Using Aerial Spectral Indices to Determine Fertility Rate and Timing in Winter Wheat

Plant breeders and weed scientists address weed management collaboratively by selecting for herbicide tolerance in breeding programs. Metribuzin, a Group 5 PSII‐inhibiting herbicide, is labeled for use in wheat ( Triticum aestivum L.). However, application to currently available lines results in frequent, variable, and unpredictable crop injury. Breeding for enhanced metribuzin tolerance would allow growers to utilize this herbicide effectively while minimizing the risk of crop injury. Incorporating an additional herbicide mode of action in winter wheat production would enhance rotational flexibility and weed resistance management. Selection for improved herbicide tolerance in crops has traditionally relied on visual estimation, yet assessments can be variable. The objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor. Multispectral data were collected on paired rows of an diversity panel and advanced generation lines grown in paired plot yield trials. Vegetation indices calculated include normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), transformed chlorophyll absorption reflectance index, normalized water index, and modified triangular vegetation index. Visual assessments of injury, plant height, and grain yield were also recorded. Correlations between reflectance indices and grain yield were stronger than those between visual injury assessments and grain yield. The top 10 lines overlapped 45%–53% when selected by highest yield and highest NDVI or NDRE, respectively, in treated plots. The relationship between yield and index differences in treated and nontreated plots showed that the difference in indices (multiple R 2 = 0.0802–0.5434) explained more yield variation than visual assessments (multiple R 2 = 0.0003–0.1915). These results suggest that multispectral analysis at the plot level is a more accurate and efficient indicator of herbicide injury in winter wheat than traditional visual assessments.

Among many other ecosystem services, cover crops have potential to protect soil and nutrient loss from erosion. However, cover crop residues can alter nitrogen (N) dynamics and affect fertilizer N availability. Response of sugarbeet (Beta vulgaris L.) yield and quality were studied for two fall‐seeded cover crops, winter wheat (Triticum aestivum L.) and cereal rye (Secale cereale L.), and three fertilizer N treatments (fall and spring applications of 100% of recommended N, and 50% split in between fall and spring), in the Red River Valley of Minnesota. Cover crop species and fertilizer N application time significantly influenced sugarbeet canopy reflectance, soil inorganic N, and cover crop biomass production; however, they did not affect root yield and sugar concentration. Sugarbeet grown without a cover crop had higher normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) than sugarbeet grown after fall‐seeded rye. Fall N application had higher NDVI and NDRE than spring N application for most of the growing season. Cereal rye produced 15% higher biomass than winter wheat. Adoption of rye as a cover crop in sugarbeet might be possible without any adverse effect on sugarbeet production.

Time series of vegetation indices can be utilized to capture crop phenology information, and have been widely used in land cover and crop classification, phenological feature extraction, and planting structure monitoring. This is of great significance for guiding agricultural production and formulating agricultural policies. According to the characteristics of the GF-6 satellite’s newly-added red edge bands, wide field view and high-frequency imaging, the time series of vegetation indices about multi-temporal GF-6 WFV data are used for the study of land cover and crop classification. In this study, eight time steps of GF-6 WFV data were selected from March to October 2019 in Hengshui City. The normalized difference vegetation index (NDVI) time series and 10 different red edge spectral indices time series were constructed. Then, based on principal component analysis (PCA), using two feature selection and evaluation methods, stepwise discriminant analysis (SDA) and random forest (RF), the red edge vegetation index of normalized difference red edge (NDRE) was selected. Seven different lengths of NDVI, NDRE and NDVI&NDRE time series were reconstructed by the Savizky-Golay (S-G) smoothing algorithm. Finally, an RF classification algorithm was used to analyze the influence of time series length and red edge indices features on land cover and crop classification, and the planting structure and distribution of crops in the study area were obtained. The results show that: (1) Compared with the NDRE red edge time series, the NDVI time series is more conducive to the improvement of the overall classification accuracy of crops, and NDRE can assist NDVI in improving the crop classification accuracy; (2) With the shortening of NDVI and NDRE time series, the accuracy of crop classification is gradually decreased, and the decline is gradually accelerated; and (3) Through the combination of the NDVI and NDRE time series, the accuracy of crop classification with different time series lengths can be improved compared with the single NDVI time series, which is conducive to improving the classification accuracy and timeliness of crops. This study has fully tapped the application potential of the new red edge bands of GF-6 WFV time series data, which can provide references for crop identification and classification of time series data such as NDVI and red edge vegetation index of different lengths. At the same time, it promotes the application of optical satellite data with red edge bands in the field of agricultural remote sensing.

