Crop Sensor Research Articles

The use of crop sensors for precise fertilization

The paper presents the results of research on the use of ISARIA and OptRx sensors for the analysis of plant optical properties for precision fertilization. It was found that using the ISARIA and OptRx sensors of plant optical properties, it was possible to detect differences in the development of spring wheat crops. Consumption of OptRx mineral fertilizers in 2016 using precision fertilization of spring wheat and using plant optical analysis sensors was 3.5% higher, and in 2017 and 2018, respectively, 1.1 and 3.5% lower than in conventional intensive technology. Consumption of ISARIA mineral fertilizers in 2016 and 2018 using sensors were 0.5 and 0.6%, respectively, higher, and in 2017 it was 4.6% lower than in conventional intensive technology.

Ecological Boundaries and Interference with the Global Nitrogen Cycle: A Review on Soil Nitrogen Management Strategies

Purpose : Nitrogen (N) fertilizer is a major input in agro-ecosystems and has health, economic, and environmental implications. Changes in the global N cycle has transgressed the ecological safe boundary at present. Therefore, sustainable soil N management tools should be identified and implemented to reduce environmental implications of agriculture. This review paper intends to describe the magnitude of the global N based pollution, its health, economic, and environmental implications and suggest for approaches to achieve sustainable soil N management. Research Method : This paper shares a literature review on current efforts in optimizing soil N management. The central topic is an on-farm experiment conducted in the University of Nebraska-Lincoln, USA that monitors and manages in-season N in maize using a crop sensor technology. Other reliable findings from previously conducted studies worldwide are presented as well. Findings : Results of field studies revealed that there were no significant variations in maize grain yields between sensor-based treatment and farmer’s business-as-usual treatment However, the crop sensor-based treatment recorded savings of N at an average rate of 32 kg/ha. Based on the reported findings in literature, advanced fertilizer technology, manipulation of fertilizer application methods and development of conceptual models to predict the crop N need are other potential tools to optimize N in agriculture. Research Limitation : The behavior of reactive N in soil is unpredictable due to a complex interacting effect of crop, soil, climatic and management factors.Originality/ Value : In Sri Lanka as a country having a significant stake in agriculture, understanding and adoption of some of these available new technologies for efficient N management will serve well in our efforts for ecological sustainability.

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.

Predicting in-season maize (Zea mays L.) yield potential using crop sensors and climatological data

The environment randomly influences nitrogen (N) response, demand, and optimum N rates. Field experiments were conducted at Lake Carl Blackwell (LCB) and Efaw Agronomy Research Station (Efaw) from 2015 to 2018 in Oklahoma, USA. Fourteen site years of data were used from two different trials, namely Regional Corn (Regional) and Optimum N rate (Optimum N). Three algorithms developed by Oklahoma State University (OSU) to predict yield potential were tested on both trials. Furthermore, three new models for predicting potential yield using optical crop sensors and climatological data were developed for maize in rain-fed conditions. The models were trained/built using Regional and were then validated/tested on the Optimum N trial. Out of three models, one model was developed using all of the Regional trial (combined model), and the other two were prepared from each location LCB and Efaw model. Of the three current algorithms; one worked best at predicting final grain yield at LCB location only. The coefficient of determination R2 = 0.15 and 0.16 between actual grain yield and predicted grain yield was observed for Regional and Optimum N rate trials, respectively. The results further indicated that the new models were better at predicting final grain yield except for Efaw model (R2 = 0.04) when tested on optimum N trial. Grain yield prediction for the combined model had an R2 = 0.31. The best yield prediction was obtained at LCB with an R2 = 0.52. Including climatological data significantly improved the ability to predict final grain yield along with using mid-season sensor data.

Crop Sensor Based Non-destructive Estimation of Nitrogen Nutritional Status, Yield, and Grain Protein Content in Wheat

Minimum NNI (Nitrogen Nutrition Index) values have been developed for each key growing stage of wheat (Triticum aestivum) to achieve high grain yields and grain protein content (GPC). However, the determination of NNI is time-consuming. This study aimed to (i) determine if the NNI can be predicted using the proximal sensing tools RapidScan CS-45 (NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge)) and Yara N-TesterTM and if a single model for several growing stages could be used to predict the NNI (or if growing stage-specific models would be necessary); (ii) to determine if yield and GPC can be predicted using both tools; and (iii) to determine if the predictions are improved using normalized values rather than absolute values. Field trials were established for three consecutive growing seasons where different N fertilization doses were applied. The tools were applied during stem elongation, leaf-flag emergence, and mid-flowering. In the same stages, the plant biomass was sampled, N was analyzed, and the NNI was calculated. The NDVI was able to estimate the NNI with a single model for all growing stages (R2 = 0.70). RapidScan indexes were able to predict the yield at leaf-flag emergence with normalized values (R2 = 0.70–0.76). The sensors were not able to predict GPC. Data normalization improved the model for yield but not for NNI prediction.

