ABSTRACT To address the poor fitness and low accuracy of multiobjective parameter optimization, the BP neural network-based constrained multiobjective optimization method was applied to optimize a seed-metering device. Taking the 2BQ-15 type Panax notoginseng seed-metering device as the research object, the picking hole column diameter, forward velocity, and dropping seed point-to-picking hole roll distance were selected as the experimental factors, and the quality index, missing index and multiple index were selected as the performance indicators. The experimental scheme was designed by the quadratic orthogonal rotation combination, and the BP neural network of the precision seed-metering device was built from the experimental data. The seed-metering device was optimized by the proposed method, and the optimal parameter combinations were obtained as follows: the picking hole column diameter was 27 mm, the forward velocity was 0.50 m/s, and the dropping seed point-to-picking hole roll distance was 330 mm. Under such parameter combinations, the quality index is 93.4%, the missing index is 3.15%, and the multiple index is 3.35%. Finally, a verification test was carried out on the basis of the optimization results, the errors were within the allowable range, and the test results and optimized results were consistent. networkbased based seedmetering metering 2BQ15 BQ 2BQ 15 2BQ-1 object pointtopicking point factors indicators combination data follows 2 mm 050 0 50 0.5 ms m s m/s 33 934 93 4 93.4% 315 3 3.15% 335 35 3.35% Finally range consistent 2BQ1 1 2BQ- 05 5 0. 9 93.4 31 3.15 3.35 93. 3.1 3.3 3.

Abstract In order to overcome the bad precision of fitted error, lower accuracy optimization results and other flaws, when extraction technology of the lotus leaf alkaloids was optimized by response surface method or regression analysis method, a linear constraint optimization method based on BP neural network is proposed. The testing program of three factors, three level was designed, which selected the hydrochloric acid mass fraction, ultrasound time, liquid-solid ratio as experimental factors. Taking the experiment data as training sample, the BP neural network model of the lotus leaf alkaloid yield and the influencing factors was obtained, and it was optimized by the proposed optimization method. The optimal parameter combination of extraction technology for lotus leaf alkaloid was obtained as follows: extraction temperature 60 °C, ultrasonic power 500W, hydrochloric acid mass fraction 0.3%, ultrasonic time 43 min, liquid-solid ratio 27, the yield of lotus leaf alkaloids under this process condition is 4.26 mg/g. It better than the best extraction technology obtained by response surface method. The obtained results is used for verification experiment, the verification results shown that the method has high fitting accuracy and stable optimization results, which optimize the extraction technology for lotus leaf alkaloid. error flaws designed liquidsolid liquid solid sample follows 6 C °C 500W W 03 0 3 0.3% 4 min 27 426 26 4.2 mgg mg g mg/g 0.3 2 42 4. 0.

ABSTRACT Research on the classification and identification of crop diseases and pests can help farmers quickly prevent crop diseases and pests. A crop disease and pest identification model based on adaptive chaotic particle swarm optimization algorithm is raised. The model introduces swarm intelligence algorithm to optimize the features of image extraction. Then the adaptive inertia weight is used to improve the optimization performance of PSO, and the support vector is used to accurately classify crop pests and diseases. Finally, the model is trained by simulation experiment to evaluate the performance of the model and analyze the performance. The model has a good performance in the experiment, the model has a clear recognition effect in the color feature extraction of pests and diseases, and the recognition accuracy is 95.08% after combining the texture feature. Moreover, in the visual transformation of 20¡ã-40¡ã, the recognition accuracy of the model is above 90%. In practical application, the average accuracy of the model is 91.78%, which is 3.71% higher than that of the comparison algorithm. In comparison experiments, the classification accuracy of the proposed models is above 90%. The experimental outcomes denote that the proposed algorithm has good effectiveness in identifying crop diseases and pests. raised PSO Finally 9508 95 08 95.08 Moreover 20¡ã40¡ã, 20ã40ã ãã 20¡ã 40¡ã, 20 ã 40 20¡ã-40¡ã 90 90% application 9178 91 78 91.78% 371 3 71 3.71 experiments 950 9 0 95.0 ã40 20¡ã40¡ã 20ã 40ã 40¡ã 2 4 917 7 91.78 37 3.7 95. ã4 91.7 3. 91.

ABSTRACT The parameter optimization of the tractor's steering trapezoid mechanism is a traditional optimization problem, and the teaching-learning-based optimization (TLBO) has a better solving ability for parameter optimization of the tractor's steering trapezoid. However, the teacher stage and student stage of TLBO limit the accuracy and stability and the ability to jump out of the local optimization solution. To obtain an optimal solution with an higher accuracy, an improved adaptive direction strategy teaching-learning-based optimization (IADS-TLBO) was used. This improved the feedback stage based on the adaptive direction strategy teaching-learning-based optimization (ADS-TLBO). The IADS-TLBO was verified by three different testing functions, and the results showed that the improved ideas are valid and feasible. Finally, the IADS-TLBO was used to optimize the steering trapezoid mechanism of JOHN DEERE T600. The optimal parameters obtained were as follows: the bottom angle was 35.4º, and the steering arm length was 154 mm. A verification experiment was conducted in the farm tool laboratory of Northeast Agricultural University (China). The experimental results showed that the average bottom angle was 35.48º, and the relative error between the measured and optimized bottom angles was 0.23%, which is less than 5%. This result showed that the results obtained by IADS-TLBO were reliable.

ABSTRACT To solve the poor fitting degree of errors in multiobjective parameter optimization and low accuracy, a multiobjective optimization method based on a BP neural network was proposed. By taking the 1ZT-210 type whole-straw returning device as the research object, a BP neural network model on power consumption, straw returning rate and the influencing factors was obtained. By optimizing the model by the proposed method, the optimal parameter combination of the test factors was as follows: the advancing speed of the device was 0.65 km/h, the blade roll rotating speed was 210 rpm, the blade installation angle was 55o, the minimum power consumption was 9.82 kW and the maximum straw returning rate was 93.23%. Under such test conditions, the minimum power consumption was 10.75 kW, and the straw returning rate was 92.46%, which were all better than those obtained by the regression analysis method. Finally, a verification test was conducted on the results of BP neural network optimization. The power consumption of the test was 10.04 kW, the absolute error was 0.22 kW and the relative error was 2.24%. For a straw returning rate of 93.11%, the absolute error was -0.12% and the relative error was 0.13%. The test results indicated that the optimization method was feasible.