GreenSeeker Sensor Research Articles

An active-optical reflectance sensor in-field testing for the prediction of winter wheat harvest metrics

The ambition of this study was to justify the possibility of wheat trait prediction using NDVI from a newly developed Plant-O-Meter sensor. Acquired data from Plant-O-Meter was matched with GreenSeeker's, which was designated as a reference. The experiment was carried out in the field during the 2022 growing season at the long-term experimental field. The experimental design included five different winter wheat genotypes and 20 different NPK fertilizer treatments. The GreenSeeker sensor always gave out NDVI values that were higher than those of the Plant-O-Meter by, on average, 0.029 (6.36%). The Plant-O-Meter sensor recorded similar NDVI values (94% of the variation is explained, p<0.01). The Plant-O-Meter's NDVIs had a higher CV for different wheat varieties and different sensing dates. For almost all varieties, GreenSeeker exceeded Plant-O-Meter in predicting yields for the early (March 21st) and late (June 6th) growing seasons. NDVIGreenSeeker data improved yield modeling performance by an average of 5.1% when compared to NDVIPlant-O-Meter; in terms of plant height prediction, NDVIGreenSeeker was 3% more accurate than NDVIPlant-O-Meter and no changes in spike length prediction were found. A compact, economical and user-friendly solution, the Plant-O-Meter, is straightforward to use in wheat breeding programs as well as mercantile wheat production.

Comprehensive study of on-the-go sensing and variable rate application of liquid nitrogenous fertilizer

Variable rate technology (VRT) provides a sustainable, efficient, and cost-effective nutrient application option. A pioneered study is carried out to develop a variable rate liquid fertilizer application system to detect on-the-go deficiency of Nitrogen within a field and apply it according to the requirement of crop. The system includes microcontroller, nitrogen sensor (i.e. Green-seeker sensor), proportional solenoid valve, pressure relief valve, pump, battery, etc. Microcontroller receives a signal from Green-seeker sensor and it transmits to the proportional solenoid valve (PSV). The PSV opening is change based on the prescribed nutritional dose. The experimental study is conducted on groundnut crop. The experiment is carried out for different level of fertilizer (N1-150, N2– 200, N3-250, and N4-300 l ha−1) as well as different operating speed is 3, 4, 5 km h−1 to estimate actual fertilizer dropped (AFD), % error, droplet size, droplet density (DD), uniformity coefficient (UC), and spray deposition. The response time of the system is in the range of 1.12 s to 1.80 s. It is observed from the experimental study that the error in the fertilizer to be dropped and AFD increases with increase in the speed of operation. The percentage error is found to be 2.73 %, which is the lowest one, at 3 km h−1 operating speed. The maximum droplet size is found to be 272.5 μm at 5 km h−1 forward speed and 300 l ha−1 nitrogen application rate, whereas minimum droplet size is found to be 241.23 μm at 3 km h−1 forward speed and 150 l ha−1 nitrogen application rate. The present study proposes maximum of 24.30 % saving in the fertilizer application for groundnut crop. Thus, the present system improved fertilizer use efficiency, profitability while minimizing environmental hazards.

Spectral Characterization of Avocado Persea Americana Mill. Cv. Hass Using Spectrometry and Imagery from the Visible to Near-Infrared Range

Remote sensing technologies, such as spectral imaging, have great potential for crop monitoring. Spectral systems measure the energy reflected and emitted by a surface, typically between the visible and near-infrared regions of the electromagnetic spectrum. This paper presents a spectral characterization of avocado (Persea americana Mill. cv. Hass) using spectrophotometry and spectral imaging. The study uses data from four avocado farms, which were collected in situ using spectrometers and GreenSeeker sensors and remotely using satellites such as Landsat 8 and Sentinel 2. The spectral signatures captured by the in situ and remote sensors were compared and subsequently related to vegetation indices. Spectrometry revealed differences between young and mature leaves, particularly in the 480 nm to 650 nm region of the spectrum, which showed color changes in young avocado leaves. The analysis of satellite data highlighted significant differences between Sentinel 2 and Landsat 8 spectral signatures. These differences are likely due to several factors, including collection date, preprocessing, and spatial resolution of the data. Finally, the vegetation indices derived from in situ and satellite measurements displayed different scales. For in situ data, the Normalized Difference Vegetation Index (NDVI) values were around 0.9 for the spectrometers and 0.7 for the GreenSeeker sensors. However, the NDVI values derived from satellite data were around 0.4 for Sentinel 2 and 0.3 for Landsat 8.