Smart Agriculture Monitoring System using ML

Agriculture plays vital role in every individual’s life. As the technology improves, agricultural sector has been improving by the needs of people. Basically, the idea here deals with monitoring of weather, temperature, soil moisture and other agriculture related aspects. The objective of this paper is to upgrade -growth probability. So by making use of Advance technologies good and efficient crop can be yield. Cloud (Firebase) is typically used to store the pre-computed data (data sets) and the data from the efficiency of agriculture sector. This idea comprises of Machine Learning techniques, Cloud Computation [5] and IoT. Here we will use machine learning techniques for predicting crop sensors and comparison between these. IoT includes NPK sensors, temperature sensor, and humidity sensor. The mechanism goes like this- initially the data from humidity, temperature sensor will be noted and NPK sensors will be placed in the soil, the values from the sensors will be sent to cloud by making use of any communication technology (ZigBee, IoT gateway devices). In cloud comparison of pre-computed data and data from sensors happens by making use of machine learning. The outcome from cloud may be stored in the server (Admin) or directly be notified to authorized person of the land in the form for notification. By taking all these parameters into consideration, we can predict the best suitable crop that can be grown and farmers will earn profit in a cost-effective manner.

Reserves for the Increase of Yield and Quality of Winter Wheat Grain and Their Dependence on the Heterogeneity of Crops in the Conditions of the Steppe Zone of the Orenburg Urals, Russia

Aim. Verification of scientific concepts regarding the spatial heterogeneity of field agrocenoses. Identification of the variability of phytometric and structural crop ndicators and determination of the degree of their influence on the yield and quality of winter wheat grain in the steppe zone of the Orenburg Urals.Material and Methods. Establishment of field experiments, related observations and counts in accordance with the methodology of state variety crops testing and B.A.Dospekhov's guidelin. Monitoring of winter wheat crops was carried by measuring the vegetation index (NDVI) with a Green Seeker Handheld Crop Sensor, Model HCS‐100 (Trimble, USA). Determination of grain quality indicators was conducted according to GOST 9353‐2016 Wheat – Technical Conditions. Microsoft Office Excel was employed for the correlation and regression analysis of experimental data. Results. Analysis of the intra‐field heterogeneity of winter wheat agrocenoses in terms of yield and grain quality was conducted. The dependences of yield and grain quality on the principal crop phytometric and structural parameters were defined and expressed in the form of regression equations.Сonclusion. The results of the studies attest to the growth of reserves of grain yield to 3.0 t/ha and grain quality to class I‐II class in zonal climatic conditions of optimization of environmental factors to the level of the best basic plots by levelling out field soil heterogeneity. This is possible by restoring the fertility of anthropogenically‐degraded soil through the introduction of landscape‐adaptive and resource saving farming systems, soil protective and soil restorative crop rotation, differentiated application of organic and mineral fertilizers and selection of the most adaptive varieties. We also advise the introduction of intelligent ‘digital technologies’ aimed at fuller implementation of the genetic potential of cultivated varieties with careful consideration of natural resources and the preservation of biological diversity.

Evaluation of a UAV-mounted consumer grade camera with different spectral modifications and two handheld spectral sensors for rapeseed growth monitoring: performance and influencing factors

The objective of this study was to evaluate the crop monitoring performance of a consumer-grade camera with non-modified and modified spectral ranges which are commonly used in low-altitude unmanned aerial vehicle (UAV) platforms. The camera was fixed sequentially with seven types of filters for collecting visible images and near-infrared (NIR) images with different center band locations and bandwidths. Meanwhile, field-based hyperspectral data and normalized difference vegetation index (NDVI) measured by a GreenSeeker handheld crop sensor (GS-NDVI) were collected to examine the accuracy of rapeseed growth monitoring in terms of vegetation indices (VIs) derived from UAV images. Results showed that the UAV-based RGB-VIs and optimal NIR-VIs had similar accuracy for predicting GS-NDVI. Moreover, similar results were achieved based on the hyperspectral data, indicating the importance of spectral characteristics for GS-NDVI estimation. However, the UAV-based results also indicated that the performance of VIs derived from the band combinations containing longer NIR center wavelengths and narrower bandwidths was obviously poorer than that of the RGB-VIs. The image quality of the NIR band was also found to be inferior to the visible band based on quantitative analysis, which also revealed that image quality had great impact on UAV-based results. Image quality was then related to the effects of camera exposure, spectral sensitivity, soil background and dark areas. The results from this study provide useful information for camera modifications by selecting appropriate filters that not only are sensitive to crop growth, but also ensure image quality.

