In order to further improve the utility of unmanned aerial vehicle(UAV)remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge m...In order to further improve the utility of unmanned aerial vehicle(UAV)remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge mulching,ridge–furrow full mulching, and flat cropping full mulching in winter wheat.Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV.Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks(ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out.The results showed that the CGEI of winter wheat under film mulching constructed using the FCE method could objectively and comprehensively evaluate the crop growth status.The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75,a root mean square error of 8.40, and a mean absolute value error of 6.53.Spectral purification could eliminate the interference of background effects caused by mulching and soil, effectively improving the accuracy of the remotesensing inversion of winter wheat under film mulching, with the best inversion effect achieved on the ridge–furrow full mulching area after spectral purification.The results of this study provide a theoretical reference for the use of UAV remote-sensing to monitor the growth status of winter wheat with film mulching.展开更多
Substrate and nutrient supply are essential for vegetable cultivation in greenhouse.The strategies for plant nutrient supply vary depending on the cultivation methods or substrate dosages employed.With the development...Substrate and nutrient supply are essential for vegetable cultivation in greenhouse.The strategies for plant nutrient supply vary depending on the cultivation methods or substrate dosages employed.With the development of mechanization,wide-row spacing substrate cultivation became an optimize mode of the greenhouse cucumber cultivation,aligning with the trend of intelligent agriculture.To determine the optimal nutrient solution supply amount(NS)and supply frequency(SF)for promoting the integrated growth of cucumber under wide-row spacing substrate cultivation,we explored the effects of substrate supply amount(SS),NS,and SF on cucumber yield,quality,and element utilization efficiency.A five-level quadratic orthogonal rotation combination design with three experimental factors(NS,SF,and SS)was implemented for 23 coupling treatments over three growing seasons,including spring(2022S and 2023S)and autumn(2022A).The technique for order preference by similarity to ideal solution(TOPSIS)combining weights based on game theory was applied to construct cucumber comprehensive growth evaluation model.Single and two experimental factors analyses revealed significant effects of single factors and the coupling of NS-SS,NS-SF and SS-SF on the integrated growth of cucumber for all three growing seasons.For the NS-SF-SS combination,the optimal parameters for comprehensive cucumber growth were determined as follows:levels of^(-1).68 for NS,-0.7 for SF,and^(-1).682 for SS in 2022A;-0.43 for NS,-0.06 for SF,and 0.34 for SS in 2022S;0.3 for NS,-0.02 for SF,and 0.04 for SS in 2023S.Furthermore,for SS ranges of 2.00-3.01,3.01-4.50,4.50-5.99,5.99-7.00(L·plant^(-1)),the corresponding NS and SF intervals maximizing cucumber integrated growth in spring were:0.28-0.30(L·plant^(-1))and 6(times·d^(-1)),0.26-0.30(L·plant^(-1))and 6(times·d^(-1)),0.25-0.30(L·plant^(-1))and 6(times·d^(-1)),0.23-0.30(L·plant^(-1))and 6(times·d^(-1)),respectively.With the same SS,the corresponding NS and SF intervals that maximized cucumber integrated growth in autumn were:0.10(L·plant^(-1))and 8(times·d^(-1)),0.18(L·plant^(-1))and 7(times·d^(-1)),0.30(L·plant^(-1))and 6(times·d^(-1)),0.49(L·plant^(-1))and 5(times·d^(-1)),respectively.The results provide a theoretical basis for solution management,and further in-depth research on cucumber cultivation.展开更多
基金This study was funded by the National Key R&D Program of China(2021YFD1900700)the National Natural Science Foundation of China(51909221)the China Postdoctoral Science Foundation(2020T130541 and 2019M650277).
文摘In order to further improve the utility of unmanned aerial vehicle(UAV)remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge mulching,ridge–furrow full mulching, and flat cropping full mulching in winter wheat.Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV.Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks(ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out.The results showed that the CGEI of winter wheat under film mulching constructed using the FCE method could objectively and comprehensively evaluate the crop growth status.The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75,a root mean square error of 8.40, and a mean absolute value error of 6.53.Spectral purification could eliminate the interference of background effects caused by mulching and soil, effectively improving the accuracy of the remotesensing inversion of winter wheat under film mulching, with the best inversion effect achieved on the ridge–furrow full mulching area after spectral purification.The results of this study provide a theoretical reference for the use of UAV remote-sensing to monitor the growth status of winter wheat with film mulching.
基金supported by the China Agriculture Research System(Grant No.CARS-23-D06)the Key Research and Development Program of Shaanxi Province(Grant Nos.2024NC2-GJHX-29 and 2024NC-ZDCYL-05-08)Shaanxi Agricultural Collaborative Innovation and Extension Alliance Project(Grant No.LMZD202202).
文摘Substrate and nutrient supply are essential for vegetable cultivation in greenhouse.The strategies for plant nutrient supply vary depending on the cultivation methods or substrate dosages employed.With the development of mechanization,wide-row spacing substrate cultivation became an optimize mode of the greenhouse cucumber cultivation,aligning with the trend of intelligent agriculture.To determine the optimal nutrient solution supply amount(NS)and supply frequency(SF)for promoting the integrated growth of cucumber under wide-row spacing substrate cultivation,we explored the effects of substrate supply amount(SS),NS,and SF on cucumber yield,quality,and element utilization efficiency.A five-level quadratic orthogonal rotation combination design with three experimental factors(NS,SF,and SS)was implemented for 23 coupling treatments over three growing seasons,including spring(2022S and 2023S)and autumn(2022A).The technique for order preference by similarity to ideal solution(TOPSIS)combining weights based on game theory was applied to construct cucumber comprehensive growth evaluation model.Single and two experimental factors analyses revealed significant effects of single factors and the coupling of NS-SS,NS-SF and SS-SF on the integrated growth of cucumber for all three growing seasons.For the NS-SF-SS combination,the optimal parameters for comprehensive cucumber growth were determined as follows:levels of^(-1).68 for NS,-0.7 for SF,and^(-1).682 for SS in 2022A;-0.43 for NS,-0.06 for SF,and 0.34 for SS in 2022S;0.3 for NS,-0.02 for SF,and 0.04 for SS in 2023S.Furthermore,for SS ranges of 2.00-3.01,3.01-4.50,4.50-5.99,5.99-7.00(L·plant^(-1)),the corresponding NS and SF intervals maximizing cucumber integrated growth in spring were:0.28-0.30(L·plant^(-1))and 6(times·d^(-1)),0.26-0.30(L·plant^(-1))and 6(times·d^(-1)),0.25-0.30(L·plant^(-1))and 6(times·d^(-1)),0.23-0.30(L·plant^(-1))and 6(times·d^(-1)),respectively.With the same SS,the corresponding NS and SF intervals that maximized cucumber integrated growth in autumn were:0.10(L·plant^(-1))and 8(times·d^(-1)),0.18(L·plant^(-1))and 7(times·d^(-1)),0.30(L·plant^(-1))and 6(times·d^(-1)),0.49(L·plant^(-1))and 5(times·d^(-1)),respectively.The results provide a theoretical basis for solution management,and further in-depth research on cucumber cultivation.