Wheat Yield and Protein Estimation with Handheld and Unmanned Aerial Vehicle-Mounted Sensors

Accurate sensor-based prediction of crop yield and grain quality in-season would enable growers to adjust nitrogen (N) fertilizer management for optimized production. This study assessed the feasibility (and compared the accuracy) of wheat (Triticum aestivum L.) yield, grain N uptake, and protein content prediction with in-season crop spectral reflectance measurements (Normalized Difference Vegetative Index, NDVI) obtained with a handheld GreenSeeker (GS) sensor and an Unmanned Aerial Vehicle (UAV)-mounted sensor. A strong positive correlation was observed between GS NDVI and UAV NDVI at Feekes 5 (R2 = 0.78) and Feekes 10 (R2 = 0.70). At Feekes 5, GS NDVI and UAV NDVI explained 42% and 43% of wheat yield, respectively. The correlation was weaker at Feekes 10 (R2 of 0.34 and 0.25 for GS NDVI and UAV NDVI, respectively). The accuracy of wheat grain N uptake prediction was comparable to that of yield: the R2 values for GS NDVI and UAV NDVI were 0.53 and 0.37 at Feekes 5 and 0.13 and 0.20 at Feekes 10. We found that neither GS NDVI nor UAV NDVI in-season data were useful in prediction of wheat grain protein content. In conclusion, wheat yield and grain N uptake can be estimated at Feekes 5 using either handheld or aerial based NDVI with comparable accuracy.

Comparative Sensitivity of Vegetation Indices Measured via Proximal and Aerial Sensors for Assessing N Status and Predicting Grain Yield in Rice Cropping Systems

Reflectance-based vegetation indices can be valuable for assessing crop nitrogen (N) status and predicting grain yield. While proximal sensors have been widely studied in agriculture, there is increasing interest in utilizing aerial sensors. Given that few studies have compared aerial and proximal sensors, the objective of this study was to quantitatively compare the sensitivity of aerially sensed Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge Index (NDRE) and proximally sensed NDVI for assessing total N uptake at panicle initiation (PI-NUP) and predicting grain yield in rice. Nitrogen response trials were established over a 3-year period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. At PI, a multispectral unmanned aircraft system (UAS) was used to measure NDVIUAS and NDREUAS (average ground sampling distance: 3.7 cm pixel−1), and a proximal GreenSeeker (GS) sensor was used to record NDVIGS. To enable direct comparisons across the different indices on an equivalent numeric scale, each index was normalized by calculating the Sufficiency-Index (SI) relative to a non-N-limiting plot. Kernel density distributions indicated that NDVIUAS had a narrower range of values that were poorly differentiated compared to NDVIGS and NDREUAS. The critical PI-NUP where yields did not increase with higher PI-NUP averaged 109 kg N ha−1 (±4 kg N ha−1). The relationship between SI and PI-NUP for the NDVIUAS saturated lower than this critical PI-NUP (96 kg N ha−1), whereas NDVIGS and NDREUAS saturated at 111 and 130 kg N ha−1, respectively. This indicates that NDVIUAS was less suitable for making N management decisions at this crop stage than NDVIGS and NDREUAS. Linear mixed effects models were developed to evaluate how well each SI measured at PI was able to predict grain yield. The NDVIUAS was least sensitive to variation in yields as reflected by having the highest slope (2.4 Mg ha−1 per 0.1 SI). In contrast, the slopes for NDVIGS and NDREUAS were 0.9 and 1.1 Mg ha−1 per 0.1 SI, respectively, indicating greater sensitivity to yields. Altogether, these results indicate that the ability of vegetation indices to inform crop management decisions depends on the index and the measurement platform used. Both NDVIGS and NDREUAS produced measurements sensitive enough to inform N fertilizer management in this system, whereas NDVIUAS was more limited.