Effect of variety and growth period on NDVI estimation of nitrogen concentration in potato plants

The normalized difference vegetation index (NDVI) is an important parameter to reflect relative chlorophyll content and nitrogen level of crops, but NDVI’s ability to estimate nitrogen nutrition is affected by varieties and growth period. The field experiments using several varieties were conducted in the main potato producing areas at the north foot of Yinshan mountain, Inner Mongolia. From early July to mid-august in 2014 to 2016, the canopy spectral index NDVI was measured by using the pocket active crop sensor GreenSeeker during potato critical growth period. The effects of cultivars and growth stages on NDVI estimation of nitrogen concentration in potato plants were compared. The linear correlation between NDVI and plant nitrogen concentration (PNC) was poor in tuber initiation, but increased in process of growth period. The combination of tuber bulking period and starch accumulation period significantly improved the linear modeling effect of NDVI and PNC. Variety combination reduced the sensitivity of NDVI and increased the discreteness of data, which could be offset by NDVI time series normalization (TNDVI), especially the fitting coefficient of determination ( R 2) of TNDVI and PNC increased from 0.13 to 0.47 in the tuber bulking period. The R 2 of linear estimation model of TNDVI for the combination of tuber initiation, tuber bulking and starch accumulation period was 0.76, which was significantly higher than that of NDVI. Plant-expanded varieties had a more linear fitting trend during tuber bulking and starch accumulation. The growth period and potato varieties had significant effects on NDVI estimation of PNC, and growth period had a greater effect. The established TNDVI spectral index overcame the data differentiation and saturation phenomenon during tuber bulking and starch accumulation caused by variety difference, which provides a theoretical basis and method for the application of NDVI in the diagnosis of nitrogen concentration in potato plants.

Nitrogen fertilizer rate and time effect on dryland no‐till hard red spring wheat production

AbstractIncreasing the efficiency of wheat (Triticum aestivum L.) production is vital for Montana growers’ sustainability and competitiveness in the market. Nitrogen fertilizer applications at the right rate and time in the growing season are important for optimizing wheat grain yield and quality. Two dryland field experiments were conducted in North Central Montana in 2014. The effects of N fertilizer rates (28, 84, and 140 kg N ha−1) and application times (seeding, tillering, jointing, and flag leaf emergence) on grain yield, test weight, protein content, protein yield, grain N uptake, and N use efficiency (NUE) were evaluated. Wheat grain yield and protein content was the highest with N fertilizer applied at 140 kg N ha−1 rate at tillering. Delaying N fertilizer until flag leaf resulted in significantly lower wheat yields and grain protein values. It is possible that higher N rates would have allowed to maximize both yield and protein content. Nitrogen use efficiency was the highest with N fertilization at tillering and jointing. Nitrogen response trials coupled with historical yield records did not provide accurate estimates of optimum N rates. Close monitoring of crop nutrient status throughout the growing season (using destructive sampling and/or crop sensors), coupled with appropriate preplant soil testing, and splitting N applications to tailor N supply with crop demand should help to improve NUE.

New growth diagnosis standards of high-yielding rice and demonstration of high yielding using NDVI by GreenSeeker handheld crop sensor

New growth diagnosis standards of high-yielding rice and demonstration of high yielding using NDVI by GreenSeeker handheld crop sensor

Evaluation of OptRx™ active optical sensor to monitor soybean response to nitrogen inputs.

