Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn

Wang, Xinbing; Miao, Yuxin; Dong, Rui; Zha, Hainie; Xia, Tingting; Chen, Zhichao; Kuśnierek, Krzysztof; Mi, Guohua; Sun, Hong; Li, Minzan

doi:10.1016/j.eja.2020.126193

Cited by 54 publications

(45 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our analysis indicated that a more sophisticated accounting for N budget and optimizing N fertilizer use in cultivation is imperative to meet the global rise in competing demands for crop productivity and environmental protection (Zhang et al, 2013). This finding also reinforces the development of precision agriculture approaches to match nitrogen fertilizer inputs to spatial variability in fertility in crop fields (Dahal et al, 2020; Shaw et al, 2016; Wang et al, 2021).…”

Section: Discussionsupporting

confidence: 69%

Soil extracellular oxidases mediated nitrogen fertilization effects on soil organic carbon sequestration in bioenergy croplands

et al. 2021

View full text Add to dashboard Cite

Nitrogen (N) fertilization significantly affects soil extracellular oxidases, agents responsible for decomposition of slow turnover and recalcitrant soil organic carbon (SOC; e.g., lignin), and consequently influences soil carbon sequestration capacity. However, it remains unclear how soil oxidases mediate SOC sequestration under N fertilization, and whether these effects co‐vary with plant type (e.g., bioenergy crop species). Using a spatially explicit design and intensive soil sampling strategy under three fertilization treatments in switchgrass (SG: Panicum virgatum L.) and gamagrass (GG: Tripsacum dactyloides L.) croplands, we quantified the activities of polyphenolic oxidase (PHO), peroxidase (PER), and their sum associated with recalcitrant C acquisition (OX). The fertilization treatments included no N fertilizer input (NN), low N input (LN: 84 kg N ha−1 year−1 in urea), and high N input (HN: 168 kg N ha−1 year−1 in urea). Besides correlations between soil oxidases and SOC (formerly published), both descriptive and geostatistical approaches were applied to evaluate the effects of N fertilization and crop type on soil oxidases activities and their spatial distributions. Results showed significantly negative correlations between soil oxidase activities and SOC across all treatments. The negative relationship of soil oxidases and SOC was also evident under N fertilization. First, LN significantly depressed oxidases in both mean activities and spatial heterogeneity, which corresponded to increased SOC in SG (though by 5.4%). LN slightly influenced oxidases activities and their spatial heterogeneity, consistent with insignificant changes of SOC in GG. Second, HN showed trends of decrease in soil oxidase activities, which aligned with the significantly enhanced SOC in both croplands. Overall, this study demonstrated that soil oxidase activities acted as sensitive and negative mediators of SOC sequestration in bioenergy croplands and optimizing fertilizer use particularly in switchgrass cropland can improve for both carbon sequestration and environmental benefit.

show abstract

Section: Discussionsupporting

confidence: 69%

Soil extracellular oxidases mediated nitrogen fertilization effects on soil organic carbon sequestration in bioenergy croplands

et al. 2021

View full text Add to dashboard Cite

show abstract

“…However, the location of the reference plots, strips, or areas can influence the diagnosis results or N recommendations, and no consensus has been reached about where to put the references and how many will be needed [24,25]. Another approach is to incorporate soil and weather information to improve crop sensor-based N recommendation or the prediction of crop variables [26][27][28][29][30][31]. A recent study found that incorporating soil and weather information improved the performance of a crop sensorbased N recommendation algorithm developed by the University of Missouri and tested across the US Midwest, reducing the difference between sensor-recommended N rates and economic optimum N rate by ~25 kg ha −1 [26].…”

Section: Introductionmentioning

confidence: 99%

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Miao

Ransom

et al. 2022

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 < 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions.

show abstract

“…In article [ 8 ], the input data of soil and crop properties were used to predict yield. ML algorithms can be used to predict alfalfa yield [ 9 ], maize yield and nitrate loss [ 10 ], and to assess the seasonal nitrogen status in maize [ 11 , 12 ], carrot yield mapping [ 13 ], soil suitability for growing individual crops [ 14 ] and peach tree nutrients at the local level [ 15 ].…”

Section: State-of-the-artmentioning

confidence: 99%

Prediction of Pest Insect Appearance Using Sensors and Machine Learning

Marković

Vujicic

Tanasković

et al. 2021

Sensors

View full text Add to dashboard Cite

The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.

show abstract

Machine learning-based in-season nitrogen status diagnosis and side-dress nitrogen recommendation for corn

Cited by 54 publications

References 73 publications

Soil extracellular oxidases mediated nitrogen fertilization effects on soil organic carbon sequestration in bioenergy croplands

Soil extracellular oxidases mediated nitrogen fertilization effects on soil organic carbon sequestration in bioenergy croplands

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Prediction of Pest Insect Appearance Using Sensors and Machine Learning

Contact Info

Product

Resources

About