Changes in spatial and temporal variability in yield estimation are detectable through plant biophysical characteristics observed at different phenological development stages of corn. A multispectral red-edge sensor mounted on an Unmanned Aerial Systems (UAS) can provide spatial and temporal information with high resolution. Spectral analysis of UAS acquired spatiotemporal images can be used to develop a statistical model to predict yield based on different phenological stages. Identifying critical vegetation indices (VIs) and significant spectral information could lead to increased yield prediction accuracy. The objective of this study was to develop a yield prediction model at specific phenological stages using spectral data obtained from a corn field. The available spectral bands (red, blue, green, near infrared (NIR), and red-edge) were used to analyze 26 different VIs. The spectral information was collected from a cornfield at Mississippi State University using a MicaSense multispectral red-edge sensor, mounted on a UAS. In this research, a new empirical method used to reduce the effects of bare soil pixels in acquired images was introduced. The experimental design was a randomized complete block that consisted of 16 blocks with 12 rows of corn planted in each block. Four treatments of nitrogen (N) including 0, 90, 180, and 270 kg/ha were applied randomly. Random forest was utilized as a feature selection method to choose the best combination of variables for different stages. Multiple linear regression and gradient boosting decision trees were used to develop yield prediction models for each specific phenological stage by utilizing the most effective variables at each stage. At the V3 (3 leaves with visible leaf collar) and V4-5 (4-5 leaves with visible leaf collar) stages, the Optimized Soil Adjusted Vegetation Index (OSAVI) and Simplified Canopy Chlorophyll Content Index (SCCCI) were the single dominant variables in the yield predicting models, respectively. A combination of the Green Atmospherically Resistant Index (GARI), Normalized Difference Red-Edge (NDRE), and green Normalized Difference Vegetation Index (GNDVI) at V6-7, SCCCI, and Soil-Adjusted Vegetation Index (SAVI) at V10,11, and SCCCI, Green Leaf Index (GLI), and Visible Atmospherically Resistant Index (VARIgreen) at tasseling stage (VT) were the best indices for predicting grain yield of corn. The prediction models at V10 and VT had the greatest accuracy with a coefficient of determination of 0.90 and 0.93, respectively. Moreover, the SCCCI as a combined index seemed to be the most proper index for predicting yield at most of the phenological stages. As corn development progressed, the models predicted final grain yield more accurately.

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.

Unmanned Aerial Vehicle (UAVs) and crop sensors are the most widely used remote sensing tool in precision agriculture. Use of UAVs in precision agriculture is attracting increasing interest due to its unique non-destructive approach. In recent years, dramatic evolution of precision agriculture technology has been driven by technologies such as sensors and controllers, telematics, and UAVs. An intriguing area in the field of precision agriculture and UAVs is big volume of data and its analysis that have not been dealt with in depth. The objective of this study was to validate the crop data acquisition procedure and the crop relationship with different type of data acquisition technique. In particular, this paper will compare ground based active optical sensor data collection with UAVs imagery for site-specific nitrogen management. To accomplish these objectives randomized complete block plantation experimental design was used with four treatments and four replicates. The plots were 12 rows wide at 38" spacing and were 125 ft. in length. GreenSeeker field sensor and Micasense sensor for UAVs was used to evaluate the Normalized Difference Vegetation Index. The software used to analyze the data were Microsoft ® Excel ® 2013, Statistical Analysis Software (ver.9.4) and ESRI ArcGIS (ver. 10.3). The results showed that UAVs assessed NDVI are good indicator of crop nutrition along with the ground based crop sensors. The result of the statistical data analysis showed that NDVI values are dependent on nitrogen application rate. The average NDVI value for no nitrogen application was recorded 0.54 whereas for 240 lb./acre nitrogen application it was noted to be 0.76. Crucially, this correlation holds true for definite extent of nitrogen application rate. Because there was not any significant change in NDVI for 160 lb./acre and 240 lb./acre. The NDVI values being 0.74 and 0.76 respectively. The results are significant because it shows the potential of further validating the use of aerial imagery derived NDVI for real time application of crop nutrient. This research has also proven that UAVs are reliable platform for nutrient assessment and making crop management decisions.

A b s t r a c t. Downward shortwave radiation is a key quantity in the land-atmosphere interaction. Since the moderate resolution imaging spectroradiometer data has a coarse temporal resolution, which is not suitable for estimating daily average radiation, many efforts have been undertaken to estimate instantaneous solar radiation using moderate resolution imaging spectroradiometer data. In this study, the principal components analysis technique was applied to capture the information of moderate resolution imaging spectroradiometer bands, extraterrestrial radiation, aerosol optical depth, and atmospheric water vapour. A regression model based on the principal components was used to estimate daily average shortwave radiation for ten synoptic stations in the Fars province, Iran, for the period 2009-2012. The Durbin-Watson statistic and autocorrelation function of the residuals of the fitted principal components regression model indicated that the residuals were serially independent. The results indicated that the fitted principal components regression models accounted for about 86-96% of total variance of the observed shortwave radiation values and the root mean square error was about 0.9-2.04 MJ m -2 d -1 . Also, the results indicated that the model accuracy decreased as the aerosol optical depth increased and extraterrestrial radiation was the most important predictor variable among all.

HighlightsLeaf nitrogen percentage in corn was estimated using various vegetation indices derived from UAVs.Eight machine learning methods were compared to find the most accurate model for nitrogen estimation.The most influential vegetation indices were determined for estimation of leaf nitrogen.Abstract. Nitrogen (N) is the most critical component of healthy plants. It has a significant impact on photosynthesis and plant reproduction. Physicochemical characteristics of plants such as leaf N content can be estimated spatially and temporally because of the latest developments in multispectral sensing technology and machine learning (ML) methods. The objective of this study was to use spectral data for leaf N estimation in corn to compare different ML models and find the best-fitted model. Moreover, the performance of vegetation indices (VIs) and spectral wavelengths were compared individually and collectively to determine if combinations of VIs substantially improved the results as compared to the original spectral data. This study was conducted at a Mississippi State University corn field that was divided into 16 plots with four different N treatments (0, 90, 180, and 270 kg ha-1). The bare soil pixels were removed from the multispectral images, and 26 VIs were calculated based on five spectral bands: blue, green, red, red-edge, and near-infrared (NIR). The 26 VIs and five spectral bands obtained from a red-edge multispectral sensor mounted on an unmanned aerial vehicle (UAV) were analyzed to develop ML models for leaf %N estimation of corn. The input variables used in these models had the most impact on chlorophyll and N content and high correlation with leaf N content. Eight ML algorithms (random forest, gradient boosting, support vector machine, multi-layer perceptron, ridge regression, lasso regression, and elastic net) were applied to three different categories of variables. The results show that gradient boosting and random forest were the best-fitted models to estimate leaf %N, with about an 80% coefficient of determination for the different categories of variables. Moreover, adding VIs to the spectral bands improved the results. The combination of SCCCI, NDRE, and red-edge had the largest coefficient of determination (R2) in comparison to the other categories of variables used to predict leaf %N content in corn. Keywords: Corn, Gradient boosting, Machine learning, Multispectral imagery, Nitrogen estimation, Random forest, UAV, Vegetation index.