Spectral indices measured with proximal sensing using canopy reflectance sensor, chlorophyll meter and leaf color chart for in-season grain yield prediction of basmati rice

A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen (N) topdressing decisions. Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field. The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor, soil and plant analyzer development (SPAD) chlorophyll meter, and two different types of leaf color charts (LCCs) for five basmati rice genotypes across different growth stages. Regression analysis was performed using normalized difference vegetation index (NDVI) recorded with GreenSeeker sensor and leaf greenness indices measured with SPAD meter and LCCs developed by Punjab Agricultural University, Ludhiana (India) (PAU-LCC) and the International Rice Research Institute, Philippines (IRRI-LCC). The exponential relationship between NDVI and grain yield exhibited the highest coefficient of determination (R2) and minimum normalized root mean square error (NRMSE) at panicle initiation stage and explained 38.3%-76.4% variation in yield using genotype-specific models. Spectral indices pooled for different genotypes were closely related to grain yield at all growth stages and explained 53.4%–57.2% variation in grain yield. Normalizing different spectral indices with cumulative growing degree days (CGDD) decreased the accuracy of yield prediction. Normalization with days after transplanting (DAT), however, did not reduce or improve the predictability of yield. The ability of each model to predict grain yield was validated with an independent dataset collected from two experiments conducted at different sites with a range of fertilizer N doses. The NDVI-based genotype-specific models exhibited a robust linear correlation (R2 = 0.77, NRMSE = 7.37%, n = 180) between observed and predicted grain yields only at 35 DAT (i.e., panicle initiation stage), while the SPAD, PAU-LCC, and IRRI-LCC consistently provided reliable predictions (with respective R2 of 0.63, 0.60, and 0.53 and NRMSE of 10%, 10%, and 13.6%) even with genotype invariant models with 900 data points obtained at different growth stages. The study revealed that unnormalized values of spectral indices, namely NDVI, SPAD, PAU-LCC, and IRRI-LCC, can be satisfactorily used for in-season estimation of grain yield for basmati rice. As LCCs are very economical compared with chlorophyll meters and canopy reflectance sensors, they can be preferably used by small farmers in developing countries.

Investigation on the Capability of GreenSeeker Sensor in Predicting Nitrogen Status and Fractional Vegetation Cover of Spinach Crop

شاخص پوشش گیاهی سبز کسری (FVC) و شاخص نرمال شده تفاضل پوشش گیاهی (NDVI) از شاخص بسیار مهم سبزینگی می‌باشند و ارتباط بسیار قوی با زیست‌توده سبز دارند. هدف اصلی این پژوهش، ارزیابی شاخص NDVI حاصل از حسگر دستیGreesnseeker (GS) در تخمین مقدار زیست‌توده، کلروفیل و شاخص FVC در گیاه اسفناج می‌باشد. در این پژوهش برای جداسازی مناسب زمینه خاک از گیاه از شاخص‌های رنگی G-B و ExG استفاده شد. در طول دوره رشد 28 تا 44 روز بعد از جوانه‌زنی گیاه، نتایج تحقیق نشان داد که NDVI حاصل از GS ارتباط خوبی با کلروفیل داشته (R = 0.61 to 0.91) و ارتباط بین این شاخص با زیست‌توده نیز معنی‌دار بود. علاوه بر این، نتایج نشان داد که در این دوره رشد ارتباط خوبی بین شاخص NDVI حاصل از GS با شاخص FVC وجود دارد ((R = 0.67 to 0.82. در حسگر در ارزیابی تاثیر نرخ نیتروژن بر شاخص NDVI، مشخص شد که تنها در دوره کوتاه 28 تا 36 روز پس از جوانه‌زنی ارتباط خطی معنی‌داری بین این دو متغیر وجود دارد. نتایج نشان داد که حسگر Greenseedke توانایی خوبی در تخمین کلروفیل و مقدار زیست‌توده گیاه دارد و از آن می‌توان در میانه رشد گیاه، مقدار شاخص پوشش گیاهی سبز کسری را به‌خوبی برآورد کرد.

Use of GreenSeeker and CM-100 as manual tools for nitrogen management and yield prediction in irrigated potato (Solanum tuberosum) production