Developing a new active canopy sensor- and machine learning-based in-season rice nitrogen status diagnosis and recommendation strategy

To ensure sustainable agricultural practices, it is crucial to monitor the health of coconut trees. Managing the shape and size of their canopies effectively poses significant challenges due to the absence of advanced technological solutions. This industry and economy can benefit greatly from the integration of multispectral imaging, which offers innovative ways to assess and improve tree health and productivity. By deploying Unmanned Aerial Vehicles (UAV), critical vegetation indexes such as Normalized Difference Vegetative Index (NDVI), Normalized Difference Red Edge (NDRE), Chlorophyll Index (CI) green, and Chlorophyll Index red edge derived from multispectral images are cost-effective, high-resolution, and facilitating continuous plant monitoring. This study is aimed to monitor leaf nitrogen content in coconut plantation using multispectral UAV-based approach in Farm at Faculty of Agriculture, Mapalana, Kamburupitiya. The correlations were identified between UAV captured images and SPAD values, as well as soil nitrogen percentage using ground truth data which were gathered through targeted explorations aimed to optimizing data acquisition efficiency. Data processing revealed strong correlations between crop and soil variables and specific vegetation index, including NDVI, NDRE CI green and CI red edge. The NDVI demonstrated strong association with soil nitrogen content, while NDRE and CI green showed significant correlations with SPAD value with R-squared values of 71.3 % and 73.4 %, respectively. The R-Squared values for the soil nitrogen content with NDVI was 57.2 %. These findings highlight that NDRE and CI green are more effective indicators for measuring nitrogen concentration of coconut leaves. Monitoring of vegetation indices using Utilizing UAV-based monitoring of vegetation indices can greatly enhance site specific management in coconut plantation, promoting sustainable agricultural practices.

Ground‐based, active light sensing relies upon the Normalized Difference Vegetation Index (NDVI) for assessing crop N response and applying N fertilizer. However, NDVI may not work well in semiarid environments where biomass and yields depend upon plant available water. This study evaluated the Canopy Chlorophyll Content Index (CCCI) for predicting leaf chlorophyll and N contents while minimizing non‐N related crop variation. Ground reflectance was measured on wheat (Triticium aestivumL.) in a small plot experiment using an active sensor with sensitivity in red, red edge, and near infrared wavebands. Relative chlorophyll and N were measured in flag leaf samples. The CCCI was calculated from the Normalized Difference Red Edge (NDRE) index and NDVI in ratio. Index CCCI was more highly correlated with chlorophyll (r2 = 0.46) and leaf N (r2 = 0.31) than NDRE (r2 ≤ 0.16) or NDVI (r2 ≤ 0.09). Chlorophyll and leaf N were well described by CCCI in two farm fields (r2 ≤ 0.79), but NDRE or NDVI performed well in only one field. The CCCI shows promise over NDVI for predicting N status in dryland fields.

Unmanned Aerial Vehicle (UAV) technologies have nowadays become an emerging tool for the agricultural field monitoring. In this study we demonstrated the UAV remote sensing technology for menthol mint crop monitoring. UAV fitted with multispectral and thermal camera was flown over the study area during the crop growing period (May & June 2019). The high resolution multispectral images were orthomosaicked and thematic maps of popular vegetation indices (VIs) viz., NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge) were generated to assess the health status of menthol mint crop. These indices (NDVI, GNDVI and NDRE) are sensitive towards crop biophysical properties like nitrogen, chlorophyll, vigor and biomass etc. Results show that the highest value for NDVI, GNDVI, and NDRE were obtained during crop maturity period and GNDVI index was found to be the most suitable indicator for menthol mint crop health assessment. Furthermore, all the indices values were increasing with crop growth stages, clearly indicating the presence of high biomass and chlorophyll in the maturity stage as compared to the early growth stages.

The application of reflective vegetation indices is crucial for advancing precision agriculture, particularly in monitoring crop growth and development. Among these indices, the Normalized Difference Vegetation Index (NDVI) is the most widely used due to its reliability in capturing vegetation dynamics. This study focuses on the applicability and temporal dynamics of the NDVI in monitoring winter wheat (Triticum aestivum) under the specific climatic conditions of Southern Dobrudja, Bulgaria. Using a Survey3W Camera RGN mounted on DJI unmanned aerial vehicles (Phantom 4 Pro and Mavic 2 Pro) at an altitude of 100 m, NDVI data were collected over a five-year period (2019–2024). Results reveal distinct NDVI trends, with maximum values reaching 0.56 during favorable conditions, and sharp declines during late spring frosts or drought periods. These NDVI variations correlate strongly with environmental factors, including precipitation and temperature fluctuations. For instance, during the 2019–2020 season, the NDVI decreased by 30% due to severe drought and high winter temperatures. In this study, vegetation indices, including the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), were utilized to compare the results with the NDVI. The high-resolution UAV methodology demonstrated in this study proves highly effective for breeding and agronomic applications, offering precise data for optimizing wheat cultivation under variable agro-climatic conditions. These findings highlight the NDVI’s potential to enhance crop monitoring, yield prediction, and stress response management in winter wheat.

