In this paper, research has been conducted to increase the quantity of fiber produced in the enterprise by creating a sorting device for spun seeds, dividing them into fractions by geometric dimensions, and by re-ginn...In this paper, research has been conducted to increase the quantity of fiber produced in the enterprise by creating a sorting device for spun seeds, dividing them into fractions by geometric dimensions, and by re-ginning, separating those with long fibers. A new model was developed for geometric sorting of cotton seeds in the harvest, and experiments determined its effectiveness and the optimal values of the factors affecting the efficiency using mathematical modeling. Based on the results of the study, graphs of the influence of factors on device performance and on device efficiency were constructed.展开更多
The aim of this study was to elucidate the effects of different machine-harvested cotton-planting patterns on defoliation,yield,and fiber quality in cotton and to provide support for improving the quality of machine-h...The aim of this study was to elucidate the effects of different machine-harvested cotton-planting patterns on defoliation,yield,and fiber quality in cotton and to provide support for improving the quality of machine-harvested cotton.In the 2015 and 2016 growing seasons,the Xinluzao 45(XLZ45)and Xinluzao 62(XLZ62)cultivars,which are primarily cultivated in northern Xinjiang,were used as study materials.Conventional wide-narrow row(WNR),wide and ultra-narrow row(UNR),wide-row spacing with high density(HWR),and wide-row spacing with low density(LWR)planting patterns were used to assess the effects of planting patterns on defoliation,yield,and fiber quality.Compared with WNR,the seed cotton yields were significantly decreased by 2.06–5.48%for UNR and by 2.50–6.99%for LWR,respectively.The main cause of reduced yield was a reduction in bolls per unit area.The variation in HWR yield was–1.07–1.07%with reduced bolls per unit area and increased boll weight,thus demonstrating stable production.In terms of fiber quality indicators,the planting patterns only showed significant effects on the micronaire value,with wide-row spacing patterns showing an increase in the micronaire values.The defoliation and boll-opening results showed that the number of leaves and dried leaves in HWR was the lowest among the four planting patterns.Prior to the application of defoliating agent and before machine-harvesting,the numbers of leaves per individual plant in HWR were decreased by 14.45 and 25.00%on average,respectively,compared with WNR,while the number of leaves per unit area was decreased by 27.44 and 36.21%on average,respectively.The rates of boll-opening and defoliation in HWR were the highest.Specifically,the boll-opening rate before defoliation and machine-harvesting in HWR was 44.54 and 5.94%higher on average than in WNR,while the defoliation rate prior to machine-harvesting was 3.45%higher on average than in WNR.The numbers of ineffective defoliated leaves and leaf trash in HWR were the lowest,decreased by 33.40 and 32.43%,respectively,compared with WNR.In conclusion,the HWR planting pattern is associated with a high and stable yield,does not affect fiber quality,promotes early maturation,and can effectively decrease the amount of leaf trash in machine-picked seed cotton,and thus its use is able to improve the quality of machine-harvested cotton.展开更多
Machine harvesting increases the foreign matter content of seed cotton. Excessive cleaning causes fiber damage and economic loss. Most trading companies in the Xinjiang Uygur Autonomous Region, China have indicated re...Machine harvesting increases the foreign matter content of seed cotton. Excessive cleaning causes fiber damage and economic loss. Most trading companies in the Xinjiang Uygur Autonomous Region, China have indicated reluctance to use machine-harvested cotton. The first objective was to determine how the fiber quality was affected by the ginning and lint cleaning and how the fiber damage during levels of lint cleaning changed. The second objective was to determine the optimum number of lint cleaners for machine-harvested cotton based on fiber damage. Cotton samples were collected from 13 fields and processed in seven ginneries between 2013 and 2015. The results indicated that ginning and lint cleaning didn＇t have significant effect on fiber strength and significantly affected both fiber length and short fiber index. Fiber length was reduced by more than 1.00 mm from six of 13 fields after lint cleaning, then the damage rate on short fiber index from 11 of 13 fields was more than 20%. The third lint cleaning caused great fiber damage, reducing fiber length by 0.35 mm and increasing short fiber index by 0.65%. So, the lint should be cleaned by one lint cleaner in the Xinjiang, however, the stage of lint cleaning was sometimes omitted when the foreign matter content of lint was little.展开更多