This study evaluated the possibility of the use of GreenSeeker sensor and CM-100 chlorophyll content meter for in-season N and yield prediction in order to promote timely split N application in potato production in Kenya. Four N-fertilization rates; N0 (0), N1 (60), N2 (90) and N3 (130 kg N/ha) were led out in a Randomized Complete Block Design (RCBD) in a Greenhouse for two seasons. The results showed that % N leaf content was significantly affected by N rates. The % N leaf content and potato leaf chlorophyll content decreased as the season continued whereas the Normalized Difference Vegetation Index (NDVI) increased as the season continued. CM-100 values were significantly correlated with % N leaf content at vegetative (r=0.86***) and tuber initiation (r=0.74***) growth stages of the crop whereas the NDVI values were only significantly correlated with % N leaf at tuber initiation (r=0.82***). A significant relationship was found between CM-100 values taken at different potato stages (end of vegetative, tuber initiation, bulking and maturation stages) and tuber yield (r=0.90***, 0.82***, 0.47* and 0.41*). The NDVI values at end of vegetative growth, tuber initiation and maturation of potato were also significantly correlated with tuber yield (r=0.81***, 0.43* and 0.54*), except at bulking stage (r=0.33). For efficient in-season N management and yield prediction, CM-100 and GreenSeeker are recommended at an early stage of the crop. Further research in the different potato growing areas in Kenya to establish the different thresholds at different potato growth stages is recommended.

Сomparative tests of differentiated fertilizer application for wheat using task cards and nitrogen scanner

In the training and experimental farm of the Kuban State Agrarian University Krasnodarskoe from February 19 to July 6, 2020, an experiment was conducted on the comparative analysis of differentiated fertilizer application in on-line and off-line modes. The aim of the experiment was to compare the modes of differentiated application of nitrogen fertilizers (first and second top dressing) for winter wheat using task maps and GreenSeeker sensors. The calculation of economic efficiency showed that when using nitrogen scanners, fertilizers are saved on average 16 kg / ha, compared to the mode of creating task maps based on the NDVI index based on satellite data without losing grain yield and quality (gluten content increased by 2.3 %; protein content-0.6 %).

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

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.

Canopy Indices: a Model to Estimate the Nitrogen Rate for Barley and Wheat

The objectives were to calibrate the Soil Plant Analysis Development (SPAD) and Green Seeker (GS) as tools to determine the differential of the economic optimum nitrogen rate (dEONR) at different growth stages of barley and to evaluate if a single SPAD or GS calibration model can be used for two cereal crops: barley and spring wheat. Fourteen field experiments were conducted (2016–2018) evaluating five N rates. Relative canopy indices were determined using a SPAD-502 (rSPAD) and a GS sensor (rNDVI) at Z24, Z31, and Z39 growth stages. The relationship between sensor indices and dEONR was evaluated by fitting quadratic-plateau (QP) regression models. Data from a previous study was used to evaluate if a unique QP model could predict dEONR from canopy indices for barley and wheat. Statistically significant QP models were determined for rSPAD and rNDVI at all evaluated growth stages. The sensitivity of these models was greater for rSPAD (0.0006 on average) than for rNDVI (0.0004 on average). A single QP model was developed to predict dEONR from rSPAD at Z31 and Z39 barley growth stages (R2 = 0.68). Also, a unique model was developed to predict dEONR from rSPAD at Z31 and Z39, but not only for barley but also for wheat (R2 = 0.64). A single model could be used to determine variable in-season N rates for barley and wheat, increasing the N use efficiency and limiting possible negative economic and environmental impacts of fertilization over the agroecosystems.

Regression modeling nitrogen fertilization requirement for maize crop by combining spectral reflectance and agronomic efficiency

This study aimed to develop a novel regression model for prescription of required nitrogen (N) for maize by combining spectral reflectance data from GreenSeeker sensor and agronomic efficiency. For this end, two field trials at two sites were carried out with maize (Zea mays L.) on Oxisols under no-till systems in Southern Brazil. For both experiments, a randomized complete block design, with three replications in a split-plot arrangement, was used. The main plots consisted of four urea-N rates at sowing (0, 20, 40, and 60 kg N ha−1) and subplots included four urea-N rates in top dressing (0, 80, 160, and 240 kg N ha−1). Based on the results, a novel model for prescription of N rate in top dressing for maize was defined. The increases in maize yields with the N rates estimated by the model (16% to 39%) were similar to that obtained with the N rates used for maximum economic yield (15% to 42%). The estimated N rate by the model provided an economic return 36.5% and 7.0% higher than the N rate for maximum yield, and only 6.6% and 2.0% lower than the N rate for maximum economic yield, at sites 1 and 2, respectively. Thus, the economic return obtained using the model was closer to that reached with the N rate for maximum economic yield than that for maximum yield. Therefore, the developed model, combining spectral reflectance and agronomic efficiency, exhibited great potential to improve the maize N fertilization efficiency.

Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat.