Understanding the effect of nitrogen (N) rates on hemp (Cannabis sativa) cultivation is crucial for optimizing crop yield and quality. This study evaluated the effectiveness of handheld (active) and drone (passive) sensors in measuring crop reflectance and predicting key growth parameters in response to varying N application rates. The study was conducted during the summer of 2022 at the Plant Science Research and Education Unit in Citra, FL. The trial involved three hemp cultivars—NWG‐2730, Yuma, and IH‐Williams—subjected to six N rates (0, 56, 112, 168, 224, and 280 kg/ha). Reflectance data were collected at 76 days after planting to calculate normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE). Results indicated that increased N rates led to higher NDVI and NDRE values in cultivars that had not yet reached senescence. NDVI from the drone sensor showed the strongest relationship with N rates and was the most accurate predictor for in‐season biomass yield, final biomass yield, and plant height. However, the predictive efficiency of NDVI and NDRE varied by cultivar and decreased as plants approached senescence. Early‐season crop reflectance sensing proved more reliable due to the lower impact of senescent leaves. The study highlights the potential of sensor technology in hemp cultivation, offering insights into yield forecasting, variable N management, and high‐throughput phenotyping. Future research should further explore the application of sensors to enhance precision agriculture practices in hemp cultivation.

One of the indicators of maintaining the quality of rice plants is monitoring and managing nitrogen requirements. Nitrogen (N) absorption in rice plants can be detected by remote sensing technology using Sentinel 2-A satellite imagery data using the NDRE (Normalized Difference Red Edge) method. This research aims to determine a mathematical model to predict nitrogen absorption in rice plants. This study uses the NDRE (Normalized Difference Red Edge) index value. The image used is Sentinel 2 imagery, namely channels 4 and 8 to see the Normalized Difference Vegetation Index (NDVI) value. Besides, the Normalized Difference Red-Edge Index (NDRE) is channels 5 and 8. The results of spatial and tabular data processing are analyzed per pixel and time trend to obtain patterns during one phase of the planting period. Based on the analysis of the NDVI and NDRE values of rice plants in Nagari Singkarak, the NDVI index pattern is in line with the NDRE index. At the beginning of planting (age ± 1 month) and the ripening period (age > ± 90 days) the NRDE value of rice plants is dominated by the low category NDRE value (red color). While when the rice is ± 60 days old, it is dominated by the high category NDRE value (green color). The estimation model for nitrogen uptake of rice plants in Nagari Singkarak based on NDRE data is y = 141.37 X + 0.0412, with a correlation coefficient (r) value of 0.97, which indicates a high correlation between NDRE values and nitrogen uptake.

In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. The objective of this research was to use vegetation indices (VIs) derived from unmanned aerial vehicle (UAV) imagery to remotely assign MCs to potato plants grown in trials, representing three different early stages within a multi-year breeding program. The relationships between VIs (GOSAVI – Green Optimized Soil Adjusted Vegetation Index, MCARI2 – Modified Chlorophyll Absorption Index-Improved, NDRE – Normalized Difference Red Edge, NDVI – Normalized Difference Vegetation Index, and OSAVI – Optimized Soil Adjusted Vegetation Index and WDVI – Weighted Difference Vegetation Index) and visual potato canopy status were determined. Further, this study aimed to identify factors that could improve the accuracy (decrease Mean Absolute Error – MAE) of potato MC estimation remotely. Results show that VIs derived from UAV imagery can be effectively used to remotely assign MCs to potato breeding lines, with higher accuracy for the potato B-clones (20 plants per plot) than the A-clones (6 plants per plot). Among the tested VIs, the NDRE allowed for potato MC evaluation with the lowest MAE. Applying NDRE for remote MC estimation using a validation dataset of potato B-clones (100 plants per plot), resulted in an MC estimate with a 0.81 MAE. However, the accuracy of potato MC estimation using UAV image-based methods should be improved by reducing the potato canopy’s variability (increasing uniformity) within the plot. This could be achieved by minimizing 1) potato vines bending over the neighboring row, causing vine overlap between plots, and 2) plants damaged by tractor wheels during field operations.

