水肥管理对紫花苜蓿种子生产的影响及多光谱机器学习模型构建OA

The seed yield and quality of the leguminous forage alfalfa(Medicago sativa L.)are significantly influenced by water and fertilizer management.This study aimed to investigate the effects of different water and phosphorus fertilizer treatments on seed yield and seed quality of alfalfa.The experiment was designed to com-bine treatments with different irrigation levels(600,900,and 1200 m³·hm-2)and phosphorus fertilizer rates(100,200,and 300 kg·hm-2).Seed yield,yield components,soluble protein content,and starch content were measured.Additionally,multispectral information of 19 bands was acquired,and an nCDA-CNN model was constructed to evaluate the potential of multispectral image technology for seed detection under different water and fertilizer treatments.The results showed that the appropriate water and phosphorus fertilizer applica-tion significantly increased seed yield,protein content,and starch content(P<0.05).Among all treatments,the combination of 600 m³·hm-2 irrigation and 200 kg·hm-2 phosphorus fertilizer achieved the highest seed yield(885.44 kg·hm-2),representing an increase of 287.96 kg·hm-2 compared to the control.Higher soluble pro-tein and starch contents of seeds were observed under the treatment with 600 m³·hm-2 irrigation and 100 kg·hm⁻² phosphorus fertilizer.Path analysis indicated that the number of seeds per pod had a greater influence on seed yield.Multispectral image analysis revealed that the nCDA-CNN model accurately predicted alfalfa seeds sub-jected to different water and fertilizer treatments,with a recognition accuracy of 95%.This provides a high-throughput,non-destructive detection method for alfalfa seed production.Optimized water and fertilizer manag-ement can significantly enhance alfalfa seed production.Multispectral image technology combined with deep learning models holds important application prospects for quality detection and sustainable development in the grass seed industry.