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Yuxin Miao
Precision Agriculture Center, Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN 55108, USA

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Journal article
Published: 22 August 2021 in Remote Sensing
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Accurate high-resolution yield maps are essential for identifying spatial yield variability patterns, determining key factors influencing yield variability, and providing site-specific management insights in precision agriculture. Cultivar differences can significantly influence potato (Solanum tuberosum L.) tuber yield prediction using remote sensing technologies. The objective of this study was to improve potato yield prediction using unmanned aerial vehicle (UAV) remote sensing by incorporating cultivar information with machine learning methods. Small plot experiments involving different cultivars and nitrogen (N) rates were conducted in 2018 and 2019. UAV-based multi-spectral images were collected throughout the growing season. Machine learning models, i.e., random forest regression (RFR) and support vector regression (SVR), were used to combine different vegetation indices with cultivar information. It was found that UAV-based spectral data from the early growing season at the tuber initiation stage (late June) were more correlated with potato marketable yield than the spectral data from the later growing season at the tuber maturation stage. However, the best performing vegetation indices and the best timing for potato yield prediction varied with cultivars. The performance of the RFR and SVR models using only remote sensing data was unsatisfactory (R2 = 0.48–0.51 for validation) but was significantly improved when cultivar information was incorporated (R2 = 0.75–0.79 for validation). It is concluded that combining high spatial-resolution UAV images and cultivar information using machine learning algorithms can significantly improve potato yield prediction than methods without using cultivar information. More studies are needed to improve potato yield prediction using more detailed cultivar information, soil and landscape variables, and management information, as well as more advanced machine learning models.

ACS Style

Dan Li; Yuxin Miao; Sanjay K. Gupta; Carl J. Rosen; Fei Yuan; Chongyang Wang; Li Wang; Yanbo Huang. Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning. Remote Sensing 2021, 13, 3322 .

AMA Style

Dan Li, Yuxin Miao, Sanjay K. Gupta, Carl J. Rosen, Fei Yuan, Chongyang Wang, Li Wang, Yanbo Huang. Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning. Remote Sensing. 2021; 13 (16):3322.

Chicago/Turabian Style

Dan Li; Yuxin Miao; Sanjay K. Gupta; Carl J. Rosen; Fei Yuan; Chongyang Wang; Li Wang; Yanbo Huang. 2021. "Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning." Remote Sensing 13, no. 16: 3322.

Journal article
Published: 02 August 2021 in Agricultural and Forest Meteorology
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One challenge in precision nitrogen (N) management is the uncertainty in future weather conditions at the time of decision-making. Crop growth models require a full season of weather data to run yield simulation, and the unknown weather data may be forecasted or substituted by historical data. The objectives of this study were to (1) develop a model-based in-season N recommendation strategy for maize (Zea mays L.) using weather data fusion; and (2) evaluate this strategy in comparison with farmers’ N rate and regional optimal N rate in Northeast China. The CERES-Maize model was calibrated using data collected from field experiments conducted in 2015 and 2016, and validated using data from 2017. At two N decision dates - planting stage and V8 stage, the calibrated CERES-Maize model was used to predict grain yield and plant N uptake by fusing current and historical weather data. Using this approach, the model simulated grain yield and plant N uptake well (R2 = 0.85–0.89). Then, in-season economic optimal N rate (EONR) was determined according to responses of simulated marginal return (based on predicted grain yield) to N rate at planting and V8 stages. About 83% of predicted EONR fell within 20% of measured values. Applying the model-based in-season EONR had the potential to increase marginal return by 120–183 $ ha−1 and 0–83 $ ha−1 and N use efficiency by 8–71% and 1–38% without affecting grain yield over farmers’ N rate and regional optimal N rate, respectively. It is concluded that the CERES-Maize model is a valuable tool for simulating yield responses to N under different planting densities, soil types and weather conditions. The model-based in-season N recommendation strategy with weather data fusion can improve maize N use efficiency compared with current farmer practice and regional optimal management practice.

ACS Style

Xinbing Wang; Yuxin Miao; William D. Batchelor; Rui Dong; Krzysztof Kusnierek. Evaluating model-based strategies for in-season nitrogen management of maize using weather data fusion. Agricultural and Forest Meteorology 2021, 308-309, 108564 .

AMA Style

Xinbing Wang, Yuxin Miao, William D. Batchelor, Rui Dong, Krzysztof Kusnierek. Evaluating model-based strategies for in-season nitrogen management of maize using weather data fusion. Agricultural and Forest Meteorology. 2021; 308-309 ():108564.

Chicago/Turabian Style

Xinbing Wang; Yuxin Miao; William D. Batchelor; Rui Dong; Krzysztof Kusnierek. 2021. "Evaluating model-based strategies for in-season nitrogen management of maize using weather data fusion." Agricultural and Forest Meteorology 308-309, no. : 108564.

Conference paper
Published: 25 June 2021 in Precision agriculture ’21
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ACS Style

R. Dong; Y. Miao; X. Wang; P. Berry. 35. In-season prediction of maize lodging characteristics using an active crop sensor. Precision agriculture ’21 2021, 1 .

