Developing an Active Canopy Sensor-Based Integrated Precision Rice Management System for Improving Grain Yield and Quality, Nitrogen Use Efficiency, and Lodging Resistance

Abstract

Active crop sensor-based precision nitrogen (N) management can significantly improve N use efficiency but generally does not increase crop yield. The objective of this research was to develop and evaluate an active canopy sensor-based precision rice management system in terms of grain yield and quality, N use efficiency, and lodging resistance as compared with farmer practice, regional optimum rice management system recommended by the extension service, and a chlorophyll meter-based precision rice management system. Two field experiments were conducted from 2011 to 2013 at Jiansanjiang Experiment Station of China Agricultural University in Heilongjiang, China, involving four rice management systems and two varieties (Kongyu 131 and Longjing 21). The results indicated that the canopy sensor-based precision rice management system significantly increased rice grain yield (by 9.4–13.5%) over the farmer practice while improving N use efficiency, grain quality, and lodging resistance. Compared with the already optimized regional optimum rice management system, in the cool weather year of 2011, the developed system decreased the N rate applied in Kongyu 131 by 12% and improved N use efficiency without inducing yield loss. In the warm weather year of 2013, the canopy sensor-based management system recommended an 8% higher N rate to be applied in Longjing 21 than the regional optimum rice management, which improved rice panicle number per unit area and eventually led to increased grain yield by over 10% and improved N use efficiency. More studies are needed to further test the developed active canopy sensor-based precision rice management system under more diverse on-farm conditions and further improve it using unmanned aerial vehicle or satellite remote sensing technologies for large-scale applications.

Analysis of the Effects of Population Structure and Environmental Factors on Rice Nitrogen Nutrition Index and Yield Based on Machine Learning

“…Moreover, in the third stage, the NNI and yield prediction effect of the ensemble learning model was significantly improved compared to that of the other two stages, and the R 2 of LGBM and RFR in yield reached 0.901 and 0.908, respectively, while the R 2 of LGBM and RFR in NNI reached 0.921 and 0.874, respectively. This is consistent with most research findings [5,12,28].…”

“…The determination of the N and density recommendation for different varieties of rice is challenging because of the interactions of crop variety [58], environmental weather conditions [5], soil properties [34], and field management techniques [35]. Most of the data used for the prediction by ML models in the past were obtained by sensors [12,25,28], and owing to the difficulty of traditional data collection, the model prediction of yield and the NNI has not been combined with characteristic value data, such as the population structure or plant shape characteristics. In addition, most crop growth model-based approaches to predict optimal N rate used average N rates across many years or sites, but are not site-specific [59].…”

“…In particular, excessive N fertilizer application not only reduces N recovery efficiency, but also causes environmental pollution, such as runoff, denitrification, leaching, and volatilization, which increase N loss [7]; it also causes plant lodging and yield loss. Therefore, finding the appropriate N fertilizer dosage for different varieties is one of the main ways to achieve a high yield and high efficiency of rice [12].…”

“…Therefore, it is important to effectively combine the above factors to improve the current crop NNI and yield prediction and N recommendations [36]. Most of the data used by ML models in the past were obtained by sensors [12,25,28], and were not combined with characteristic value data such as the population structure, plant type traits, field meteorological data, and N fertilizer and density for prediction of the yield and NNI. Therefore, there is an urgent need to develop a PNM strategy combined with plant population structure and plant shape characteristics for sustainable rice production in northeast China.…”