Abstract
Citrus fruit detection can provide technical support for fine management and yield determination of citrus orchards. Accurate detection of citrus fruits in mountain orchards is challenging because of leaf occlusion and citrus fruit mutual occlusion of different fruits. This paper presents a citrus detection task that combines UAV data collection, AI embedded device, and target detection algorithm. The system used a small unmanned aerial vehicle equipped with a camera to take full-scale pictures of citrus trees; at the same time, we extended the state-of-the-art model target detection algorithm, added the attention mechanism and adaptive fusion feature method, improved the model’s performance; to facilitate the deployment of the model, we used the pruning method to reduce the amount of model calculation and parameters. The improved target detection algorithm is ported to the edge computing end to detect the data collected by the unmanned aerial vehicle. The experiment was performed on the self-made citrus dataset, the detection accuracy was 93.32%, and the processing speed at the edge computing device was 180 ms/frame. This method is suitable for citrus detection tasks in the mountainous orchard environment, and it can help fruit growers to estimate their yield.
Highlights
IntroductionWith the gradual increase of citrus yield every year, the planting range is gradually expanded, people pay more attention to this highly nutritious fruit
Citrus is the largest fruit in the world and one of the main cash crops
We presented a citrus fruit recognition method based on a deep learning target detection model, mobile data platform, and edge computing device
Summary
With the gradual increase of citrus yield every year, the planting range is gradually expanded, people pay more attention to this highly nutritious fruit. In this case, fruit farmers’ yield increase, and yield estimation play a role in economic income generation. Target detection algorithm can provide technical support for these citrus tasks. In recent years, it has been widely used in citrus operations [1], such as citrus picking [2], orchard yield measurement [3]. It is of great significance to study the acquisition and transmission of citrus images on the mobile operating platform/UAV and realize the accurate recognition of citrus targets for the fine management and yield estimation of orchards
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