To solve the inspection problems in cotton storage, as well as the need for environmental monitoring in the process of modern cotton bale storage, an intelligent inspection and temperature and humidity intelligent monitoring system based on RFID cotton bale was developed by adopting RFID (Radio Frequency Identification) technology, wireless temperature and humidity real-time monitoring technology and handheld terminal intelligent inspection technology. The system was composed of RFID positioning inspection module and temperature and humidity real-time monitoring and transmission module. The artificial neural network (ANN) based on the particle swarm optimization (PSO) algorithm was used to process the monitoring data of the system by Gaussian filtering, and an accurate classification model of RSSI and label position was established. The test results showed that: Through the comparative analysis of the RFID indoor positioning algorithm, the positioning error of the PSO-ANN algorithm was small. In the actual cotton bale warehouse test, the relative error of positioning and monitoring for RFID cotton bale intelligent inspection and monitoring system was less than 6.7%, which effectively improved the working efficiency of inspection personnel and the security of cotton bale storage. The relative error of temperature and humidity was less than 8% and less than 7%, which could display the temperature and humidity information in real time and meet the real-time demand. This study improved the management personnel's effective positioning and inspection of the cotton bale, prevented the loss of cotton bale, reduced the deterioration probability of cotton bale, and effectively improved the storage management level of the cotton bale. It was of great practical significance to realize the networking, automation, and intelligence of cotton bale storage management.

To realize the real-time monitoring of the cotton precision seeding operation process and improve the intelligence level of cotton precision planters, based on automatic color matching detection technology and visualization technology, this study designs a monitoring system for the sowing quality of cotton precision planters. The monitoring system is based on the double-silo turntable type cotton vertical disc hole seed metering device as the research carrier, and is composed of a missed seeding monitoring module and a visualization module. Among them, the missed seeding monitoring module includes an incremental rotary encoder, color code electric eye color fiber optic sensor, color code sensor amplifier, etc.; the visualization module includes data acquisition module, industrial computer, and so on. The missing seeding monitoring module is installed on the seed spacer of the cotton precision seed metering device. It uses Labview software for graphical programming and is equipped with a multi-functional industrial computer. It realizes the monitoring of parameters such as the number of sowings, the number of missed sowings, the speed of the hole seeder, the forward speed of the machine, and the sowing area. The results of the bench test and field test of the sowing monitoring system showed that the accuracy rate of the system’s broadcast monitoring was over 93%, and the accuracy rate of missed broadcast monitoring was over 91%. The system solved the technical problem that cotton film-laying and sowing were not easy to detect. It could accurately detect the quality of cotton sowing in real time and meet the actual requirements of sowing monitoring.

This work aims to construct an artificial neural network (ANN) ant colony algorithm (ACA)-based fine recognition system for plant factory seedling phenotypes. To address the problems of complexity and high delay of the plant recognition system in plant factories, first, multiple cameras at different positions are employed to collect images of seedlings and construct 3D images. Then, the mask region convolutional neural networks (MRCNN) algorithm is adopted to analyze plant phenotypes. Finally, the optimized ACA is employed to optimize the process timing in the plant factory, thereby constructing a plant factory seedling phenotype fine identification system via ANN combined with ACA. Moreover, the model performance is analyzed. The results show that plants have four stages of phenotypes, namely, the germination stage, seedling stage, rosette stage, and heading stage. The accuracy of the germination stage reaches 97.01%, and the required test time is 5.64 s. Additionally, the optimization accuracy of the process timing sequence of the proposed model algorithm is maintained at 90.26%, and the delay and energy consumption are stabilized at 20.17 ms and 17.71, respectively, when the data volume is 6000 Mb. However, the problem of image acquisition occlusion in the process of 3D image construction still needs further study. Therefore, the constructed ANN-ACA-based fine recognition system for plant seedling phenotypes can optimize the process timing in a more real-time and lower energy consumption way and provide a reference for the integrated progression of unmanned intelligent recognition systems and complete sets of equipment for plant plants in the later stage.

No abstract

Due to the characteristics of the cotton picker working in the field and the physical characteristics of cotton, it is easy to burn during the operation, and it is difficult to be detected, monitored, and alarmed. In this study, a fire monitoring system of cotton pickers based on GA optimized BP neural network model was designed. By integrating the monitoring data of SHT21 temperature and humidity sensors and CO concentration monitoring sensors, the fire situation was predicted, and an industrial control host computer system was developed to monitor the CO gas concentration in real time and display it on the vehicle terminal. The BP neural network was optimized by using the GA genetic algorithm as the learning algorithm, and the data collected by the gas sensor were processed by the optimized network, which effectively improved the data accuracy of CO concentration during fires. In this system, the CO concentration in the cotton box of the cotton picker was validated, and the measured value of sensor was compared with the actual value, which verified the effectiveness of the optimized BP neural network model with GA. The experimental verification showed that the system monitoring error rate was 3.44%, the accurate early warning rate was over 96.5%, and the false alarm rate and the missed alarm rate were less than 3%. In this study, the fire of cotton pickers can be monitored in real time and an early warning can be made in time, and a new method was provided for accurate monitoring of fire in the field operation of cotton pickers.