Huimin Li , Ziyuan Wei , Miao Lu , Pan Gao , Zhangtong Sun , Huarui Wu , Jin Hu
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引用次数: 0
Abstract
Nitrogen (N) fertilization plays an important role in plant life activities and growth. To optimize N uptake and improve dry matter accumulation in plants, this study proposed a method to obtain optimal root zone temperatures (RZT) under different N levels. The experiment was designed with five N levels and five RZTs for tomato seedlings, and the chlorophyll fluorescence data of tomato leaves were measured at each treatment. Polynomial regression and elastic net regularization were used to fitting the response curve of Fv/Fo, which is indicated to the potential activity of photosystem Ⅱ (PSⅡ). The feature parameters of curvature on the response curve were considered as the targets for regulation and were calculated by the U-chord curvature method. The dynamic regulation model of suitable RZT of tomato was obtained by fitting the curvature feature parameters. The results of the verification experiment showed that the experimental group increased root dry weight by 20.60%-92.30%, stem dry weight by 9.09%-39.90%, leaf N content by 2.70%-6.15%, and leaf phosphorus content by 6.08%-8.59%. Therefore, RZT optimization has a key role in improving plant photosynthesis, plant N uptake efficiency and dry matter accumulation under different N fertilization conditions. The research is significant for RZT optimization of hydroponic crops with different nutrient supply in protected agriculture.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.