Ashmita Rawal, Alfred Hartemink, Yakun Zhang, Yi Wang, Richard A. Lankau, Matthew D. Ruark
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引用次数: 0
Abstract
Visible-Near Infrared (vis-NIR) spectroscopy can provide a faster, cost-effective, and user-friendly solution to monitor leaf N status, potentially overcoming the limitations of current techniques. The objectives of the study were to develop and validate partial least square regression (PLSR) to estimate the total N contents of fresh and removed leaves of potatoes using the vis-NIR spectral range (350–2500 nm) generated from a handheld proximal sensor. The model was built using data collected from Hancock Agricultural Research Station, WI, USA in 2020 and was validated using samples collected in 2021 for four different conditions. The conditions included two sites (Coloma and Hancock), four potato varieties (Burbank, Norkotah, Goldrush, and Silverton), two N rates (unfertilized and 308 kg N ha−1), and four growth stages (vegetative, tuber initiation, tuber bulking, and tuber maturation). The calibration and validation models had high predictive performance for leaf total N with R2 > 0.8 and RPD > 2. The model accuracy was affected by the total N contents in the leaf samples where the model underpredicted the samples with total leaf N contents greater than 6%.
可见-近红外(vis-NIR)光谱可以提供一种更快、成本效益高、用户友好的解决方案来监测叶片N状态，有可能克服当前技术的局限性。本研究的目的是开发和验证偏最小二乘回归(PLSR)，利用手持近端传感器产生的可见光-近红外光谱范围(350-2500 nm)估算新鲜和去皮土豆叶片的总氮含量。该模型使用2020年从美国威斯康星州汉考克农业研究站收集的数据建立，并使用2021年在四种不同条件下收集的样本进行验证。试验条件包括2个地点(Coloma和Hancock)， 4个马铃薯品种(Burbank、Norkotah、Goldrush和Silverton)， 2个氮肥水平(未施肥和308 kg N ha - 1)， 4个生长阶段(营养、块茎萌发、块茎膨大和块茎成熟)。校正和验证模型对叶片全氮的预测效果良好，R2 > 0.8, RPD > 2。模型精度受叶片总氮含量的影响，对于叶片总氮含量大于6%的样品，模型的预测偏低。
期刊介绍：
Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming.
There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to:
Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc.
Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc.
Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc.
Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc.
Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc.
Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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