AMA Style

R. Dong, Y. Miao, X. Wang, P. Berry. 35. In-season prediction of maize lodging characteristics using an active crop sensor. Precision agriculture ’21. 2021; ():1.

Chicago/Turabian Style

R. Dong; Y. Miao; X. Wang; P. Berry. 2021. "35. In-season prediction of maize lodging characteristics using an active crop sensor." Precision agriculture ’21 , no. : 1.

Conference paper
Published: 25 June 2021 in Precision agriculture ’21
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ACS Style

H. Zha; J. Lu; Y. Li; Y. Miao; K. Kusnierek; W.D. Batchelor. 111. In-season calibration of the CERES-Rice model using proximal active canopy sensing data for yield prediction. Precision agriculture ’21 2021, 1 .

AMA Style

H. Zha, J. Lu, Y. Li, Y. Miao, K. Kusnierek, W.D. Batchelor. 111. In-season calibration of the CERES-Rice model using proximal active canopy sensing data for yield prediction. Precision agriculture ’21. 2021; ():1.

Chicago/Turabian Style

H. Zha; J. Lu; Y. Li; Y. Miao; K. Kusnierek; W.D. Batchelor. 2021. "111. In-season calibration of the CERES-Rice model using proximal active canopy sensing data for yield prediction." Precision agriculture ’21 , no. : 1.

Conference paper
Published: 25 June 2021 in Precision agriculture ’21
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ACS Style

X. Wang; Y. Miao; R. Dong; Z. Chen; K. Kusnierek. 54. Improving in-season nitrogen status diagnosis using a three-band active canopy sensor and ancillary data with machine learning. Precision agriculture ’21 2021, 1 .

AMA Style

X. Wang, Y. Miao, R. Dong, Z. Chen, K. Kusnierek. 54. Improving in-season nitrogen status diagnosis using a three-band active canopy sensor and ancillary data with machine learning. Precision agriculture ’21. 2021; ():1.

Chicago/Turabian Style

X. Wang; Y. Miao; R. Dong; Z. Chen; K. Kusnierek. 2021. "54. Improving in-season nitrogen status diagnosis using a three-band active canopy sensor and ancillary data with machine learning." Precision agriculture ’21 , no. : 1.

Conference paper
Published: 25 June 2021 in Precision agriculture ’21
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ACS Style

C. Cummings; Y. Miao; S. Kang; K. Stueve. 64. Developing a remote sensing and calibration strip-based in-season nitrogen management strategy for corn. Precision agriculture ’21 2021, 1 .

AMA Style

C. Cummings, Y. Miao, S. Kang, K. Stueve. 64. Developing a remote sensing and calibration strip-based in-season nitrogen management strategy for corn. Precision agriculture ’21. 2021; ():1.

Chicago/Turabian Style

C. Cummings; Y. Miao; S. Kang; K. Stueve. 2021. "64. Developing a remote sensing and calibration strip-based in-season nitrogen management strategy for corn." Precision agriculture ’21 , no. : 1.

Journal article
Published: 25 May 2021 in Field Crops Research
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Precision nitrogen (N) management requires rapid and real-time technologies for in-season crop N status diagnosis. The leaf fluorescence sensor Dualex 4 is an effective and promising tool to monitor crop N status. N nutrition index (NNI) is the most widely recognized diagnostic tool for accurate in-season diagnosis of crop N status. However, studies focusing on revealing the relationships between fluorescence sensing indices and NNI and assessing the N status of maize is limited. The objectives of this study were to (1) evaluate the potential of using Dualex 4 indices measured on three differently positioned leaves to estimate NNI across different stages; and (2) determine if the incorporation of environmental (weather) and management information can significantly improve the in-season N status prediction and diagnosis of maize. In 2016 and 2017, a total of four experiments with six N rates and three plant densities were conducted in two fields in Northeast China. Dualex sensor readings – Chlorophyll (Chl) and N balance index (NBI) – were collected from three differently positioned leaves at three growth stages. Some external factors including weather and management conditions were included for in-season N status assessment. The results indicated that the two Dualex indices (Chl and NBI) had strong relationships with NNI at different growth stages, and both stage-specific and across-stage models could estimate NNI based on their values acquired from differently positioned leaves. Nevertheless, the N diagnostic accuracies based on the estimated NNI by the Dualex indices were not satisfactory with Kappa values all lower than 0.40. Likewise, similar results were found in the multiple linear regression (MLR) models only based on the Dualex readings (MLRChl, MLRNBI and MLRChl+NBI). However, when weather and management variables were used together with Dualex sensor measurements in MLR analysis, the prediction of NNI (R2 = 0.81 to 0.85) and the accuracy of maize N status diagnosis (areal agreement = 0.79 and Kappa = 0.52 to 0.55) were significantly improved. More studies are needed to develop strategies combining more environmental and management variables with sensor data to further improve in-season N status diagnosis and N management and/or combine proximal with remote sensing for large-scale crop N nutritional status diagnosis and in-season site-specific N management.

ACS Style

Rui Dong; Yuxin Miao; Xinbing Wang; Zhichao Chen; Fei Yuan. Improving maize nitrogen nutrition index prediction using leaf fluorescence sensor combined with environmental and management variables. Field Crops Research 2021, 269, 108180 .

