QR code is widely used in production management. For example, we scan the QR code printed on the fertilizer bag during the production of fertilizer to record production information. We propose a method to intelligently obtain the information carried by QR code through machine vision, combining deep learning and image processing technology. We obtain the original image through the industrial camera, locate the QR code area in the image based on the SSD target detection model, use the image processing method to rectify the QR code area, and finally decode it through the Zxing library. Through the real production environment test, the recognition rate of the method is 98.6%, and the average detection and recognition time of a frame image is 94 ms.
Fertilizer is the food of grain, which has a decisive impact on grain yield. The granulation particle size of fertilizers is one of the important factors affecting product quality. Today, fertilizer production plants still observe the granulation particle size manually. This method is timeconsuming, labor-intensive, and unstable. For this reason, this study uses computer vision technology and high-speed cameras to quickly capture fertilizer granulation particles, uses a control system to control industrial cameras to capture particle size, and then train and optimize the neural network model. Achieve the monitoring of fertilizer granulation production quality, adjust the production factors in real time, and improve the production quality. Finally, it can replace manual work and accomplish unmanned and intelligent granulation detection.
As an important agricultural product, the market price of chemical fertilizer is regulated by various factors such as raw material price, exchange rate policy, supply, etc., and the fluctuation of chemical fertilizer price has a significant impact on the national economy. It can be seen that if the price of chemical fertilizer can be accurately predicted, its impact on agriculture and even the national economy can be minimized. Therefore, in order to accurately predict the price of fertilizers, this paper proposes a bidirectional long-short term memory neural network model based on the attention mechanism(A-Bi-LSTM). The model makes full use of the contextual relationship between the forward and backward directions on the time series, which can effectively adopt the long distance information for the sequence data relying on. Combined with the trend data of fertilizer transaction prices in recent years, this paper performs regression fitting on the data to generate a training model which is used to predict the price of chemical fertilizer. Finally, the root mean square error on the test set is 0.011, and good prediction results are obtained.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.