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

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.

Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield

Applying the optimum rate of fertilizer nitrogen (N) is a critical factor for field management. Multispectral information collected by active canopy sensors can potentially indicate the leaf N status and aid in predicting grain yield. Crop Circle multispectral data were acquired with the purpose of measuring the reflectance data to calculate vegetation indices (VIs) at different growth stages. Applying the optimum rate of fertilizer N can have a considerable impact on grain yield and profitability. The objectives of this study were to evaluate the reliability of a handheld Crop Circle ACS-430, to estimate corn leaf N concentration and predict grain yield of corn using machine learning (ML) models. The analysis was conducted using four ML models to identify the best prediction model for measurements acquired with a Crop Circle ACS-430 field sensor at three growth stages. Four fertilizer N levels from deficient to excessive in 50/50 spilt were applied to corn at 1–2 leaves, with visible leaf collars (V1-V2 stage) and at the V6-V7 stage to establish widely varying N nutritional status. Crop Circle spectral observations were used to derive 25 VIs for different growth stages (V4, V6, and VT) of corn at the W. B. Andrews Agricultural Systems farm of Mississippi State University. Multispectral raw data, along with Vis, were used to quantify leaf N status and predict the yield of corn. In addition, the accuracy of wavelength-based and VI-based models were compared to examine the best model inputs. Due to limited observed data, the stratification approach was used to split data to train and test set to obtain balanced data for each stage. Repeated cross validation (RCV) was then used to train the models. Results showed that the Simplified Canopy Chlorophyll Content Index (SCCCI) and Red-edge ratio vegetation index (RERVI) were the most effective VIs for estimating leaf N% and that SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were the most effective VIs for predicting corn grain yield. Additionally, among the four ML models utilized in this research, support vector regression (SVR) achieved the most accurate results for estimating leaf N concentration using either spectral bands or VIs as the model inputs.

Improving Nitrogen Status Estimation in Malting Barley Based on Hyperspectral Reflectance and Artificial Neural Networks

Malting barley requires sensitive methods for N status estimation during the vegetation period, as inadequate N nutrition can significantly limit yield formation, while overfertilization often leads to an increase in grain protein content above the limit for malting barley and also to excessive lodging. We hypothesized that the use of N nutrition index and N uptake combined with red-edge or green reflectance would provide extended linearity and higher accuracy in estimating N status across different years, genotypes, and densities, and the accuracy of N status estimation will be further improved by using artificial neural network based on multiple spectral reflectance wavelengths. Multifactorial field experiments on interactive effects of N nutrition, sowing density, and genotype were conducted in 2011–2013 to develop methods for estimation of N status and to reduce dependency on changing environmental conditions, genotype, or barley management. N nutrition index (NNI) and total N uptake were used to correct the effect of biomass accumulation and N dilution during plant development. We employed an artificial neural network to integrate data from multiple reflectance wavelengths and thereby eliminate the effects of such interfering factors as genotype, sowing density, and year. NNI and N uptake significantly reduced the interannual variation in relationships to vegetation indices documented for N content. The vegetation indices showing the best performance across years were mainly based on red-edge and carotenoid absorption bands. The use of an artificial neural network also significantly improved the estimation of all N status indicators, including N content. The critical reflectance wavelengths for neural network training were in spectral bands 400–490, 530–570, and 710–720 nm. In summary, combining NNI or N uptake and neural network increased the accuracy of N status estimation to up 94%, compared to less than 60% for N concentration.

Canopy Fluorescence Sensing for in-Season Maize Nitrogen Status Diagnosis

Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex® 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R2 = 0.73–0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R2 = 0.46–0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R2 = 0.84–0.93) and the most accurate diagnostic result.

Developing a Stacked Ensemble-Based Classification Scheme to Predict Second Primary Cancers in Head and Neck Cancer Survivors

Despite a considerable expansion in the present therapeutic repertoire for other malignancy managements, mortality from head and neck cancer (HNC) has not significantly improved in recent decades. Moreover, the second primary cancer (SPC) diagnoses increased in patients with HNC, but studies providing evidence to support SPCs prediction in HNC are lacking. Several base classifiers are integrated forming an ensemble meta-classifier using a stacked ensemble method to predict SPCs and find out relevant risk features in patients with HNC. The balanced accuracy and area under the curve (AUC) are over 0.761 and 0.847, with an approximately 2% and 3% increase, respectively, compared to the best individual base classifier. Our study found the top six ensemble risk features, such as body mass index, primary site of HNC, clinical nodal (N) status, primary site surgical margins, sex, and pathologic nodal (N) status. This will help clinicians screen HNC survivors before SPCs occur.

