| The yield monitoring acts as the beginning of the precise agriculture, and also the end. The accurate yield monitor information can examine the implementation effect of the precise agriculture in the current year. Even if there was not precise agriculture operation this year, the yields information could also reflect the differences of the soil fertility for each grid by looking into the different yields of it. Therefore, the yield information is an important tool for guiding the agriculture's working of the next year, and the yield monitoring plays an active role in researches and experiments for precision agriculture at present.The yield monitoring method is mainly a research for corn grain in the world-wide. There isn't any yield monitoring method for corn ear. Limited by factors like corn varieties, multiple cropping index and practices, farmers harvest corn ear instead of the corn grain. This leads to the fact that the yield monitoring method that widely used in the world couldn't apply in most parts of China. So the yield monitoring method for corn ear should be researched as soon as possible.The yield monitoring method for corn ear based on impact-based sensor is studied in this paper, which includes the application of electronic technology, information technology, agricultural mechanization engineering, agriculture and mathematics. The corn ear yield real-time monitoring system was developed in this way, and applied in the harvesting of corn.The paper did research on the yield real-time monitoring system for corn ear by the support of the National "863" High Technology Research and Development Program of Funded Projects (2006AA10A309) and the Project supported by Graduate Innovation Fund of Jilin University (20091017,20101018). The thesis's main research work and conclusions are as follows:1) The research on the yield real-time monitoring method for corn ear had been done. The impact-sensor was used as the yield sensor in this method. The 4YW-2 corn harvester was selected for the test in this paper. This paper analyzed the working principle of the method, and designed both direct impact and indirect impact programs. The paper designed the corn ear's guiding device for the program of indirect impact. The bench testing was done for the corn ear's guiding device in the laboratory. The best installation angle of the guiding device and the optimum space between the sensor and the guiding device was worked out by the bench tests, which were 45°and 300 mm. In the indirect program, the sensor should be installed in front of the elevator with the space of 684.4mm.2) The monitoring system was developed, according to the monitoring method for corn ear. The development of the yield monitoring system was based on S3C2410 microprocessor. The software of this system was developed on Win CE operating system and EVC platform.①The hardware development of the yield monitoring system was based on S3C2410 microprocessor. The yield monitoring system for corn ear was made up by seven modules, listed as the signal acquisition module of corn yield, the signal acquisition module of ground speed, the signal acquisition module of GPS, the signal acquisition module of the elevator's revolving speed, the trip protector, and data exchange interface.②The software of this system was developed on EVC platform and Win CE operating system. System software was divided into 5 subroutine as followed:the acquisition and processing program of the yield, the acquisition and processing program of the digital signal, the processing program of the position, the program of data real-time restoring, and the program of human-computer interaction.3) The influence factors of the yield in this yield monitoring system had been studied. The influence factors of the corn ear yield models were divided into three aspects:impulse of the corn ear, the elevator's revolution speed, and the ground speed. The bench tests of speed acquisition technology, the elevator's revolution acquisition technology and yield signal acquisition technology were all tested in the laboratory. The maximum errors of the speed acquisition in GPS and dead reckoning system were 3.26%and 2.33%. The proximity switches had the highest cost performance in speed acquisition test. In the test of elevator's revolving speed acquisition, the maximum error rate of speed sensor is 10.00% when the speed had reached 200rpm. The measured value of the revolving speed was steady when the speed between 500-600rpm. The measurement of accumulating impulse was less effected by the feed quantity of the corn ear. The accumulating impulse of corn ear was steady when the feed quantity of the corn ear was greater than 2.5. The influence of the posture could be ignored, when the corn ear impacted the sensor with lateral posture and longitudinal posture.4) The model was built by the method of data fitting and Back Propagation Neural Network (BPNN, for short). These models were applied in the field, and the conclusions were as followed:①The yield models of direct impact and indirect impact programs were established by the method of data-fitting, which was yi;=471.11 (Ii)/(ωi) and yi=0.7506Ii+20.074. The logsig and purelin function was chose for transfer function. The weight matrix was achieved by the training of the BPNN, and then the test of it was done. The yield of the corn ear was predicted by these models.②The average error of yield was used for the accuracy evaluation index of yield monitoring. In the program of direct impact sensor, the average errors of data-fitting model and BPNN model were 13.85% and 7.17%. In the program of indirect impact sensor, the average errors of data-fitting model and BPNN model were 13.64% and 9.28%. The error of total yield was lass than 5% in both models.③The research of yield level's division was done in the paper. The error rate of yield level's division was used for the accuracy evaluation index of yield monitoring. In the program of direct impact sensor, the average errors of data-fitting model and BPNN model were 17.14% and 10.00%. In the program of indirect impact sensor, the average errors of data-fitting model and BPNN model were 28.57% and 17.13%.④The tests of the yield monitoring system for corn ear were done in the farm of Jilin Agricultural University. The size of the areas for these tests was 1.1 hm2. By comparing the direct-impact and indirect-impact programs with two different yield models, the paper suggested choosing direct-impact program and BPNN model. The differences of the yields between grids were described objectively as same as the variation trend of the yield. It could provide experimental basis for variable fertilization precise agriculture.The innovations of this paper were as followed:according to the harvesting way of corn in China, this paper researched on the method and the yield's influence factors of yield monitoring for corn ear. Two different programs were designed, which were the indirectly and directly corn ear impacting sensors. In addition, the research of yield level's division was done in the paper, and the paper suggested using the precision of yield level's division as the accuracy evaluation index of monitoring.This method of yield monitoring system for corn ear fits well with China's harvesting of corn ear and provides a theory basis for the promotion of Precision agriculture.
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