AMA Style

Rui Dong, Yuxin Miao, Xinbing Wang, Zhichao Chen, Fei Yuan. Improving maize nitrogen nutrition index prediction using leaf fluorescence sensor combined with environmental and management variables. Field Crops Research. 2021; 269 ():108180.

Chicago/Turabian Style

Rui Dong; Yuxin Miao; Xinbing Wang; Zhichao Chen; Fei Yuan. 2021. "Improving maize nitrogen nutrition index prediction using leaf fluorescence sensor combined with environmental and management variables." Field Crops Research 269, no. : 108180.

Journal article
Published: 24 January 2021 in Remote Sensing
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Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.

ACS Style

Cadan Cummings; Yuxin Miao; Gabriel Dias Paiao; Shujiang Kang; Fabián G. Fernández. Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System. Remote Sensing 2021, 13, 401 .

AMA Style

Cadan Cummings, Yuxin Miao, Gabriel Dias Paiao, Shujiang Kang, Fabián G. Fernández. Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System. Remote Sensing. 2021; 13 (3):401.

Chicago/Turabian Style

Cadan Cummings; Yuxin Miao; Gabriel Dias Paiao; Shujiang Kang; Fabián G. Fernández. 2021. "Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System." Remote Sensing 13, no. 3: 401.

Journal article
Published: 30 November 2020 in European Journal of Agronomy
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Reliable and efficient in-season nitrogen (N) status diagnosis and recommendation methods are crucially important for the success of crop precision N management (PNM). The accuracy of these methods has been found to be influenced by soil properties, weather conditions, and crop management practices. It is important to effectively incorporate these variables to improve in-season N management. Machine learning (ML) methods are promising due to their capability of processing different types of data and modeling both linear and non-linear relationships. The objectives of this study were to (1) determine the potential improvement of in-season prediction of corn N nutrition index (NNI) and grain yield by combining soil, weather and management data with active sensor data using random forest regression (RFR) as compared with Lasso linear regression (LR) using similar data and simple regression (SR) models only using crop sensor data; and (2) to develop a new in-season side-dress N fertilizer recommendation strategy at eighth to ninth leaf stage (V8-V9) of corn developement using the RFR model. Twelve site-year experiments examining corn N rates and planting densities were conducted in Northeast China. The GreenSeeker sensor data and corn NNI were collected at V8-V9 stage, and grain yield was determined at the harvest stage (R6). The soil information was obtained at planting and the weather data was measured throughout the growing season. The results indicated that corn NNI and grain yield were better predicted by combining soil, weather and management information with GreenSeeker sensor data using RFR model (R2 = 0.86 and 0.79) and LR model (R2 = 0.85 and 0.76) as compared with only using GreenSeeker sensor data (R2 = 0.66 and 0.62–63) based on the test dataset. An innovative in-season side-dress N recommendation strategy was developed using the RFR grain yield prediction model to simulate corn grain yield responses to a series of side-dress N rates at V8-V9 stage. Based on these response curves, site-, and year-specific optimum side-dress N rates can be determined. The scenario analysis results indicated that this RFR model-based in-season N recommendation strategy could recommend side-dress N rates similar to those based on measured agronomic optimum N rate (AONR) or economic optimum N rate (EONR), with root mean square error (RMSE) of 17 kg ha−1 and relative error (RE) of 14–15 %. It is concluded that combining soil, weather and management information with crop sensor data using RFR can significantly improve both in-season corn NNI and grain yield prediction and N management, compared with the approach based only on crop sensor data. More studies are needed to further improve and evaluate this approach under diverse on-farm conditions.

ACS Style

Xinbing Wang; Yuxin Miao; Rui Dong; Hainie Zha; Tingting Xia; Zhichao Chen; Krzysztof Kusnierek; Guohua Mi; Hong Sun; Minzan Li. Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn. European Journal of Agronomy 2020, 123, 126193 .

AMA Style

Xinbing Wang, Yuxin Miao, Rui Dong, Hainie Zha, Tingting Xia, Zhichao Chen, Krzysztof Kusnierek, Guohua Mi, Hong Sun, Minzan Li. Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn. European Journal of Agronomy. 2020; 123 ():126193.

Chicago/Turabian Style

Xinbing Wang; Yuxin Miao; Rui Dong; Hainie Zha; Tingting Xia; Zhichao Chen; Krzysztof Kusnierek; Guohua Mi; Hong Sun; Minzan Li. 2020. "Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn." European Journal of Agronomy 123, no. : 126193.

Journal article
Published: 12 November 2020 in Agronomy
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Small-scale farms represent about 80% of the farming area of China, in a context where they need to produce economic and environmentally sustainable food. The objective of this work was to define management zone (MZs) for a village by comparing the use of crop yield proxies derived from historical satellite images with soil information derived from remote sensing, and the integration of these two data sources. The village chosen for the study was Wangzhuang village in Quzhou County in the North China Plain (NCP) (30°51′55″ N; 115°02′06″ E). The village was comprised of 540 fields covering approximately 177 ha. The subdivision of the village into three or four zones was considered to be the most practical for the NCP villages because it is easier to manage many fields within a few zones rather than individually in situations where low mechanization is the norm. Management zones defined using Landsat satellite data for estimation of the Green Normalized Vegetation Index (GNDVI) was a reasonable predictor (up to 45%) of measured variation in soil nitrogen (N) and organic carbon (OC). The approach used in this study works reasonably well with minimum data but, in order to improve crop management (e.g., sowing dates, fertilization), a simple decision support system (DSS) should be developed in order to integrate MZs and agronomic prescriptions.

