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ABSTRACT China's socialized service industry of agricultural machinery emerged as the times required, which resolved the institutional contradiction between small farm households and large-scale agriculture. However, as cross-operation harvesters are still monitored based on phone calls, the monitoring efficiency and real-time performance are relatively poor. To improve the dynamic monitoring accuracy of harvester working areas, we proposed a working progress monitoring method based on harvester traveling trajectory and header status identification. The position and gap bridge angle information of harvesters was synchronously acquired in real-time. The height of the header above ground was calculated to determine whether the harvester was harvesting on a valid trajectory. Finally, the valid trajectory of the harvester was selected for area calculation. Field experiments showed that the header heights at the working and non-working tracing points collected by the system were discretely distributed at intervals of [0m, 0.5m] and (0.5m, 2m), respectively. The working progress monitoring algorithm proposed in this paper effectively improved the monitoring accuracy of harvester working areas. The mean error between the working areas calculated with identifying the header status and the actual cultivated land area in the five test plots was 0.09 hm2, with a mean error rate of 3.10%, 8.59% lower than that without identifying the harvester header status.

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.

In mechanical rice transplanting, hill-row spacing combination directly influenced the growth and yield of rice. To compare the effect of hill-row spacing on mechanical transplanting rice, the growth and yield of rice, we set up 2-year field experiments located in Zhangjiagang, Jingjiang and the Yellow Sea farm in Jiangsu Province, separately. The rice breed planted in Zhangjiagang, Jingjiang and the Yellow Sea farm was Wuyunjing 29, Wuyunjing24 and Lianjing 7, separately, all of which were japonica hybrid rice. There were 3 row-hill spacing treatments in—at each site, which the row spacing by hill spacing was 30 cm by 11 cm, 25 cm by 14 cm, 25 cm by 11 cm, each repeated 3 times. By the influence of the spacing, the area of 30 cm by 11 cm (row spacing by hill spacing), 25 cm by 14 cm (row spacing by hill spacing) and 25 cm by 11 cm (row spacing by hill spacing) was 135 m2, 150 m2 and 150 m2, separately. At the same site within each treatment, only the row-hill spacing differences were the mutation factors, the other factors were controlled. The effect of the mechanical transplanting rice, the tillering dynamics of rice, yield and yield composition of rice were tested. The effect of mechanical rice transplanting measured immediately after transplanting. 200 continuous points were taken in each area; the number of plants per hole, seedling injury and inverted were recorded to calculate the average number of plants per hole, drain planting rate, seedling injury rate and seedling inverted rate. To determine the tillering dynamic, the number of tillers was counted every 7 days from rice transplanting to the full panicle stage, 20 points for each plot were fixed. Actual yield of rice was determined after ripening. For each treatment, 3 m2 of rice were weighed after harvesting to calculate the actual yield, repeated 3 times. According to the average number of plants per hill, 3 hill plants were selected for testing, and then the yield component and the theoretical yield of rice were calculated. SPSS17.0 and Excel 2007 were used for the statistical analysis. The results are indicated below: 1) Drain planting rate of 25 cm by 14 cm (row spacing by hill spacing) was the lowest, then was 25 cm by 11 cm (row spacing by hill spacing), 30 cm by 11 cm (row spacing by hill spacing) was the highest. These indicate that narrow row spacing large hill spacing was conducive to reducing the drain planting rate of rice. No rule was found from the rates of seedling injury and inverted in between these 3 breeds. 2) The number of tillers after tiller stabilization of 30 cm by 11 cm (row spacing by hill spacing) was the highest, and then was 25 cm by 14 cm (row spacing by hill spacing), 25 cm by 11 cm (row spacing by hill spacing) was the lowest. These indicate that large row spacing and hill spacing is conducive to the promotion of rice tillering. 3) In the aspect of the yield components, ears of 25 cm by 11 cm (row spacing by hill spacing) were the highest, and then were 25 cm by 14 cm (row spacing by hill spacing), 30 cm by 11 cm (row spacing by hill spacing) was the lowest. No rule was found between the grain numbers per spike, the seed rate and 1000-grain weight. Finally in the aspect of the theoretical rice yield and the actual rice yield, 25 cm by 11 cm(row spacing by hill spacing) was the highest, and then 25 cm by 14 cm (row spacing by hill spacing), 30 cm by 11 cm (row spacing by hill spacing) was the lowest. The theoretical rice yields of 25 cm by 11 cm (row spacing by hill spacing) were 9.68%-26.5% higher than 30 cm by 11 cm (row spacing by hill spacing), and the actual rice yields of 25 cm by 11 cm (row spacing by hill spacing) were 3.18%-20.6% higher than 30 cm by 11 cm (row spacing by hill spacing). The 25 cm by 11 cm (row spacing by hill spacing) row transplanter should be more suitable for mechanical rice planting in Jiangsu Province. [ABSTRACT FROM AUTHOR]