on portable devices are constantly being developed in precision agriculture to support quality control, to dramatically reduce costs and obtain results which are comparable to the ones obtained in labs with traditional technologies. One important and appealing opportunity for farmers is to employ the smartphone they already have and use in their daily activities, with the addition of ad hoc technologies that can help boost their productivity.