Active optical crop sensors have been gaining importance to determine in-season nitrogen (N) fertilization requirements for on-the-go variable rate applications. Although most of these active in-field crop sensors have been evaluated in maize (Zea mays L.) and wheat (Triticum aestivum L. emend. Thell.), these sensors have not been evaluated in soybean [Glycine max (L.) Merr.] production systems in North Dakota, USA. Recent research from both South Dakota and North Dakota, USA indicate that in-season N application in soybean can increase soybean yield under certain conditions. The study revealed that OptRx™ sensor reading did not show any significant differences from early to midway through the growing season. The NDRE (normalized difference red edge) index data collected towards the end of the growing season showed significantly higher values for some of the N treatments as compared to others in both years. The NDRE values were strongly correlated to grain yield for both years under tiled (r=0.923) and non-tiled (r=0.901) drainage conditions. Certain soybean varieties displayed significantly higher NDRE values over both years. The three varieties tested across years, under both tiled and non-tiled conditions, showed a significant linear relationship between late August NDRE values and yield (R2 =0.85 for tiled and R2 =0.81 for non-tiled). In this research, the study results show that the OptRx™ sensor has the potential to work for soybean as well, though later in the crop growing season. Further investigation is needed to confirm the use of OptRx™ sensor for variable rate in-season N applications in soybeans. © 2019 Society of Chemical Industry.

Normalized Difference Vegetation Index versus Dark Green Colour Index to estimate nitrogen status on bermudagrass hybrid and tall fescue

ABSTRACT In recent years digital sensors have been successfully integrated on board Unmanned Aerial Vehicles (UAV) to assess crop vigour, vegetation coverage, and to quantify the ‘greenness’ of foliage as indirect measurements of crop nitrogen status. The classical approach of precision agriculture has involved the use of multispectral sensors onboard UAV and the development of numerous vegetation indices associated with vegetation parameters, such as the mostly used Normalized Difference Vegetation Index (NDVI). However, the main negative issue when dealing with multi and hyper-spectral reflectance measuring tools is their high cost and complexity from the operational point of view. As a low-cost alternative, vegetation indices derived from Red Green Blue (RGB) cameras have been employed for remote-sensing assessment, providing data on different stress conditions and species. Digital images record information as amounts of RGB light emitted for each pixel of the image; however, the intensity of red and blue will often alter how green an image appears. To simplify the interpretation of digital colour data, recent studies have suggested converting RGB values to the more intuitive Hue, Saturation, and Brightness (HSB) colour spectrum, and then into a single measure of dark green colour, the Dark Green Color Index (DGCI). In this study, NDVI acquired by a ground-based handheld crop sensor and by a multispectral camera mounted on board a UAV has been compared with DGCI calculated from images taken with a commercial digital camera on board a UAV, trying to quantify the colour of turfgrass that had received different nitrogen (N) rates. The objectives of the trial were to study an affordable easy-to-use tool evaluating the relationship among NDVI, DGCI and leaf nitrogen content on turfgrass.

Site–Year Characteristics Have a Critical Impact on Crop Sensor Calibrations for Nitrogen Recommendations

Core Ideas Reflectance sensors are often calibrated to predict yield potential and crop nitrogen response.Site–year characteristics affect crop sensor calibrations.Univariate sensor models (e.g. those focused solely on normalized difference vegetation index) derived from multi site–year experimentation can have low accuracy.Soil moisture and crop sensor information should be combined for more holistic calibrations. Sensor‐based approaches to delivering nitrogen (N) fertilizer recommendations often rely on field experimentation to build calibrations between sensor readings and particular crop variables of interest. One important question is the extent to which calibrations derived from specific locations in specific seasons can be confidently extrapolated to other locations in future seasons. In this study, we examined a dataset covering 19 yr of wheat (Triticum aestivum L.) yield potential and crop N response calibrations to help answer two questions. First, what drives the variability in sensor calibration between different years? Second, what are the consequences of seasonal variability for the accuracy of sensor predictions and N recommendations? Deep soil moisture information, measured between 50 and 90 d after sowing, helped to explain the variability in the R2 and equation coefficients of yield potential and response index sensor calibrations between years. When data from multiple experimental years were combined to build generalized models, the R2 was quite low (∼0.25 for all 19 yr of data). Consequently, the benefit of the N rates recommended by the sensor over a conventional application based on historical optimum N rates was insignificant. We suggest that, rather than increasing experimental years indiscriminately, the relevant seasonal covariates should be incorporated into the sensor predictions in order build holistic sensor models. The results also highlight the likelihood that relevant site‐specific information, such as soil moisture status, may also explain between‐site variation in sensor calibration and so promote an enhanced ability to extrapolate calibrations from one location to another.

