Design and Test of Intelligent Inspection and Monitoring System for Cotton Bale Storage based on RFID

In order to solve the patrol inspection problems in cotton storage and the need for environmental monitoring in the modern cotton bale warehousing process, RFID positioning technology, wireless temperature and humidity real-time monitoring technology and handheld terminal intelligent inspection technology are used to design and develop RFID-based cotton bale intelligence Inspection and intelligent temperature and humidity monitoring system. The artificial neural network (ANN) method performs Gaussian filter processing on the system monitoring data to establish an accurate RSSI and label position classification model. The system finally realizes the functions of locating cotton bales in storage, collecting bale information, and real-time measurement and collection of temperature and humidity parameters in the cotton bale warehouse. The test results show that the relative error of RFID cotton bale intelligent inspection and monitoring system positioning and monitoring does not exceed 6.7%, effectively improving the work efficiency of inspectors and the safety of cotton bale storage. The relative error of temperature is less than 8%, and the relative error of humidity is less than 7%. It can display the storage environment temperature in real time and meet the real-time requirements. It improves the effective positioning and inspection of the cotton stack by management personnel, prevents the loss of cotton bale and reduces the probability of cotton stack deterioration.

Technique Based on Fuzzy Logic for Cotton Bale Lay-down Management

In this paper a new technique has been proposed for cotton bale management using fuzzy logic. The fuzzy c-means clustering algorithm has been applied for clustering cotton bales into 5 categories from 1200 randomly chosen bales of the J-34 variety. In order to cluster bales of different categories, eight fibre properties, viz., the strength, elongation, upper half mean length, length uniformity, short fibre content, micronaire, reflectance and yellowness of each bale have been considered. The fuzzy c-means clustering method is able to handle the haziness that may be present in the boundaries between adjacent classes of cotton bales as compared to the K-means clustering method. This method may be used as a convenient tool for the consistent picking of different bale mixes from any number of bales in a warehouse.

Identification of Moisture Content in Cotton Bale by Microwave Imaging

Understanding of cotton quality is important in order to properly identify the moisture content .Measurement of moisture is difficult particularly at harvest and through the gin, because of the influence these processes have different fibre quality. Dry cotton can be harvested cleanly and efficiently but may suffer undue damage in the gin. On the other hand harvesting and ginning wet cotton leads to significant issues in processing and quality. A number of methods are used to measure moisture in seed cotton, lint and fuzzy seed, each has its varying advantages. A moisture variation of the bales that is not monitored from the outside of the bale. This research examines a new microwave imaging technique to view the internal moisture variations of cotton bale. Tests on the developed imaging sensor showed the ability to resolve small structures of parameters, against a low standard background, that were less than 1 cm in width. The accuracy of the sensing structure was also shown to provide the ability to accurately determine parameter standards. A preliminary test of the imaging capabilities on a wet commercial bale showed the technique was able to accurately image and determines the location of the wet layer within the bale.