ACS Style

Davide Cammarano; Hainie Zha; Lucy Wilson; Yue Li; William D. Batchelor; Yuxin Miao. A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems. Agronomy 2020, 10, 1767 .

AMA Style

Davide Cammarano, Hainie Zha, Lucy Wilson, Yue Li, William D. Batchelor, Yuxin Miao. A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems. Agronomy. 2020; 10 (11):1767.

Chicago/Turabian Style

Davide Cammarano; Hainie Zha; Lucy Wilson; Yue Li; William D. Batchelor; Yuxin Miao. 2020. "A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems." Agronomy 10, no. 11: 1767.

Journal article
Published: 21 August 2020 in Agronomy
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The dynamic interactions between soil, weather and crop management have considerable influences on crop yield within a region, and should be considered in optimizing nitrogen (N) management. The objectives of this study were to determine the influence of soil type, weather conditions and planting density on economic optimal N rate (EONR), and to evaluate the potential benefits of site-specific N management strategies for maize production. The experiments were conducted in two soil types (black and aeolian sandy soils) from 2015 to 2017, involving different N rates (0 to 300 kg ha−1) with three planting densities (55,000, 70,000, and 85,000 plant ha−1) in Northeast China. The results showed that the average EONR was higher in black soil (265 kg ha−1) than in aeolian sandy soil (186 kg ha−1). Conversely, EONR showed higher variability in aeolian sandy soil (coefficient of variation (CV) = 30%) than in black soil (CV = 10%) across different weather conditions and planting densities. Compared with farmer N rate (FNR), applying soil-specific EONR (SS-EONR), soil- and year-specific EONR (SYS-EONR) and soil-, year-, and planting density-specific EONR (SYDS-EONR) would significantly reduce N rate by 25%, 30% and 38%, increase net return (NR) by 155 $ ha−1, 176 $ ha−1, and 163 $ ha−1, and improve N use efficiency (NUE) by 37–42%, 52%, and 67–71% across site-years, respectively. Compared with regional optimal N rate (RONR), applying SS-EONR, SYS-EONR and SYDS-EONR would significantly reduce N application rate by 6%, 12%, and 22%, while increasing NUE by 7–8%, 16–19% and 28–34% without significantly affecting yield or NR, respectively. It is concluded that soil-specific N management has the potential to improve maize NUE compared with both farmer practice and regional optimal N management in Northeast China, especially when each year’s weather condition and planting density information is also considered. More studies are needed to develop practical in-season soil (site)-specific N management strategies using crop sensing and modeling technologies to better account for soil, weather and planting density variation under diverse on-farm conditions.

ACS Style

Xinbing Wang; Yuxin Miao; Rui Dong; Zhichao Chen; Krzysztof Kusnierek; Guohua Mi; David J. Mulla. Economic Optimal Nitrogen Rate Variability of Maize in Response to Soil and Weather Conditions: Implications for Site-Specific Nitrogen Management. Agronomy 2020, 10, 1237 .

AMA Style

Xinbing Wang, Yuxin Miao, Rui Dong, Zhichao Chen, Krzysztof Kusnierek, Guohua Mi, David J. Mulla. Economic Optimal Nitrogen Rate Variability of Maize in Response to Soil and Weather Conditions: Implications for Site-Specific Nitrogen Management. Agronomy. 2020; 10 (9):1237.

Chicago/Turabian Style

Xinbing Wang; Yuxin Miao; Rui Dong; Zhichao Chen; Krzysztof Kusnierek; Guohua Mi; David J. Mulla. 2020. "Economic Optimal Nitrogen Rate Variability of Maize in Response to Soil and Weather Conditions: Implications for Site-Specific Nitrogen Management." Agronomy 10, no. 9: 1237.

Journal article
Published: 02 May 2020 in Remote Sensing
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RapidSCAN is a portable active canopy sensor with red, red-edge, and near infrared spectral bands. The objective of this study is to develop and evaluate a RapidSCAN sensor-based precision nitrogen (N) management (PNM) strategy for high-yielding rice in Northeast China. Six rice N rate experiments were conducted from 2014 to 2016 at Jiansanjiang Experiment Station of China Agricultural University in Northeast China. The results indicated that the sensor performed well for estimating rice yield potential (YP0) and yield response to additional N application (RIHarvest) at the stem elongation stage using normalized difference vegetation index (NDVI) (R2 = 0.60–0.77 and relative error (REr) = 6.2–8.0%) and at the heading stage using normalized difference red edge (NDRE) (R2 = 0.70–0.82 and REr = 7.3–8.7%). A new RapidSCAN sensor-based PNM strategy was developed that would make N recommendations at both stem elongation and heading growth stages, in contrast to previously developed strategy making N recommendation only at the stem elongation stage. This new PNM strategy could save 24% N fertilizers, and increase N use efficiencies by 29–35% as compared to Farmer N Management, without significantly affecting the rice grain yield and economic returns. Compared with regional optimum N management, the new PNM strategy increased 4% grain yield, 3–10% N use efficiencies and 148 $ ha−1 economic returns across years and varieties. It is concluded that the new RapidSCAN sensor-based PNM strategy with two in-season N recommendations using NDVI and NDRE is suitable for guiding in-season N management in high-yield rice management systems. Future studies are needed to evaluate this RapidSCAN sensor-based PNM strategy under diverse on-farm conditions, as well as to integrate it into high-yield rice management systems for food security and sustainable development.

