基于卷积神经网络的经济林产品检测与分选研究进展

经济林作为重要森林资源，其种植面积及产品产量逐年增加。随着科学技术的不断创新与升级，经济林产品加工产业快速发展、衍伸产品日趋增多，急需智能化检测、采收与分选技术与装备。深度融合人工智能技术与经济林产品加工产业，是实现高效化、精准化、智能化发展的重要手段之一。文中综合比较了深度学习技术中不同卷积神经网络算法及模型的优缺点，综述了其在经济林产品检测与分选中的研究进展，并针对研究应用过程中存在的问题提出了进一步深入研究建议，以期为经济林产品检测与分选的智能化发展提供参考。

Research on Detection and Sorting of Products from Non-Wood Forest Based on Convolution Neural Network

Non-wood forest is an important forest resource, and its planting area and yield have been increasing year by year. With the innovation and upgrading of science and technology, its product processing industry has been developing rapidly, with more augmented product. In this sense, the technologies and equipment for intelligent detection, picking and sorting are urgently needed. The integration of deep integration of artificial intelligence technology with non-wood forest products industry is one of the important means to achieve efficient, precise and intelligent development. This paper comprehensively compares the advantages and disadvantages of different convolution neural network algorithms and models based on deep learning technology, reviews the research progress in detection and sorting of the products from non-wood forest, and puts forward suggestions in view of the problems rising in the process of research and application, with an expectation to provide a reference for the intelligent development of product detection and sorting for non-wood forest.