Statistical models can efficiently establish the relationships between crop growth and environmental conditions while explicitly quantifying uncertainties. This study aimed to test the efficiency of statistical models...Statistical models can efficiently establish the relationships between crop growth and environmental conditions while explicitly quantifying uncertainties. This study aimed to test the efficiency of statistical models established using partial least squares regression(PLSR) and artificial neural network(ANN) in predicting seed yields of sunflower(Helianthus annuus). Two-year field trial data on sunflower growth under different salinity levels and nitrogen(N) application rates in the Yichang Experimental Station in Hetao Irrigation District, Inner Mongolia, China, were used to calibrate and validate the statistical models. The variable importance in projection score was calculated in order to select the sensitive crop indices for seed yield prediction. We found that when the most sensitive indices were used as inputs for seed yield estimation, the PLSR could attain a comparable accuracy(root mean square error(RMSE) = 0.93 t ha-1, coefficient of determination(R^2) = 0.69) to that when using all measured indices(RMSE = 0.81 t ha-1,R^2= 0.77). The ANN model outperformed the PLSR for yield prediction with different combinations of inputs of both microplots and field data. The results indicated that sunflower seed yield could be reasonably estimated by using a small number of crop characteristic indices under complex environmental conditions and management options(e.g., saline soils and N application). Since leaf area index and plant height were found to be the most sensitive crop indices for sunflower seed yield prediction, remotely sensed data and the ANN model may be joined for regional crop yield simulation.展开更多
Maintenance of sufficiently healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling.Tightly regulated age-related physiological senescence and various biotic and abiot...Maintenance of sufficiently healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling.Tightly regulated age-related physiological senescence and various biotic and abiotic stressors drive overall greenness decay dynamics under field conditions.Besides direct effects on green leaf area in terms of leaf damage,stressors often anticipate or accelerate physiological senescence,which may multiply their negative impact on grain filling.Here,we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots(stems+leaves)based on deep learning models for semantic segmentation and color properties of vegetation.A vegetation segmentation model was trained using semisynthetic training data generated using image composition and generative adversarial neural networks,which greatly reduced the risk of annotation uncertainties and annotation effort.Application of the models to image time series revealed temporal patterns of greenness decay as well as the relative contributions of chlorosis and necrosis.Image-based estimation of greenness decay dynamics was highly correlated with scoring-based estimations(r≈0.9).Contrasting patterns were observed for plots with different levels of foliar diseases,particularly septoria tritici blotch.Our results suggest that tracking the chlorotic and necrotic fractions separately may enable(a)a separate quantification of the contribution of biotic stress and physiological senescence on overall green leaf area dynamics and(b)investigation of interactions between biotic stress and physiological senescence.The high-throughput nature of our methodology paves the way to conducting genetic studies of disease resistance and tolerance.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 51609175, 51790533, 51879196, and 51439006)
文摘Statistical models can efficiently establish the relationships between crop growth and environmental conditions while explicitly quantifying uncertainties. This study aimed to test the efficiency of statistical models established using partial least squares regression(PLSR) and artificial neural network(ANN) in predicting seed yields of sunflower(Helianthus annuus). Two-year field trial data on sunflower growth under different salinity levels and nitrogen(N) application rates in the Yichang Experimental Station in Hetao Irrigation District, Inner Mongolia, China, were used to calibrate and validate the statistical models. The variable importance in projection score was calculated in order to select the sensitive crop indices for seed yield prediction. We found that when the most sensitive indices were used as inputs for seed yield estimation, the PLSR could attain a comparable accuracy(root mean square error(RMSE) = 0.93 t ha-1, coefficient of determination(R^2) = 0.69) to that when using all measured indices(RMSE = 0.81 t ha-1,R^2= 0.77). The ANN model outperformed the PLSR for yield prediction with different combinations of inputs of both microplots and field data. The results indicated that sunflower seed yield could be reasonably estimated by using a small number of crop characteristic indices under complex environmental conditions and management options(e.g., saline soils and N application). Since leaf area index and plant height were found to be the most sensitive crop indices for sunflower seed yield prediction, remotely sensed data and the ANN model may be joined for regional crop yield simulation.
文摘Maintenance of sufficiently healthy green leaf area after anthesis is key to ensuring an adequate assimilate supply for grain filling.Tightly regulated age-related physiological senescence and various biotic and abiotic stressors drive overall greenness decay dynamics under field conditions.Besides direct effects on green leaf area in terms of leaf damage,stressors often anticipate or accelerate physiological senescence,which may multiply their negative impact on grain filling.Here,we present an image processing methodology that enables the monitoring of chlorosis and necrosis separately for ears and shoots(stems+leaves)based on deep learning models for semantic segmentation and color properties of vegetation.A vegetation segmentation model was trained using semisynthetic training data generated using image composition and generative adversarial neural networks,which greatly reduced the risk of annotation uncertainties and annotation effort.Application of the models to image time series revealed temporal patterns of greenness decay as well as the relative contributions of chlorosis and necrosis.Image-based estimation of greenness decay dynamics was highly correlated with scoring-based estimations(r≈0.9).Contrasting patterns were observed for plots with different levels of foliar diseases,particularly septoria tritici blotch.Our results suggest that tracking the chlorotic and necrotic fractions separately may enable(a)a separate quantification of the contribution of biotic stress and physiological senescence on overall green leaf area dynamics and(b)investigation of interactions between biotic stress and physiological senescence.The high-throughput nature of our methodology paves the way to conducting genetic studies of disease resistance and tolerance.