ACS Style

Junjun Lu; Yuxin Miao; Wei Shi; Jingxin Li; Xiaoyi Hu; Zhichao Chen; Xinbing Wang; Krzysztof Kusnierek. Developing a Proximal Active Canopy Sensor-based Precision Nitrogen Management Strategy for High-Yielding Rice. Remote Sensing 2020, 12, 1440 .

AMA Style

Junjun Lu, Yuxin Miao, Wei Shi, Jingxin Li, Xiaoyi Hu, Zhichao Chen, Xinbing Wang, Krzysztof Kusnierek. Developing a Proximal Active Canopy Sensor-based Precision Nitrogen Management Strategy for High-Yielding Rice. Remote Sensing. 2020; 12 (9):1440.

Chicago/Turabian Style

Junjun Lu; Yuxin Miao; Wei Shi; Jingxin Li; Xiaoyi Hu; Zhichao Chen; Xinbing Wang; Krzysztof Kusnierek. 2020. "Developing a Proximal Active Canopy Sensor-based Precision Nitrogen Management Strategy for High-Yielding Rice." Remote Sensing 12, no. 9: 1440.

Journal article
Published: 02 April 2020 in Remote Sensing
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Nitrogen (N) is one of the most essential nutrients that can significantly affect crop grain yield and quality. The implementation of proximal and remote sensing technologies in precision agriculture has provided new opportunities for non-destructive and real-time diagnosis of crop N status and precision N management. Notably, leaf fluorescence sensors have shown high potential in the accurate estimation of plant N status. However, most studies using leaf fluorescence sensors have mainly focused on the estimation of leaf N concentration (LNC) rather than plant N concentration (PNC). The objectives of this study were to (1) determine the relationship of maize (Zea mays L.) LNC and PNC, (2) evaluate the main factors influencing the variations of leaf fluorescence sensor parameters, and (3) establish a general model to estimate PNC directly across growth stages. A leaf fluorescence sensor, Dualex 4, was used to test maize leaves with three different positions across four growth stages in two fields with different soil types, planting densities, and N application rates in Northeast China in 2016 and 2017. The results indicated that the total leaf N concentration (TLNC) and PNC had a strong correlation (R2 = 0.91 to 0.98) with the single leaf N concentration (SLNC). The TLNC and PNC were affected by maize growth stage and N application rate but not the soil type. When used in combination with the days after sowing (DAS) parameter, modified Dualex 4 indices showed strong relationships with TLNC and PNC across growth stages. Both modified chlorophyll concentration (mChl) and modified N balance index (mNBI) were reliable predictors of PNC. Good results could be achieved by using information obtained only from the newly fully expanded leaves before the tasseling stage (VT) and the leaves above panicle at the VT stage to estimate PNC. It is concluded that when used together with DAS, the leaf fluorescence sensor (Dualex 4) can be used to reliably estimate maize PNC across growth stages.

ACS Style

Rui Dong; Yuxin Miao; Xinbing Wang; Zhichao Chen; Fei Yuan; Weina Zhang; Haigang Li. Estimating Plant Nitrogen Concentration of Maize Using a Leaf Fluorescence Sensor across Growth Stages. Remote Sensing 2020, 12, 1139 .

AMA Style

Rui Dong, Yuxin Miao, Xinbing Wang, Zhichao Chen, Fei Yuan, Weina Zhang, Haigang Li. Estimating Plant Nitrogen Concentration of Maize Using a Leaf Fluorescence Sensor across Growth Stages. Remote Sensing. 2020; 12 (7):1139.

Chicago/Turabian Style

Rui Dong; Yuxin Miao; Xinbing Wang; Zhichao Chen; Fei Yuan; Weina Zhang; Haigang Li. 2020. "Estimating Plant Nitrogen Concentration of Maize Using a Leaf Fluorescence Sensor across Growth Stages." Remote Sensing 12, no. 7: 1139.

Journal article
Published: 08 January 2020 in Remote Sensing
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Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were

ACS Style

Hainie Zha; Yuxin Miao; Tiantian Wang; Yue Li; Jing Zhang; Weichao Sun; Zhengqi Feng; Krzysztof Kusnierek. Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning. Remote Sensing 2020, 12, 215 .

AMA Style

Hainie Zha, Yuxin Miao, Tiantian Wang, Yue Li, Jing Zhang, Weichao Sun, Zhengqi Feng, Krzysztof Kusnierek. Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning. Remote Sensing. 2020; 12 (2):215.