【目的】叶面积指数(leaf area index,LAI)是表征作物长势、光合、蒸腾的重要指标。论文旨在研究不同生育期、多生育期无人机多光谱数据棉花LAI估测模型,明确不同生育期间棉花LAI估测模型变化规律,为实时掌握棉花长势并因地制宜进行田...【目的】叶面积指数(leaf area index,LAI)是表征作物长势、光合、蒸腾的重要指标。论文旨在研究不同生育期、多生育期无人机多光谱数据棉花LAI估测模型,明确不同生育期间棉花LAI估测模型变化规律,为实时掌握棉花长势并因地制宜进行田间科学管理提供依据。【方法】利用大疆精灵4多光谱无人机获取棉花现蕾期、初花期、结铃期、吐絮期多光谱图像和RGB图像。选用归一化差植被指数(NDVI)、绿度归一化差植被指数(GNDVI)、归一化差红边指数(NDRE)、叶片叶绿素指数(LCI)、优化的土壤调节植被指数(OSAVI)5种多光谱指数和修正红绿植被指数(MGRVI)、红绿植被指数(GRVI)、绿叶指数(GLA)、超红指数(EXR)、大气阻抗植被指数(VARI)5种颜色指数分别建立棉花各生育期及棉花生长多生育期数据集合,结合打孔法获取地面LAI实测数据,使用机器学习算法中偏最小二乘(PLSR)、岭回归(RR)、随机森林(RF)、支持向量机(SVM)、神经网络(BP)构建棉花LAI预测模型。【结果】覆膜棉花LAI随着生育期的变化呈现先增长后下降的趋势,现蕾期、初花期、结铃期内侧棉花叶面积指数均值均显著大于外侧(P<0.05);选择的指数在各时期彼此间均呈显著相关(P<0.05),总体而言,多光谱指数与颜色指数间的相关性随着生育期的进行而呈现下降趋势,选择的指数在各时期均与棉花LAI相关性显著(P<0.05),多光谱指数相关系数介于0.35—0.85,颜色指数相关系数介于0.49—0.71,相关系数绝对值较大的指数多为多光谱指数,颜色指数与棉花LAI的相关系数绝对值较小;估测模型性能结果显示棉花各生育期模型中多光谱指数优于颜色指数,且各指数模型预测性能随着生育期的变化呈现一定规律性,NDVI是预测棉花LAI的最优指数。从模型结果上看,RF模型和BP模型在各生育期下获得了较高的估计精度。初花期LAI反演模型精度最高,最优模型验证集R2为0.809,MAE为0.288,NRMSE为0.120。多生育期最优模型验证集R2为0.386,MAE为0.700,NRMSE为0.198。【结论】棉花内外侧LAI在现蕾期、初花期、结铃期存在显著差异。在各生育期中,RF和BP模型是预测棉花LAI较优模型。NDVI在各指数中表现最好,是预测棉花LAI的最优指数。多生育期模型效果较单生育期明显下降,最优指数为GNDVI,最优模型为BP。本研究中预测棉花LAI的最优窗口期是初花期。研究结果可为无人机遥感监测棉花LAI提供理论依据和技术支持。展开更多
文摘In this paper, research has been conducted to increase the quantity of fiber produced in the enterprise by creating a sorting device for spun seeds, dividing them into fractions by geometric dimensions, and by re-ginning, separating those with long fibers. A new model was developed for geometric sorting of cotton seeds in the harvest, and experiments determined its effectiveness and the optimal values of the factors affecting the efficiency using mathematical modeling. Based on the results of the study, graphs of the influence of factors on device performance and on device efficiency were constructed.
基金supported by the National Natural Science Foundation of China (31560342)the Major Science and Technology Projects of Xinjiang Production and Construction Corps, China (2016AA001-2)the National Key Research and Development Program of China (2017YFD0201900)
文摘The aim of this study was to elucidate the effects of different machine-harvested cotton-planting patterns on defoliation,yield,and fiber quality in cotton and to provide support for improving the quality of machine-harvested cotton.In the 2015 and 2016 growing seasons,the Xinluzao 45(XLZ45)and Xinluzao 62(XLZ62)cultivars,which are primarily cultivated in northern Xinjiang,were used as study materials.Conventional wide-narrow row(WNR),wide and ultra-narrow row(UNR),wide-row spacing with high density(HWR),and wide-row spacing with low density(LWR)planting patterns were used to assess the effects of planting patterns on defoliation,yield,and fiber quality.Compared with WNR,the seed cotton yields were significantly decreased by 2.06–5.48%for UNR and by 2.50–6.99%for LWR,respectively.The main cause of reduced yield was a reduction in bolls per unit area.The variation in HWR yield was–1.07–1.07%with reduced bolls per unit area and increased boll weight,thus demonstrating stable production.In terms of fiber quality indicators,the planting patterns only showed significant effects on the micronaire value,with wide-row spacing patterns showing an increase in the micronaire values.The defoliation and boll-opening results showed that the number of leaves and dried leaves in HWR was the lowest among the four planting patterns.Prior to the application of defoliating agent and before machine-harvesting,the numbers of leaves per individual plant in HWR were decreased by 14.45 and 25.00%on average,respectively,compared with WNR,while the number of leaves per unit area was decreased by 27.44 and 36.21%on average,respectively.The rates of boll-opening and defoliation in HWR were the highest.Specifically,the boll-opening rate before defoliation and machine-harvesting in HWR was 44.54 and 5.94%higher on average than in WNR,while the defoliation rate prior to machine-harvesting was 3.45%higher on average than in WNR.The numbers of ineffective defoliated leaves and leaf trash in HWR were the lowest,decreased by 33.40 and 32.43%,respectively,compared with WNR.In conclusion,the HWR planting pattern is associated with a high and stable yield,does not affect fiber quality,promotes early maturation,and can effectively decrease the amount of leaf trash in machine-picked seed cotton,and thus its use is able to improve the quality of machine-harvested cotton.