Rapid and effective acquisition of crop growth information is a crucial step of precision agriculture for making in-season management decisions. Active canopy sensor GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) is a portable device commonly used for non-destructively obtaining crop growth information. This study intended to expand the applicability of GreenSeeker in monitoring growth status and predicting grain yield of winter wheat (Triticum aestivum L.). Four field experiments with multiple wheat cultivars and N treatments were conducted during 2013–2015 for obtaining canopy normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) synchronized with four agronomic parameters: leaf area index (LAI), leaf dry matter (LDM), leaf nitrogen concentration (LNC), and leaf nitrogen accumulation (LNA). Duration models based on NDVI and RVI were developed to monitor these parameters, which indicated that NDVI and RVI explained 80%, 68–70%, 10–12%, and 67–73% of the variability in LAI, LDM, LNC and LNA, respectively. According to the validation results, the relative root mean square error (RRMSE) were all <0.24 and the relative error (RE) were all <23%. Considering the variation among different wheat cultivars, the newly normalized vegetation indices rNDVI (NDVI vs. the NDVI for the highest N rate) and rRVI (RVI vs. the RVI for the highest N rate) were calculated to predict the relative grain yield (RY, the yield vs. the yield for the highest N rate). rNDVI and rRVI explained 77–85% of the variability in RY, the RRMSEs were both <0.13 and the REs were both <6.3%. The result demonstrates the feasibility of monitoring growth parameters and predicting grain yield of winter wheat with portable GreenSeeker sensor.

Developing Active Canopy Sensor-Based Precision Nitrogen Management Strategies for Maize in Northeast China

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.

Improving Maize N Use Efficiency Through Proximal Sensing in the Lockyer Valley

The high cost and soil mobility of Nitrogen (N) raise financial and environmental concerns, calling for its increased efficiency use. Nitrogen use efficiency (NUE) can be improved in maize production through the GreenSeeker proximal sensor. However, the optimal utilization of sensors requires a site specific algorithm in order to control factors interfering with sensors’ readings. These factors include the sensor specificities, anatomy and morphology of the crop canopy, soil properties and environmental conditions. The design of such an algorithm to improve the NUE in the Lockyer Valley will require four N rates (0, 40, 80 and 120 Kg.ha-1), in-season sensing of the canopy reflectance using the GreenSeeker sensor as well as N analysis of grains and leaves. The built algorithm will enable farmers using the GreenSeeker sensor to better manage maize N fertilization with positive outcomes for their financial returns and environmental contamination.

Evaluating RGB Imaging and Multispectral Active and Hyperspectral Passive Sensing for Assessing Early Plant Vigor in Winter Wheat.

Plant vigor is an important trait of field crops at early growth stages, influencing weed suppression, nutrient and water use efficiency and plant growth. High-throughput techniques for its evaluation are required and are promising for nutrient management in early growth stages and for detecting promising breeding material in plant phenotyping. However, spectral sensing for assessing early plant vigor in crops is limited by the strong soil background reflection. Digital imaging may provide a low-cost, easy-to-use alternative. Therefore, image segmentation for retrieving canopy cover was applied in a trial with three cultivars of winter wheat (Triticum aestivum L.) grown under two nitrogen regimes and in three sowing densities during four early plant growth stages (Zadok’s stages 14–32) in 2017. Imaging-based canopy cover was tested in correlation analysis for estimating dry weight, nitrogen uptake and nitrogen content. An active Greenseeker sensor and various established and newly developed vegetation indices and spectral unmixing from a passive hyperspectral spectrometer were used as alternative approaches and additionally tested for retrieving canopy cover. Before tillering (until Zadok’s stage 20), correlation coefficients for dry weight and nitrogen uptake with canopy cover strongly exceeded all other methods and remained on higher levels (R² > 0.60***) than from the Greenseeker measurements until tillering. From early tillering on, red edge based indices such as the NDRE and a newly extracted normalized difference index (736 nm; ~794 nm) were identified as best spectral methods for both traits whereas the Greenseeker and spectral unmixing correlated best with canopy cover. RGB-segmentation could be used as simple low-cost approach for very early growth stages until early tillering whereas the application of multispectral sensors should consider red edge bands for subsequent stages.