Chicago/Turabian Style

Hainie Zha; Yuxin Miao; Tiantian Wang; Yue Li; Jing Zhang; Weichao Sun; Zhengqi Feng; Krzysztof Kusnierek. 2020. "Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning." Remote Sensing 12, no. 2: 215.

Journal article
Published: 09 October 2019 in Agronomy
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Improving nitrogen (N) management of small-scale farming systems in developing countries is crucially important for food security and sustainable development of world agriculture, but it is also very challenging. The N Nutrition Index (NNI) is a reliable indicator for crop N status, and there is an urgent need to develop an effective method to non-destructively estimate crop NNI in different smallholder farmer fields to guide in-season N management. The eBee fixed-wing unmanned aerial vehicle (UAV)-based remote sensing system, a ready-to-deploy aircraft with a Parrot Sequoia+ multispectral camera onboard, has been used for applications in precision agriculture. The objectives of this study were to (i) determine the potential of using fixed-wing UAV-based multispectral remote sensing for non-destructive estimation of winter wheat NNI in different smallholder farmer fields across the study village in the North China Plain (NCP) and (ii) develop a practical strategy for village-scale winter wheat N status diagnosis in small scale farming systems. Four plot experiments were conducted within farmer fields in 2016 and 2017 in a village of Laoling County, Shandong Province in the NCP for evaluation of a published critical N dilution curve and for serving as reference plots. UAV remote sensing images were collected from all the fields across the village in 2017 and 2018. About 150 plant samples were collected from farmer fields and plot experiments each year for ground truthing. Two indirect and two direct approaches were evaluated for estimating NNI using vegetation indices (VIs). To facilitate practical applications, the performance of three commonly used normalized difference VIs were compared with the top performing VIs selected from 59 tested indices. The most practical and stable method was using VIs to calculate N sufficiency index (NSI) and then to estimate NNI non-destructively (R2 = 0.53–0.56). Using NSI thresholds to diagnose N status directly was quite stable, with a 57–59% diagnostic accuracy rate. This strategy is practical and least affected by the choice of VIs across fields, varieties, and years. This study demonstrates that fixed-wing UAV–based remote sensing is a promising technology for in-season diagnosis of winter wheat N status in smallholder farmer fields at village scale. The considerable variability in local soil conditions and crop management practices influenced the overall accuracy of N diagnosis, so more studies are needed to further validate and optimize the reported strategy and consecutively develop practical UAV remote sensing–based in-season N recommendation methods.

ACS Style

Zhichao Chen; Yuxin Miao; Junjun Lu; Lan Zhou; Yue Li; Hongyan Zhang; Weidong Lou; Zheng Zhang; Krzysztof Kusnierek; Changhua Liu. In-Season Diagnosis of Winter Wheat Nitrogen Status in Smallholder Farmer Fields Across a Village Using Unmanned Aerial Vehicle-Based Remote Sensing. Agronomy 2019, 9, 619 .

AMA Style

Zhichao Chen, Yuxin Miao, Junjun Lu, Lan Zhou, Yue Li, Hongyan Zhang, Weidong Lou, Zheng Zhang, Krzysztof Kusnierek, Changhua Liu. In-Season Diagnosis of Winter Wheat Nitrogen Status in Smallholder Farmer Fields Across a Village Using Unmanned Aerial Vehicle-Based Remote Sensing. Agronomy. 2019; 9 (10):619.

Chicago/Turabian Style

Zhichao Chen; Yuxin Miao; Junjun Lu; Lan Zhou; Yue Li; Hongyan Zhang; Weidong Lou; Zheng Zhang; Krzysztof Kusnierek; Changhua Liu. 2019. "In-Season Diagnosis of Winter Wheat Nitrogen Status in Smallholder Farmer Fields Across a Village Using Unmanned Aerial Vehicle-Based Remote Sensing." Agronomy 9, no. 10: 619.

Journal article
Published: 08 August 2019 in Remote Sensing
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Precision nitrogen (N) management requires an accurate and timely in-season assessment of crop N status. The proximal fluorescence sensor Multiplex®3 is a promising tool for monitoring crop N status. It performs a non-destructive estimation of plant chlorophyll, flavonol, and anthocyanin contents, which are related to plant N status. The objective of this study was to evaluate the potential of proximal fluorescence sensing for N status estimation at different growth stages for rice in cold regions. In 2012 and 2013, paddy rice field experiments with five N supply rates and two varieties were conducted in northeast China. Field samples and fluorescence data were collected in the leaf scale (LS), on-the-go (OG), and above the canopy (AC) modes using Multiplex®3 at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. The relationships between the Multiplex indices or normalized N sufficient indices (NSI) and five N status indicators (above-ground biomass (AGB), leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI)) were evaluated. Results showed that Multiplex measurements taken using the OG mode were more sensitive to rice N status than those made in the other two modes in this study. Most of the measured fluorescence indices, especially the N balance index (NBI), simple fluorescence ratios (SFR), blue–green to far-red fluorescence ratio (BRR_FRF), and flavonol (FLAV) were highly sensitive to N status. Strong relationships between these fluorescence indices and N indicators, especially the LNC, PNC, and NNI were revealed, with coefficients of determination (R2) ranging from 0.40 to 0.78. The N diagnostic results indicated that the normalized N sufficiency index based on NBI under red illumination (NBI_RNSI) and FLAV achieved the highest diagnostic accuracy rate (90%) at the SE and HE stages, respectively, while NBI_RNSI showed the highest diagnostic consistency across growth stages. The study concluded that the Multiplex sensor could be used to reliably estimate N nutritional status for rice in cold regions, especially for the estimation of LNC, PNC, and NNI. The normalized N sufficiency indices based on the Multiplex indices could further improve the accuracy of N nutrition diagnosis by reducing the influences of inter-annual variations and different varieties, as compared with the original Multiplex indices.