Comparison of Trimble GreenSeeker and Crop Circle (Model ACS-210) Reflectance Meters for Assessment of Severity of Late Leaf Spot

ABSTRACT Four field experiments conducted in 2015 were used to examine the relationships among normalized difference vegetation index (NDVI) measurements from two canopy crop sensors and visual estimates of defoliation by late leaf spot (Nothopassalora personata) of peanut (Arachis hypogaea) the predominant foliar disease in this study. For each evaluation, reflectance was measured with each the two meters, and leaf spot severity was measured visually within as short a time as possible. Linear and quadratic regression models were used to characterize the relationships between percent defoliation from late leaf spot and NDVI measured with the GreenSeeker (NDVIGS) and Crop Circle (NDVICC) instruments and the relationships between NDVIGS and NDVICC. NDVIGS decreased with increasing percent defoliation according to linear or quadratic functions in three of the four trials, NDVICC decreased with increasing percent defoliation according to linear functions in three of four trials. In two of the four trials, NDVICC increased with increasing NDVIGS according to quadratic functions, but there was no significant regression for those variables in two trials. In three of the four trials, NDVICC linear regression had a better fit for predicting percent defoliation according to the coefficient of determination (R2). There was no indication for either instrument that the same NDVI reading corresponded with the same level of defoliation across trials. Results indicated that NDVI measurements from the two instruments are not interchangeable.

A Novel Illumination Compensation Technique for Multi-Spectral Imaging in NDVI Detection.

To overcome the dependence on sunlight of multi-spectral cameras, an active light source multi-spectral imaging system was designed and a preliminary experimental study was conducted at night without solar interference. The system includes an active light source and a multi-spectral camera. The active light source consists of four integrated LED (Light Emitting Diode) arrays and adjustable constant current power supplies. The red LED arrays and the near-infrared LED arrays are each driven by an independently adjustable constant current power supply. The center wavelengths of the light source are 668 nm and 840 nm, which are consistent with that of filter lens of the Rededge-M multi-spectral camera. This paper shows that the radiation intensity measured is proportional to the drive current and is inversely proportional to the radiation distance, which is in accordance with the inverse square law of light. Taking the inverse square law of light into account, a radiation attenuation model was established based on the principle of image system and spatial geometry theory. After a verification test of the radiation attenuation model, it can be concluded that the average error between the radiation intensity obtained using this model and the actual measured value using a spectrometer is less than 0.0003 w/m2. In addition, the fitting curve of the multi-spectral image grayscale digital number (DN) and reflected radiation intensity at the 668 nm (Red light) is y = −3484230x2 + 721083x + 5558, with a determination coefficient of R2 = 0.998. The fitting curve with the 840 nm (near-infrared light) is y = 491469.88x + 3204, with a determination coefficient of R2 = 0.995, so the reflected radiation intensity on the plant canopy can be calculated according to the grayscale DN. Finally, the reflectance of red light and near-infrared light can be calculated, as well as the Normalized Difference Vegetation Index (NDVI) index. Based on the above model, four plants were placed at 2.85 m away from the active light source multi-spectral imaging system for testing. Meanwhile, NDVI index of each plant was measured by a Greenseeker hand-held crop sensor. The results show that the data from the two systems were linearly related and correlated with a coefficient of 0.995, indicating that the system in this article can effectively detect the vegetation NDVI index. If we want to use this technology for remote sensing in UAV, the radiation intensity attenuation and working distance of the light source are issues that need to be considered carefully.

Fine-tuning of wheat (Triticum aestivum, L.) variable nitrogen rate by combining crop sensing and management zones approaches in southern Brazil

The integration of crop sensors and management zones aiming at fine-tuning variable nitrogen rate (VNR) is a technological alternative to increase nitrogen use efficiency (NUE). The main objective of this study was to delimit management zones with different response to nitrogen input and using simulations to compare different wheat fertilization strategies: (a) traditional N management (constant rate), (b) VNR based on crop remote sensing, (c) VNR based on management zones and, (d) integrated approach combining management zones and crop sensing. This study was conducted in three steps: (i) delineation of management by overlapping maize grain yield maps, topographic features and soil apparent electrical conductivity; (ii) field experiments aiming at checking plant N uptake, N use efficiency (NUE) and wheat yield responses to N rates within each MZ—the N rates ranged from 0 to 120 kg ha−1 in field 1 and from 0 to 160 kg ha−1 in field 2- and, (iii) simulations of the four different strategies of wheat N fertilization. Both fields considered in this study were located in Carazinho, southern Brazil. The main outcomes were (1) significant N rates by management zones interactions for plant N uptake (P < 0.05) and for wheat grain yield (P < 0.05); (2) lower N fertilization response and NUE in low yield zone compared to medium and high yield zones and (3) superior performance of the integrated approach combining management zones and remote crop sensing.