Drought is one of the most devastating disasters and a serious constraint on agricultural development. The reflectance-based vegetation indices (VIs), such as Normalized Difference Vegetation Index (NDVI), have been widely used for drought monitoring, but there is a lag in the response of VIs to the changes of photosynthesis induced by drought. Solar-induced chlorophyll fluorescence (SIF) is closely related to photosynthesis of vegetation and can capture changes induced by drought timely. This study investigated the capability of SIF for drought monitoring. An intelligent irrigation control system (IICS) utilizing the Internet of Things was designed and constructed. The soil moisture of the experiment plots was controlled at 60–80% (well-watered, T1), 50–60% (mild water stress, T2), 40–50% (moderate water stress, T3) and 30–40% (severe water stress, T4) of the field water capacity using the IICS based on data collected by soil moisture sensors. Meanwhile, SIF, NDVI, Normalized Difference Red Edge (NDRE) and Optimized Soil Adjusted Vegetation Index (OSAVI) were collected for a long time series using an automated spectral monitoring system. The differences in the responses of SIF, NDVI, NDRE and OSAVI to different drought intensities were fully analyzed. This study illustrates that the IICS can realize precise irrigation management strategies and the construction of regulated deficit irrigation treatments. SIF significantly decreased under mild stress, while NDVI, NDRE and OSAVI only significantly decreased under moderate and severe stress, indicating that SIF is more sensitive to drought. This study demonstrates the excellent ability of SIF for drought monitoring and lays the foundation for the future application of SIF in agricultural drought monitoring.

Adequate tiller density is critical for attaining optimum grain yield in winter wheat (Triticum aestivum L.). To ensure maximum tiller development, several states in the Mid‐Atlantic recommend split‐applying N in the spring based on tiller density at Zadoks Growth Stage 25. However, this strategy requires that several labor‐intensive measurements be made in each field. Recent work has suggested that remote sensing might eliminate this problem. The objectives of this study were to estimate winter wheat tiller density using an on‐the‐go, spectral reflectance sensor and to determine the effect on grain yield of tiller density–based, variable‐rate N applications at a 1‐m2 resolution. Twenty‐two site‐years of data were collected from diverse locations across Virginia from 2000 to 2002. The normalized difference vegetation index (NDVI) was a reliable predictor of tiller density across environments (0.67 ≤ r 2 ≤ 0.99), with 18 of 22 sites having slopes and intercepts that were not different from one another. Nitrogen fertilizer rates and grain yields resulting from using sensor‐based estimates of tiller density were not different from those when using the standard practice for the Mid‐Atlantic region at four out of six locations. At two of the six sites, sensor‐based N recommendations were 11 kg N ha−1 lower than standard recommendations with no effect on grain yield, resulting in higher N use efficiencies at these locations. These results show that on‐the‐go, optical sensor technology can be used to accurately estimate winter wheat tiller density for determining and applying appropriate N fertilization rates at a 1‐m2 resolution with minimal ground truthing required (one physical tiller count for each major soil type in a field).

<p id="C3">To clarify the effects of sowing dates, sowing rates, and nitrogen rates on growth and spectral indices in winter wheat, a two-year winter wheat field experiment under different sowing dates, sowing rates, and nitrogen rates was conducted. We studied systematically the effects of three factors and their interactions on yield, leaf area index (LAI), and normalized difference red edge (NDRE) of winter wheat at critical growth stages. Furthermore, to facilitate real-time monitoring of winter wheat growth dynamics, we also established the appropriate time-series curves of winter wheat canopy NDRE under different yield levels. The results indicated that the change patterns of NDRE and LAI at critical growth stages were very consistent, and the response of three factors to the two indices at critical growth stages was basically the same in winter wheat. In 2018 and 2019, the yield, LAI, and NDRE of winter wheat at each growth stages decreased with the delay of sowing date. In 2019 and 2020, the yield, peaks of LAI and NDRE under late sowing date were the largest except for the filling stage. The LAI and NDRE of winter wheat at different growth stages in the two years increased with the increase of nitrogen rates. However, there was basically no significant difference among sowing rates. Among the three factors, the sowing dates and nitrogen rates had a significant influence on the time-series curves of winter wheat canopy NDRE. The NDRE time series curves of winter wheat were stretched with the increase of nitrogen rates, the descending part of NDRE time series curve shifted to the left with the delay of the sowing date. In 2018 and 2019, the peak values of NDRE time series curves of winter wheat were declined with the delay of the sowing date. In 2019 and 2020, the peak values of the NDRE time series curves of late sowing and over-late sowing winter wheat were higher than that of suitable sowing wheat. The data of two years were merged to establish suitable time-series curves of winter wheat canopy NDRE under three yield levels, and the yield levels were less than 6.75 t hm^-2, 6.75-8.25 t hm^-2, and higher than 8.25 t hm^-2, respectively. The peak values and width of the NDRE time-series curves increased with the increase of yield level. In summary, winter wheat should be sown early at an appropriate date, but if the accumulated temperature before winter was higher, the sowing date should be postponed appropriately. And the growth of late sowing winter wheat could be improved by increasing a certain amount of sowing rates and nitrogen rates. At the same time, these results could provide a technical support for monitoring the growth of winter wheat under different sowing dates and different yield levels.

Assessing plant pigmentation impacts: A novel approach integrating UAV and multispectral data to analyze atrazine metabolite effects from soil contamination