English

The low efficiency of nitrogen (N) fertilizers impels the innovation of current N management strategies in cereal production. Site specific N management is an emerging field providing novel alternatives to current nutrient management practices through canopy sensing. Barley N use efficiency can be enhanced with GreenSeeker proximal sensors, whose optimal utilization requires algorithms. The design of such algorithms required four N rates (0, 50, 100 and 150 kg N ha-1) and in-season sensing of barley canopy reflectance using a handheld GreenSeeker sensor as well as crop N analysis. The N rates produced enough variability in yields, N uptake and normalized difference vegetation index (NDVI) readings together with strong determination coefficients between in-season NDVI values on one hand and on the other hand in-season N uptake (R2=0.68, p<0.001), forage yield (R2=0.84, p<0.001), forage N uptake at harvest (R2 = 0.65, p<0.001), grain yields (R2=0.88, p<0.001), and grain N uptake (R2 = 0.84, p<0.001). These factors enabled the development of in-season N fertilizer algorithms for barley grain and forage production. The built algorithms will enable farmers using GreenSeeker sensors to better manage barley N fertilization with positive outcomes for their financial returns and environmental contamination. Key words: Barley canopy reflectance, nitrogen fertilizer algorithm, GreenSeeker, N use efficiency, normalized difference vegetation index (NDVI), nitrogen uptake.

A ground based platform for high throughput phenotyping

The objective of this effort was to evaluate current commercially-available sensor technology (three sonic ranging and two NDVI sensors) for use in a ground-based platform for plant phenotyping and crop management decisions. The Global Positioning System (GPS) receiver from Trimble provided a high level of accuracy during our tests. Normalized Difference Vegetation Index (NDVI) data collected using the GreenSeeker sensors were more consistent and presented less variability when compared to the Decagon SRS sensor. The consistency could be due to the GreenSeeker system averaging readings of more rows. The tests also indicated that although sonic ranging sensor technology may be employed to obtain average plant height estimates, the technology is still a limiting factor for high-accuracy measurements at the plant level.

TEMPORAL VARIABILITY IN ACTIVE REFLECTANCE SENSOR-MEASURED NDVI IN SOYBEAN AND WHEAT CROPS

ABSTRACT: Optimization of N management is one of the great challenges to be overcome in grain production, as it is directly related to productivity and can also cause environmental damage. Precision agriculture aims to solve this problem by applying nitrogen fertilizer at varying rates. Reflectance sensors are instruments capable of estimating N needs in various crops, including grain crops. However, it is not clear how these sensors perform under varying solar radiation and cloud cover, due to a lack of research on their temporal variability. Thus, this study examined the temporal variability of the NDVI (normalized difference vegetation index), as measured by an active reflectance sensor, in both soybean and wheat crops. The NDVI data were collected using a GreenSeeker sensor every 15 minutes over 12 or 14 consecutive hours. Incident solar radiation was recorded using an Instrutherm MES-100 pyranometer. In all experiments in soybean and wheat, NDVI was negatively influenced by irradiation, showing higher values at the beginning and end of the day. Changes in cloud cover also affected NDVI values during the experiments.

Characterizing Wheat Response to Water Limitation Using Multispectral and Thermal Imaging

Abstract. Effective screening of plant genotypes for their tolerance to abiotic stress is a vital step for crop improvement. Various sensing technologies can be used for developing automated plant phenotyping systems as well as for better control of stress levels imposed on the plants. In this study, seven different wheat genotypes (G1 through G7) were grown under two different water regimes in a greenhouse. Soil moisture was monitored by soil water tension sensors distributed among the experimental plots. A sensor platform with two cameras (a multispectral camera and a thermal infrared camera) was developed to capture top-view images of the wheat plots (once a week) during the course of the experiment. Image processing algorithms were developed to extract wheat growth index (GI) and crop water stress index (CWSI). Ratios of GI and CWSI between the two water treatments were calculated as proxies to assess drought tolerance of the different genotypes. The results showed that GI was correlated with NDVI measured by a GreenSeeker sensor (R2 = 0.65). Among the seven wheat genotypes studied, G5, G6, and G7 exhibited consistently higher GI ratios and lower CWSI ratios, an indication of their superiority in drought tolerance. It was also found that non-uniform environmental conditions in the greenhouse had quite a large influence on wheat growth, which made the characterization and differentiation of phenotypes among various genotypes more challenging. It is concluded that the multispectral and thermal infrared imaging system has potential for phenotypic screening of wheat genotypes for drought tolerance in a semi-controlled environment. Keywords: CWSI, Drought, Image processing, Multispectral imaging, Phenotyping, Thermal infrared imaging.