Assessment of Soil Variability of South Moravian Region Based on the Satellite Imagery

The aim of this study was to analyse the sets of atmospherically corrected Remote Sensing (RS) data in order to assess the variability of bare arable land between different soil blocks in the selected area and evaluate the suitability of this approach for locally targeted management. The study was carried out on the territory of parts of South Moravian, Vysocina and Zlín regions. The RS data was analyzed by two models developed in the Arc GIS 10.22 environment using the Normalized Differential Vegetation Index (NDVI) and the Principal component analysis (PCA) analyzing bare soil (BS) with more than 50 % and more than 95 % of the block representation. A layer of agricultural land from the LPIS system was used to delimit arable land areas. For correct determinig of BS value for NDVI were carried out the terrestrial measurements in the monitored area by using the GreenSeeker handheld crop sensor and The FieldSpec® HandHeld 2 spectrometer. Based on these measurements and image dates, 0.2 NDVI was selected as the limit value. As a more suitable source for identifying BS, RapidEye appears to be able to identify an average of 26 % of the observed area as bare ground compared to Sentinel 2 data (22.5 % of the observed area) in models with more than 50 % By representing the BS in a block (NDVI 50, PCA 50). In these versions of the model was more variable soil (VS) indicated by RapidEye. With more than 95 % of the BS in the block (NDVI 95, PCA 95) was found more variable soils by Sentinel 2. This method of indirectly identifying soil variability can assist in the application of fertilizer or soil treatment in the area of site‑specific management.

An overview of use of precision farming technologies by the farmers- A case study of North Eastern Karnataka

Agriculture in future can be expected to be competitive, knowledge-intensive and market driven. Modern frontier technologies involving systems approach towards efficient crop and input management, land and water use planning are essential for Indian agriculture to meet the challenges in the present context. Further, increasing the productivity level of a pollution free technological factor of production is inevitable. With this concern the present study was carried out in North Eastern parts of Karnataka state, where the precision farming technology was implemented. Hence, an attempt has been made through present paper to study the extent of usage of precision farming technologies at farm level. The technologies implemented were GPS, GIS, variable rate applications of inputs, crop sensors etc. Adopters were aware of benefits of management aspects like size of grid, soil sampling, soil analysis, variable rate of application of fertilizers, laser levelling and harvesting by grids. But still, precision farming has yet to take its firm ground in India by moving from its infant stage. This is possible by dissemination of technology and its benefits by bringing awareness among farming community through efforts of Department of Agriculture by using SAUs and NGOs as channels.

Determining corn nitrogen rates using multiple prediction models

ABSTRACTWeather uncertainty and soil spatial variability impact nitrogen (N) cycling and corn (Zea mays L.) growth, making accurate N predictions a challenge. Field studies were conducted in Lansing, Michigan, to evaluate a computer model (i.e., Adapt-N), a preseason year-based model (i.e., maximum return to N [MRTN]), and a crop sensor model (i.e., active canopy sensor with algorithm) for recommending corn N rates. To determine site-specific economic optimum N rates (EONR), five N rates were also applied (0, 33%, 66%, 133%, and 166% of the suggested MRTN) as starter + sidedress (SD) at V4. In a wet year (i.e., 2015), Adapt-N increased V8 SD N rates 35 kg N ha−1 relative to the MRTN V4 SD N application. Although the greater rate of N may have provided additional yield protection, no statistical yield differences were observed between the two models. The MRTN model increased partial factor productivity (PFP) 20% relative to Adapt-N. Limited expression of V8 corn N deficiency reduced crop sensor total N rates (21–56 kg N ha−1) and yield (0.82–1.05 Mg ha−1) relative to other models. In a drier year (i.e., 2016), N demand was reduced (EONR 64 kg N ha−1 less than 2015), resulting in similar corn response to all three models. Despite differences in actual corn N rate recommendations, all three models resulted in similar economic net returns across study years.Abbreviations: EONR, economic optimum nitrogen rate; MRTN, Maximum Return to Nitrogen; NUE, nitrogen-use efficiency; PFP, partial factor productivity; SBNRC, sensor-based nitrogen rate calculator; SD, side-dress