ACS Style

Shanyu Huang; Yuxin Miao; Fei Yuan; Qiang Cao; Huichun Ye; Victoria I.S. Lenz-Wiedemann; Georg Bareth. In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages. Remote Sensing 2019, 11, 1847 .

AMA Style

Shanyu Huang, Yuxin Miao, Fei Yuan, Qiang Cao, Huichun Ye, Victoria I.S. Lenz-Wiedemann, Georg Bareth. In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages. Remote Sensing. 2019; 11 (16):1847.

Chicago/Turabian Style

Shanyu Huang; Yuxin Miao; Fei Yuan; Qiang Cao; Huichun Ye; Victoria I.S. Lenz-Wiedemann; Georg Bareth. 2019. "In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages." Remote Sensing 11, no. 16: 1847.

Conference paper
Published: 08 July 2019 in Precision agriculture ’19
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ACS Style

X. Wang; Y. Miao; R. Dong; Y. Guan; D.J. Mulla. Evaluating the potential benefits of field-specific nitrogen management of spring maize in northeast China. Precision agriculture ’19 2019, 1 .

AMA Style

X. Wang, Y. Miao, R. Dong, Y. Guan, D.J. Mulla. Evaluating the potential benefits of field-specific nitrogen management of spring maize in northeast China. Precision agriculture ’19. 2019; ():1.

Chicago/Turabian Style

X. Wang; Y. Miao; R. Dong; Y. Guan; D.J. Mulla. 2019. "Evaluating the potential benefits of field-specific nitrogen management of spring maize in northeast China." Precision agriculture ’19 , no. : 1.

Journal article
Published: 29 January 2019 in Sustainability
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Precision nitrogen (N) management (PNM) strategies are urgently needed for the sustainability of rain-fed maize (Zea mays L.) production in Northeast China. The objective of this study was to develop an active canopy sensor (ACS)-based PNM strategy for rain-fed maize through improving in-season prediction of yield potential (YP0), response index to side-dress N based on harvested yield (RIHarvest), and side-dress N agronomic efficiency (AENS). Field experiments involving six N rate treatments and three planting densities were conducted in three growing seasons (2015–2017) in two different soil types. A hand-held GreenSeeker sensor was used at V8-9 growth stage to collect normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). The results indicated that NDVI or RVI combined with relative plant height (NDVI*RH or RVI*RH) were more strongly related to YP0 (R2 = 0.44–0.78) than only using NDVI or RVI (R2 = 0.26–0.68). The improved N fertilizer optimization algorithm (INFOA) using in-season predicted AENS optimized N rates better than the N fertilizer optimization algorithm (NFOA) using average constant AENS. The INFOA-based PNM strategies could increase marginal returns by 212 $ ha−1 and 70 $ ha−1, reduce N surplus by 65% and 62%, and improve N use efficiency (NUE) by 4%–40% and 11%–65% compared with farmer’s typical N management in the black and aeolian sandy soils, respectively. It is concluded that the ACS-based PNM strategies have the potential to significantly improve profitability and sustainability of maize production in Northeast China. More studies are needed to further improve N management strategies using more advanced sensing technologies and incorporating weather and soil information.

ACS Style

Xinbing Wang; Yuxin Miao; Rui Dong; Zhichao Chen; Yanjie Guan; Xuezhi Yue; Zheng Fang; David Mulla. Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China. Sustainability 2019, 11, 706 .

AMA Style

Xinbing Wang, Yuxin Miao, Rui Dong, Zhichao Chen, Yanjie Guan, Xuezhi Yue, Zheng Fang, David Mulla. Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China. Sustainability. 2019; 11 (3):706.

Chicago/Turabian Style

Xinbing Wang; Yuxin Miao; Rui Dong; Zhichao Chen; Yanjie Guan; Xuezhi Yue; Zheng Fang; David Mulla. 2019. "Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China." Sustainability 11, no. 3: 706.

