Real-time detection and counting method for solanaceous vegetables and fruits in plant factory

Abstract

The invention discloses a real-time detection and counting method for solanaceous vegetables in a plant factory. The system comprises an image and video acquisition unit, a cloud computing platform unit, a computer terminal unit, a handheld intelligent terminal unit, a data acquisition module, a data annotation module, a data enhancement module, a data set conversion module, a data set preprocessing module, a detection model training module, a detection model evaluation module and a target detection and counting module. The method comprises the following steps of: labeling image data containing a target by using a modified Labelimg and an automatic labeling and artificial auxiliary supplementary correction method, and constructing a special data set; training the optimized deep learning model in combination with an improved real-time detection method to obtain a target detection model and a parameter file; and the computer and the intelligent terminal are imported for real-time detection and counting of vegetables and fruits in the plant factory, so that the detection precision and speed are improved, a large amount of manual labor is saved, and the production efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, relates to computer vision, image processing and target detection and classification methods, and in particular relates to a general real-time detection and counting method for solanaceous vegetables and fruits in plant factories. Background technique [0002] Artificial intelligence has played a huge role in all fields of human society, profoundly changing the way of life and production of human beings, and bringing us great convenience in all aspects. Many excellent deep learning models and algorithms emerge in an endless stream, and they have been widely used in different fields. Plant factory is a new agricultural form with the most modern agricultural characteristics, and the highest form of facility agriculture and smart agriculture. It is in the stage of stepping out of the laboratory and moving towards industrialization and commercialization, becoming the most promising an...

AI Technical Summary

Problems solved by technology

[0004] Image and video-based target detection is the most basic and challenging task in the field of computer vision. After the R-CNN target detection algorithm based on deep learning was proposed in 2014, Fast R-CNN and Faster R-CNN appeared. , SPPNet, YOLO series and many other excellent target monitoring algorithms have been greatly enhanced in terms of speed, accuracy, and robustness under complex conditions, but the resource consumption is large and the speed is slow, which cannot meet the real-time detection requirements

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