A Quality Grading Method of Pinellia Pinelliae Based on Neural Network

Abstract

The invention discloses a neural network-based method for grading the quality of pinellia pinellia. The method includes the following steps: the terminal obtains the size, defect area, shape, color, surface texture, and color distribution of Pinellia through machine vision from the collected image of Pinellia; the size, defect area, shape, color, surface texture, The color distribution is further combined with the temperature and humidity of the operating area to form 8-dimensional feature data as the input of the BP neural network, and the manually marked pinellia quality level is used as the output; the BP neural network is trained and tested; the collected pinellia images are Input the neural network model processed in step 1 and trained in step 2 to obtain the quality grade of pinellia, combine the GPS sensor to collect the working position of the pinellia harvester and the batch number of the manual record, and use the QR Code coding technology to generate an exclusive Traceability information QR code. The invention can accurately determine the quality of Pinellia pinelliae, and realize traceability of Pinellia pinellia product information.

Description

technical field [0001] The invention belongs to the technical field of agriculture, in particular to a method for grading the quality of Pinellia ternata based on a neural network. Background technique [0002] In the context of the rapid development of information technology, "smart agriculture" is a new generation of new agricultural production methods that integrates the Internet, cloud computing, Internet of Things and artificial intelligence, enabling comprehensive and comprehensive applications of various information technologies in agriculture. . The traceability of agricultural product information is an important guarantee for its quality and safety. As an important Chinese herbal medicine with anti-cancer effect, Pinellia is particularly important for its production information traceability. At present, a large amount of data in the process of planting and production of Pinellia, such as origin, production environment, fertilization, etc., have not been effectively...

AI Technical Summary

Problems solved by technology

The information recorded in this method is limited, most of which are product processing information rather than production information, and some data are input manually, which inevitably has data reliability problems, so it is not suitable for agricultural products such as pinellia.

Images