Sugar beet（Beta vulgaris L.） is an industrial crop, grown worldwide for sugar production. In Pakistan, sugar is mostly extracted from sugarcane, soil and environmental conditions are equally favorable for sugar beet...Sugar beet（Beta vulgaris L.） is an industrial crop, grown worldwide for sugar production. In Pakistan, sugar is mostly extracted from sugarcane, soil and environmental conditions are equally favorable for sugar beet cultivation. Beet sugar contents are higher than sugarcane sugar contents, which can be further increased by potassium（K） fertilization. Total K concentration is higher in Pakistani soils developed from mica minerals, but it does not represent plant available K for sustainable plant growth. A pot experiment was conducted in the wire-house of Institute of Soil and Environmental Sciences at University of Agriculture Faisalabad, Pakistan. K treatments were the following： no K（K_0）, K application at 148 kg ha^（–1）（K_1） and 296 kg ha^（–1）（K_2）. Irrigation levels were used as water sufficient at 60% water holding capacity and water deficient at 40% water holding capacity. The growth, yield and beet quality data were analyzed statistically using LSD. The results revealed that increase in the level of K fertilization at water sufficient level significantly increased plant growth, beet yield and industrial beet sugar content. The response of K fertilization under water deficient condition was also similar, however overall sugar production was less than that in water sufficient conditions. It is concluded from this study that K application could be used not only to enhance plant growth and beet yield but also enhance beet sugar content both under water-deficient as well as water-sufficient conditions.展开更多
Accurate pre-harvest prediction of sugar beet yield is vital for effective agricultural management and decision-making.However,traditional methods are constrained by reliance on empirical knowledge,time-consuming proc...Accurate pre-harvest prediction of sugar beet yield is vital for effective agricultural management and decision-making.However,traditional methods are constrained by reliance on empirical knowledge,time-consuming processes,resource intensiveness,and spatial-temporal variability in prediction accuracy.This study presented a plot-level approach that leverages UAV technology and recurrent neural networks to provide field yield predictions within the same growing season,addressing a significant gap in previous research that often focuses on regional scale predictions relied on multi-year history datasets.End-of-season yield and quality parameters were forecasted using UAV-derived time series data and meteorological factors collected at three critical growth stages,providing a timely and practical tool for farm management.Two years of data covering 185 sugar beet varieties were used to train a developed stacked Long Short-Term Memory(LSTM)model,which was compared with traditional machine learning approaches.Incorporating fresh weight estimates of aboveground and root biomass as predictive factors significantly enhanced prediction accuracy.Optimal performance in prediction was observed when utilizing data from all three growth periods,with R^(2)values of 0.761(rRMSE=7.1%)for sugar content,0.531(rRMSE=22.5%)for root yield,and 0.478(rRMSE=23.4%)for sugar yield.Furthermore,combining data from the first two growth periods shows promising results for making the predictions earlier.Key predictive features identified through the Permutation Importance(PIMP)method provided insights into the main factors influencing yield.These findings underscore the potential of using UAV time-series data and recurrent neural networks for accurate pre-harvest yield prediction at the field scale,supporting timely and precise agricultural decisions.展开更多
文摘Sugar beet（Beta vulgaris L.） is an industrial crop, grown worldwide for sugar production. In Pakistan, sugar is mostly extracted from sugarcane, soil and environmental conditions are equally favorable for sugar beet cultivation. Beet sugar contents are higher than sugarcane sugar contents, which can be further increased by potassium（K） fertilization. Total K concentration is higher in Pakistani soils developed from mica minerals, but it does not represent plant available K for sustainable plant growth. A pot experiment was conducted in the wire-house of Institute of Soil and Environmental Sciences at University of Agriculture Faisalabad, Pakistan. K treatments were the following： no K（K_0）, K application at 148 kg ha^（–1）（K_1） and 296 kg ha^（–1）（K_2）. Irrigation levels were used as water sufficient at 60% water holding capacity and water deficient at 40% water holding capacity. The growth, yield and beet quality data were analyzed statistically using LSD. The results revealed that increase in the level of K fertilization at water sufficient level significantly increased plant growth, beet yield and industrial beet sugar content. The response of K fertilization under water deficient condition was also similar, however overall sugar production was less than that in water sufficient conditions. It is concluded from this study that K application could be used not only to enhance plant growth and beet yield but also enhance beet sugar content both under water-deficient as well as water-sufficient conditions.
基金supported by the Science and Technology projects Inner Mongolia(Grant No.2019ZD024)National Center of Pratacultural Technology Innovation(under preparation)Special fund for innovation platform construction(CCPTZX2023K03).
文摘Accurate pre-harvest prediction of sugar beet yield is vital for effective agricultural management and decision-making.However,traditional methods are constrained by reliance on empirical knowledge,time-consuming processes,resource intensiveness,and spatial-temporal variability in prediction accuracy.This study presented a plot-level approach that leverages UAV technology and recurrent neural networks to provide field yield predictions within the same growing season,addressing a significant gap in previous research that often focuses on regional scale predictions relied on multi-year history datasets.End-of-season yield and quality parameters were forecasted using UAV-derived time series data and meteorological factors collected at three critical growth stages,providing a timely and practical tool for farm management.Two years of data covering 185 sugar beet varieties were used to train a developed stacked Long Short-Term Memory(LSTM)model,which was compared with traditional machine learning approaches.Incorporating fresh weight estimates of aboveground and root biomass as predictive factors significantly enhanced prediction accuracy.Optimal performance in prediction was observed when utilizing data from all three growth periods,with R^(2)values of 0.761(rRMSE=7.1%)for sugar content,0.531(rRMSE=22.5%)for root yield,and 0.478(rRMSE=23.4%)for sugar yield.Furthermore,combining data from the first two growth periods shows promising results for making the predictions earlier.Key predictive features identified through the Permutation Importance(PIMP)method provided insights into the main factors influencing yield.These findings underscore the potential of using UAV time-series data and recurrent neural networks for accurate pre-harvest yield prediction at the field scale,supporting timely and precise agricultural decisions.
