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 field of artificial intelligence technology, relates to computer vision, image processing and target detection and classification methods, and specifically relates to a general real-time detection and counting method for solanaceous vegetables and fruits in a plant factory. Background technique [0002] Artificial intelligence has played a huge role in various fields of human society, profoundly changing human life and production methods, and bringing us great convenience in all aspects. Many excellent deep learning models and algorithms are emerging one after another and have been widely used in different fields. Plant factory is a new form of agriculture with the most characteristics of agricultural modernization. It is the highest form of facility agriculture and smart agriculture. It is in the stage of moving beyond the laboratory to industrialization and commercialization, becoming the most promising and challenging ur...

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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