外文摘要：Plant nitrogen (N) is one of the key factors for its growth and yield. Timely assessment of plant N at a spatio-temporal scale enables its precision management in the field scale with better N use efficiency. Airborne imaging spectroscopy is a potential technique for non-invasive near real-time rapid assessment of plant N on a field scale. The present study attempted to assess plant N in a wheat field with three different irrigation levels (I1-I3) along with five nitrogen treatments (N0-N4) using a UAV hyperspectral imager with a spectral range of 400 to 1000 nm. A total of 61 vegetative indices were evaluated to find suitable indices for estimating plant N. A hybrid method of R-Square (R2) and Variable Importance Projection (VIP) followed by Variance Inflation Factor was used to limit the best suitable N-sensitive 13 spectral indices. The selected indices were used as feature vectors in the Artificial Neural Network algorithm to model and generate a spatial map of plant N in the experimental wheat field. The model resulted in R2 values of 0.97, 0.84, and 0.86 for training, validation, and testing respectively for plant N assessment.
 
外文关键词：UAV;machine learning;ANN;Leaf nitrogen assessment;Vegetative indices
作者：Meena, Mahesh C;Dhakar, Rajkumar;Mukherjee, Joydeep;Kumar, Sudhir;Chinnusamy, Viswanathan;Sahoo, Rabi N;Rejith, R G;Gakhar, Shalini;Ranjan, Rajeev;Dey, Abir;Meena, Abhishek;Daas, Anchal;Babu, Subhash;Upadhyay, Pravin K;Sekhawat, Kapila;Kumar, Mahesh;Khanna, Manoj
作者单位：ICAR IARI;ICAR Indian Agr Res Inst IARI
期刊名称：PRECISION AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：25（2）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台