Projected Elevated [CO2] and Warming Result in Overestimation of SPAD-Based Rice Leaf Nitrogen Status for Nitrogen Management

Nitrogen (N) has a unique place in agricultural systems with large requirements. To achieve optimal nitrogen management that meets the needs of agricultural systems without causing potential environmental risks, it is of great significance to increase N use efficiency (NUE) in agricultural systems. A chlorophyll meter, for example, the SPAD-502, can provide a simple, nondestructive, and quick method for monitoring leaf N status and NUE. However, the SPAD-based crop leaf’s N status varies greatly due to environmental factors such as CO2 concentration ([CO2]) or temperature variations. In this study, we conducted [CO2] (ambient and enriched up to 500 μmol moL1) and temperature (ambient and increased by 1.5~2.0 °C) controlled experiments from 2015 to 2017 and in 2020 in two Free-Air CO2 Enrichment (FACE) sites. Leaf characters (SPAD readings, chlorophyll a + b, N content, etc.) of seven rice cultivars were measured in this four year experiment. Here, we provide evidence that SPAD readings are significantly linearly correlated with rice leaf chlorophyll a + b content (chl a + b) and N content, while the relationships are profoundly affected by elevated [CO2] and warming. Under elevated [CO2] treatment (E), the relationship between chl a + b content and N content remains unchanged, but SPAD readings and chl a + b content show a significant difference to those under ambient (A) treatment, which distorts the SPAD-based N monitoring. Under warming (T), and combined elevated [CO2] and warming (ET) treatments, both of the relationships between SPAD and leaf a + b content and between leaf a + b content and N content show a significant difference to those under A treatment. To deal with this issue under the background of global climate change dominated by warming and elevated [CO2] in the future, we need to increase the SPAD reading’s threshold value by at least 5% to adjust for applying N fertilizer within the rice cropping system by mid-century.

Estimation of Critical Nitrogen Concentration Based on Leaf Dry Matter in Drip Irrigation Spring Maize Production in Northern China

Background: The application of nitrogen (N) fertilizer not only increases crop yield but also improves the N utilization efficiency. The critical N concentration (Nc) can be used to diagnose crops N nutritional status. The Nc dilution curve model of maize was calibrated with leaf dry matter (LDM) as the indicator, and the performance of the model for diagnosing maize N nutritional status was further evaluated. Three field experiments were carried out in two sites between 2018 and 2020 in Ningxia Hui Autonomous Region with a series of N levels (application of N from 0 to 450 kg N ha-1). Two spring maize cultivars, i.e., Tianci19 (TC19) and Ningdan19 (ND19), were utilized in the field experiment. Results: The results showed that a negative power function relationship existed between LDM and leaf N concentration (LNC) for spring maize under drip irrigation. The Nc dilution curve equation was divided into two parts: when the LDM < 1.11 t ha-1, the constant leaf Nc value was 3.25%; and when LDM > 1.11 t ha-1, the Nc curve was 3.33*LDM-0.24. Conclusion: The LDM based Nc curve can well distinguish data the N-limiting and non-N-limiting N status of maize, which was independent with maize varieties, growing seasons and stages. Additionally, the N nutrition index (NNI) had a significant linear correlation with the relative leaf dry matter (RLDM). This study revealed that the LDM based Nc dilution curve could accurately identify spring maize N status under drip irrigation. NNI can thus, be used as a robust and reliable tool to diagnose N nutritional status of maize.

Hidden in the bushes: uncovering the diversity of the genus Neometrypus Desutter, 1988 n. status (Orthoptera: Gryllidae: Paroecanthini: Tafaliscina) in Neotropical forests

Neometrypus Desutter, 1988 n. status is elevated to the generic level. Ten new species of this genus are described (N. azevedoi n. sp., N. carvalhoi n. sp., N. catiae n. sp., N. couriae n. sp., N. lopesae n. sp., N. maiae n. sp., N. marcelae n. sp., N. mejdalanii n. sp., N. mendoncae n. sp., N. monnei n. sp.). All the species are from Brazil, nine from the Atlantic Forest, and one from Amazonia. We also provide a distribution map of all type localities of Neometrypus n. status, an identification key for all 13 known species of the genus, the first record of the mating behavior, and a short discussion about paedomorphic characters and communication between these crickets.

Use of fluorescence indices as predictors of crop N status and yield for greenhouse sweet pepper crops

To increase nitrogen (N) use efficiency and reduce water pollution from vegetable production, it is necessary to optimize N management. Fluorescence-based optical sensors are devices that can improve N fertilization through non-destructive field monitoring of crop variables. The aim of this work was to compare the performance of five fluorescence indices (SFR-R, SFR-G, FLAV, NBI-R, and NBI-G) to predict crop variables, as dry matter production, crop N content, crop N uptake, Nitrogen Nutrition Index (NNI), absolute and relative yield, in sweet pepper (Capsicum annuum) crops grown in greenhouse. Fluorescence measurements were periodically made with the Multiplex® 3.6 sensor throughout three cropping cycles subjected to five N application treatments. The performance of fluorescence indices to predict crop variables considered calibration and validation analyses. In general, the five fluorescence indices were strongly related with NNI, crop N content and relative yield. The best performing indices to predict crop N content and NNI at the early stages of the crops (i.e., vegetative and flowering phenological stages) were the SFR indices, both under red (SFR-R) and green (SFR-G) excitation. However, in the final stage of the crop (i.e., harvest stage), the best performing indices were NBI, both under red (NBI-R) and green (NBI-G) excitation, and FLAV. The two SFR indices best predicted relative yield of sweet pepper at early growth stages. Overall, the fluorescence sensor and the fluorescence indices evaluated were able to predict crop variables related to N status in sweet pepper. They have the capacity to be incorporated into best N management practices.