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Rice production is highly seasonal, and the timing for conducting harvest operations has a relatively significant impact on yields, especially the appropriate timing of the harvesting operations. Using scientific methods to monitor the appropriate harvest period can effectively reduce harvesting losses. Most of the existing studies in this field focus on yield changes that occur during the harvest period of rice and also use multispectral remote sensing for inversion of the crop canopy. However, there has been no research on combining the yields and spectral characteristics of the harvest period. In this article, a monitoring method for the appropriate harvest period of rice based on multispectral remote sensing was established. Remote sensing data of the rice canopy were acquired using UAV equipped with multispectral (550 nm, 660 nm, 735 nm, and 790 nm) cameras, and physiological characteristic data of rice moisture content, thousand-grain weight, and tassel ratios were determined simultaneously. Single-band and combined-band spectral reflectance were introduced as model input variables to compare the BP neural network, SVM, and decision tree; a quantitative inversion model of the physiological characteristics of rice was established. The accuracy of the inversion model with different input variables and model methods was evaluated, and the optimal inversion model for rice moisture content and thousand-grain weight was selected, which could be used to determine whether the crop was harvested during the appropriate period. The continuous monitoring of two rice varieties (South Japonica 46 and South Japonica 5055) indicated that the moisture content of South Japonica 46 decreased linearly during the 20-day trial period, while its thousand-grain weight showed a trend of increasing first and then decreasing. The moisture content of South Japonica 5055 showed a fluctuating downward trend, and its thousand-grain weight also showed a trend of increasing first and then decreasing. The spectral reflectance at 735 nm increased gradually, and that of both rice varieties increased from about 35% at the beginning of the trial to about 75% in the later stage. The spectral reflectance at the other three bands did not show significant change with the harvest date. The inverse model of spectral reflectance with rice thousand-grain weight and moisture content was established using the BP neural network, SVM, and decision tree. In single-band inversion models, the regression effect at 735 nm was relatively good in the BP neural network model, with the highest determination coefficients of spectral reflectance and thousand-grain weight of both rich varieties and the smallest RMSE of prediction results. In combined-band inversion models, the regression effects at 550 + 660+735 nm and 660 + 735+790 nm were the best in the SVM and thousand-grain weight inversion models. [ABSTRACT FROM AUTHOR]

When the outbreak of COVID-19 began, people could not go out. It was not allowed to provide agricultural machinery services in different places across regions to reduce the flow and gathering of people. Improvement of utilization efficiency of agricultural machinery resources is required through scientific scheduling of agricultural machinery. With seizing the farming season and stabilizing production as the goal, this paper studied the scientific scheduling of tractors within the scope of town and established agricultural machinery operation scheduling model with the minimization of total scheduling cost as the optimization objective. Factors such as farmland area, agricultural machinery, and farmland location information and operating time window are considered in this model to improve the accuracy of the agricultural machinery operation scheduling model. The characteristics of multiple scheduling algorithms are analyzed comprehensively. The scheduling requirements of agricultural machinery operation to ensure spring ploughing are combined to design the agricultural machinery scheduling algorithm based on the SA algorithm. With Hushu Street, Jiangning District, Nanjing City, as an example, a comparative experiment is conducted on the simulated annealing algorithm (SA) designed in this paper and the empirical algorithm and genetic algorithm (GA). The results suggest that the total cost of the scheduling scheme generated by the SA algorithm is 19,042.07 yuan lower than that by the empirical scheduling algorithm and 779.19 yuan lower than that by the genetic algorithm on average. Compared with the GA algorithm, the transfer distance, waiting cost, and delay cost of the SA algorithm are reduced by 11.6%, 100%, and 98.1% on average, indicating that the transfer distance of agricultural machinery in the scheduling scheme generated by the SA algorithm is shorter, so is the waiting and delay time. Meanwhile, it can effectively obtain the near-optimal solution that meets the time window constraint, with good convergence, stability, and adaptability. [ABSTRACT FROM AUTHOR]