Journal article
Published: 15 November 2018 in Agronomy
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Efficient use of nitrogen (N) fertilizer is critically important for China’s food security and sustainable development. Crop models have been widely used to analyze yield variability, assist in N prescriptions, and determine optimum N rates. The objectives of this study were to use the CERES-Rice model to simulate the N response of different high-latitude, adapted flooded rice varieties to different types of weather seasons, and to explore different optimum rice N management strategies with the combinations of rice varieties and types of weather seasons. Field experiments conducted for five N rates and three varieties in Northeast China during 2011–2016 were used to calibrate and evaluate the CERES-Rice model. Historical weather data (1960–2014) were classified into three weather types (cool/normal/warm) based on cumulative growing degree days during the normal growing season for rice. After calibrating the CERES-Rice model for three varieties and five N rates, the model gave good simulations for evaluation seasons for top weight (R2 ≥ 0.96), leaf area index (R2 ≥ 0.64), yield (R2 ≥ 0.71), and plant N uptake (R2 ≥ 0.83). The simulated optimum N rates for the combinations of varieties and weather types ranged from 91 to 119 kg N ha−1 over 55 seasons of weather data and were in agreement with the reported values of the region. Five different N management strategies were evaluated based on farmer practice, regional optimum N rates, and optimum N rates simulated for different combinations of varieties and weather season types over 20 seasons of weather data. The simulated optimum N rate, marginal net return, and N partial factor productivity were sensitive to both variety and type of weather year. Based on the simulations, climate warming would favor the selection of the 12-leaf variety, Longjing 21, which would produce higher yield and marginal returns than the 11-leaf varieties under all the management strategies evaluated. The 12-leaf variety with a longer growing season and higher yield potential would require higher N rates than the 11-leaf varieties. In summary, under warm weather conditions, all the rice varieties would produce higher yield, and thus require higher rates of N fertilizers. Based on simulation results using the past 20 years of weather data, variety-specific N management was a practical strategy to improve N management and N partial factor productivity compared with farmer practice and regional optimum N management in the study region. The CERES-Rice crop growth model can be a useful tool to help farmers select suitable precision N management strategies to improve N-use efficiency and economic returns.

ACS Style

Jing Zhang; Yuxin Miao; William D. Batchelor; Junjun Lu; Hongye Wang; Shujiang Kang. Improving High-Latitude Rice Nitrogen Management with the CERES-Rice Crop Model. Agronomy 2018, 8, 263 .

AMA Style

Jing Zhang, Yuxin Miao, William D. Batchelor, Junjun Lu, Hongye Wang, Shujiang Kang. Improving High-Latitude Rice Nitrogen Management with the CERES-Rice Crop Model. Agronomy. 2018; 8 (11):263.

Chicago/Turabian Style

Jing Zhang; Yuxin Miao; William D. Batchelor; Junjun Lu; Hongye Wang; Shujiang Kang. 2018. "Improving High-Latitude Rice Nitrogen Management with the CERES-Rice Crop Model." Agronomy 8, no. 11: 263.

Journal article
Published: 01 October 2018 in The Crop Journal
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Accurate leaf area simulation is critical for the performance of crop growth models. Area of fully expanded individual leaves of maize hybrids released before 1995 (defined as old hybrids) has been simulated using a bell-shaped function (BSF) and the relationship between its parameters and total leaf number (TLNO). However, modern high-yielding maize hybrids show different canopy architectures. The function parameters calibrated for old hybrids will not accurately represent modern hybrids. In this study, we evaluated these functions using a dataset including old and modern hybrids that have been widely planted in China in recent years. Maximum individual leaf area (Y0) and corresponding leaf position (X0) were not predicted well by TLNO (R2 = 0.56 and R2 = 0.70) for modern hybrids. Using recalibrated shape parameters a and b with values of Y0 and X0 for modern hybrids, the BSF accurately predicted individual leaf area (R2 = 0.95–0.99) and total leaf area of modern hybrids (R2 = 0.98). The results show that the BSF is still a robust way to predict the fully expanded leaf area of maize when parameters a and b are modified and Y0 and X0 are fitted. Breeding programs have led to increases in TLNO of maize but have not altered Y0 and X0, reducing the correlation between Y0, X0, and TLNO. For modern hybrids, the values of Y0 and X0 are hybrid-specific. Modern hybrids tend to have less-negative values of parameter a and more-positive values of parameter b in the leaf profile. Growth conditions, such as plant density and environmental conditions, also affect the fully expanded leaf area but were not considered in the original published equations. Thus, further research is needed to accurately estimate values of Y0 and X0 of individual modern hybrids to improve simulation of maize leaf area in crop growth models.

ACS Style

Xiaoxing Zhen; Hui Shao; Weina Zhang; Weige Huo; William David Batchelor; Peng Hou; Enli Wang; Guohua Mi; Yuxin Miao; Haigang Li; Fusuo Zhang. Testing a bell-shaped function for estimation of fully expanded leaf area in modern maize under potential production conditions. The Crop Journal 2018, 6, 527 -537.

AMA Style

Xiaoxing Zhen, Hui Shao, Weina Zhang, Weige Huo, William David Batchelor, Peng Hou, Enli Wang, Guohua Mi, Yuxin Miao, Haigang Li, Fusuo Zhang. Testing a bell-shaped function for estimation of fully expanded leaf area in modern maize under potential production conditions. The Crop Journal. 2018; 6 (5):527-537.

Chicago/Turabian Style

Xiaoxing Zhen; Hui Shao; Weina Zhang; Weige Huo; William David Batchelor; Peng Hou; Enli Wang; Guohua Mi; Yuxin Miao; Haigang Li; Fusuo Zhang. 2018. "Testing a bell-shaped function for estimation of fully expanded leaf area in modern maize under potential production conditions." The Crop Journal 6, no. 5: 527-537.