基金supported by the National Key Technology R&D Program of China (2014BAD09B03)the National Natural Science Foundation of China (31560366)
文摘Machine harvesting increases the foreign matter content of seed cotton. Excessive cleaning causes fiber damage and economic loss. Most trading companies in the Xinjiang Uygur Autonomous Region, China have indicated reluctance to use machine-harvested cotton. The first objective was to determine how the fiber quality was affected by the ginning and lint cleaning and how the fiber damage during levels of lint cleaning changed. The second objective was to determine the optimum number of lint cleaners for machine-harvested cotton based on fiber damage. Cotton samples were collected from 13 fields and processed in seven ginneries between 2013 and 2015. The results indicated that ginning and lint cleaning didn＇t have significant effect on fiber strength and significantly affected both fiber length and short fiber index. Fiber length was reduced by more than 1.00 mm from six of 13 fields after lint cleaning, then the damage rate on short fiber index from 11 of 13 fields was more than 20%. The third lint cleaning caused great fiber damage, reducing fiber length by 0.35 mm and increasing short fiber index by 0.65%. So, the lint should be cleaned by one lint cleaner in the Xinjiang, however, the stage of lint cleaning was sometimes omitted when the foreign matter content of lint was little.
文摘【目的】叶面积指数(leaf area index,LAI)是表征作物长势、光合、蒸腾的重要指标。论文旨在研究不同生育期、多生育期无人机多光谱数据棉花LAI估测模型,明确不同生育期间棉花LAI估测模型变化规律,为实时掌握棉花长势并因地制宜进行田间科学管理提供依据。【方法】利用大疆精灵4多光谱无人机获取棉花现蕾期、初花期、结铃期、吐絮期多光谱图像和RGB图像。选用归一化差植被指数(NDVI)、绿度归一化差植被指数(GNDVI)、归一化差红边指数(NDRE)、叶片叶绿素指数(LCI)、优化的土壤调节植被指数(OSAVI)5种多光谱指数和修正红绿植被指数(MGRVI)、红绿植被指数(GRVI)、绿叶指数(GLA)、超红指数(EXR)、大气阻抗植被指数(VARI)5种颜色指数分别建立棉花各生育期及棉花生长多生育期数据集合,结合打孔法获取地面LAI实测数据,使用机器学习算法中偏最小二乘(PLSR)、岭回归(RR)、随机森林(RF)、支持向量机(SVM)、神经网络(BP)构建棉花LAI预测模型。【结果】覆膜棉花LAI随着生育期的变化呈现先增长后下降的趋势,现蕾期、初花期、结铃期内侧棉花叶面积指数均值均显著大于外侧(P<0.05);选择的指数在各时期彼此间均呈显著相关(P<0.05),总体而言,多光谱指数与颜色指数间的相关性随着生育期的进行而呈现下降趋势,选择的指数在各时期均与棉花LAI相关性显著(P<0.05),多光谱指数相关系数介于0.35—0.85,颜色指数相关系数介于0.49—0.71,相关系数绝对值较大的指数多为多光谱指数,颜色指数与棉花LAI的相关系数绝对值较小;估测模型性能结果显示棉花各生育期模型中多光谱指数优于颜色指数,且各指数模型预测性能随着生育期的变化呈现一定规律性,NDVI是预测棉花LAI的最优指数。从模型结果上看,RF模型和BP模型在各生育期下获得了较高的估计精度。初花期LAI反演模型精度最高,最优模型验证集R2为0.809,MAE为0.288,NRMSE为0.120。多生育期最优模型验证集R2为0.386,MAE为0.700,NRMSE为0.198。【结论】棉花内外侧LAI在现蕾期、初花期、结铃期存在显著差异。在各生育期中,RF和BP模型是预测棉花LAI较优模型。NDVI在各指数中表现最好,是预测棉花LAI的最优指数。多生育期模型效果较单生育期明显下降,最优指数为GNDVI,最优模型为BP。本研究中预测棉花LAI的最优窗口期是初花期。研究结果可为无人机遥感监测棉花LAI提供理论依据和技术支持。
