Аннотация:The accurate estimation of the number of strawberries in a greenhouse can be used to determine the yield and adaptability of different varieties in a controlled environment. The detection results play an important role in the evaluation of the maturity of fruits for the purpose of quantitative classification. The existing manual examination method is error-prone and time-consuming, which makes mechanized harvesting difficult. In this work, we propose a robust architecture, named "improved Faster-RCNN", to detect strawberries in ground-level RGB images captured by a self-developed "Large Scene Camera System". The purpose of this research is to develop a fully automatic detection and grading system for living plants in field conditions which does not require any prior information about targets. The experimental results show that the proposed method obtained an average fruit extraction Accuracy of more than 86%, which is higher than that obtained using three other methods. This demonstrates that image processing combined with the introduced novel deep learning architecture is highly feasible for counting the number of, and identifying the quality of, strawberries from ground-level images. Additionally, this work shows that deep learning techniques can serve as invaluable tools in larger field investigation frameworks, specifically for applications involving plant phenotyping.
