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Detecting winter canola(Brassica napus) phenological stages using an improved shape-model method based on time-series UAV spectral data

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The Crop Journal 2022年第5期10卷 1353-1362页

作者： Chao Zhang Zi’ang Xie Jiali Shang Jiangui Liu Taifeng Dong Min Tang Shaoyuan Feng Huanjie Cai College of Hydraulic Science and Engineering Yangzhou UniversityYangzhou 225009JiangsuChina Agriculture and Agri-Food Canada Ottawa Research and Development Centre960 Carling AvenueOttawaON K1A 0C6Canada Key Laboratory of Agricultural Soil and Water Engineering in Arid Area of Ministry of Education Northwest A&F UniversityYangling 712100ShaanxiChina

Accurate information about phenological stages is essential for canola field management practices such as irrigation, fertilization, and harvesting. Previous studies in canola phenology monitoring focused mainly on the flowering stage, using its apparent structure features and colors. Additional phenological stages have been largely overlooked. The objective of this study was to improve a shape-model method(SMM) for extracting winter canola phenological stages from time-series top-of-canopy reflectance images collected by an unmanned aerial vehicle(UAV). The transformation equation of the SMM was refined to account for the multi-peak features of the temporal dynamics of three vegetation indices(VIs)(NDVI, EVI, and CI). An experiment with various seeding scenarios was conducted, including four different seeding dates and three seeding densities. Three mathematical functions: asymmetric Gaussian function(AGF), Fourier function, and double logistic function, were employed to fit timeseries vegetation indices to extract information about phenological stages. The refined SMM effectively estimated the phenological stages of canola, with a minimum root mean square error(RMSE) of 3.7 days for all phenological stages. The AGF function provided the best fitting performance, as it captured multiple peaks in the growth dynamics characteristics for all seeding date scenarios using four scaling parameters. For the three selected VIs, CIred-edgeachieved the greatest accuracy in estimating the phenological stage dates. This study demonstrates the high potential of the refined SMM for estimating winter canola phenology.

关键词： Time-seriesⅥ Asymmetric Gaussian function phenological stage Shape model Remote sensing

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Adjusting the nutrient solution formula based on growth stages to promote the yield and quality of strawberry in greenhouse

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International Journal of Agricultural and Biological Engineering 2023年第2期16卷 57-64页

作者： Weizhong Yu Jianfeng Zheng Yingli Wang Fang Ji Baoying Zhu Key Laboratory of Agricultural Engineering in Structure and Environment of MARA College of Water Resources&Civil EngineeringChina Agricultural UniversityBeijing 100083China Beijing Changping District Management Center of Xiaotangshan Modern Agricultural Science and Technology Demonstration Park Beijing 102200China

To optimize the nutrient supply of strawberry,fertilization was reduced as much as possible on the premise of satisfying the elements needed for strawberry *** study put forward a modified nutrient solution formula(NM)by adjusting the ratios of N/K,K/Ca,NO_(3)^(-)-N/NH_(4)^(+)-N in vegetative growth,flowering and fruiting,and fruit expanding stages based on Yamasaki strawberry nutrient solution formula(NY).Taking the NY as the control group,the NM was verified with elevated-grown‘Ssanta’strawberry using three different substrates(Vulcanics-added substrate,Horticultural substrate,Comme rcial substrate)in *** results indicated that under the NM treatment,strawberry plants had better photosynthesis in the vegetative growth stage and flowering and fruiting stage,and the first crop yield and total crops yield per plant also increased by 20%.Although the vegetative growth stage was longer,it entered the full fruiting stage earlier so that the maturity stage was advanced one *** to strawberries grown in horticultural substrate irrigated with NY,the fruit yield per plant of‘Ssanta’strawberry grown in horticultural substrate irrigated with NM increased by 26%,and the sugar-acid ratio and vitamin C content of primary fruits reached 10.6 and 74.1 mg/100g,improving by 41%and 34%*** NM based on the growth stage can optimize the element needs of strawberry in all growth and development stages and maintain the substrate’s pH stability,improving the yield and quality of‘Ssanta’strawberry under substrate ***,it is recommended to use the horticultural substrate irrigated with the modified nutrient solution for the‘Ssanta’strawberries cultivation elevated in greenhouse to improve the economic benefit of strawberry cultivation.

关键词： elevated-grown strawberry nutrient solution formula substrate cultivation phenological stage fruit yield fruit quality

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Artificial Neural Networks Application to Predict Wheat Yield Using Climatic Data

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Journal of Agricultural Science and Technology(B) 2011年第1期1卷 76-88页

作者： B. Safa A. Khalili M. Teshnehlab A. Liaghat Department of Agronomy and Horticulture University of Nebraska-Lineoln 202 Keim Hall Lincoln NE 68583-0915 USA Department of Irrigation and Reclamation Engineering College of Agriculture University of Tehran Karaj lran Department of Electrical Engineering K.N. Toosi Technology University Tehran lran

The goal of this study was to apply artificial neural networks to predict rain-fed wheat yield using meteorological data a few days to few months before harvesting. The climatic observation data used; were mean of daily minimum and maximum temperature, extreme of daily minimum and maximum temperature, sum of daily rainfall, number of rainy days, sum of daily sun hours, mean of daily wind speed, extreme of daily wind speed, mean of daily relative humidity, and sum of daily water requirements that were collected during 1990-1999 in Sararood Station for wheat phenological stages consisting; sowing, germination, emergence, 3rd leaves, tillering, stem formation, heading, flowering, milk maturity, wax maturity, full maturity, separately for each growing season. Then, they arranged in a matrix whose rows form each of the statistical years and the columns are meteorological factors at each phenological stage. Finally, the obtained model had the following capabilities： Prediction of wheat yield with maximum errors of 45-60 kg/ha at least two months before full maturity stage, determination of the sensitivity of each phenological stage with respect to meteorological factors, and determination of the priority order and importance of each meteorological factor effective in plant growth and crop yield.

关键词： Artificial neural network wheat yield climatic data phenological stage crop model.

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