Green Seeker Research Articles

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.

Precision farming and climate change adaptation strategies used among cowpea farmers

Climate change will continue to threaten cowpea supply security due to spontaneous global warming. Efforts towards achieving effective climate change adaptation have been hindered by factors that are yet to be investigated. This paper ascertains the factors affecting effective climate change adaptation among cowpea farmers in Ido local government area of Oyo State, Nigeria for cowpea supply security. Following a survey of 108 randomly selected cowpea farmers, descriptive statistical tools were used for data analysis while multiple regression analysis was used to identify factors influencing effective climate change adaptation among cowpea farmers. Adaptation measures adopted by respondents ranged from local (low sophisticated) to high regional (moderate sophisticated) technology. The use of precision farming technology such as variable-rate technology and green seeker were discouraged among farmers. Hence, there is a need to eliminate factors hindering utilisation of modern adaptation measures for better coping mechanism and sustainable cowpea production.

In‐season potato yield prediction with active optical sensors

AbstractCrop yield prediction is a critical measurement, especially in the time when parts of the world are suffering from farming issues. Yield forecasting gives an alert regarding economic trading, food production monitoring, and global food security. This research was conducted to investigate whether active optical sensors could be utilized for potato (Solanum tuberosum L.) yield prediction at the mid.le of the growing season. Three potato cultivars (Russet Burbank, Superior, and Shepody) were planted and six rates of N (0, 56, 112, 168, 224, and 280 kg ha−1), ammonium sulfate, which was replaced by ammonium nitrate in the 2nd year, were applied on 11 sites in a randomized complete block design, with four replications. Normalized difference vegetation index (NDVI) and chlorophyll index (CI) measurements were obtained weekly from the active optical sensors, GreenSeeker (GS) and Crop Circle (CC). The 168 kg N ha−1 produced the maximum potato yield. Indices measurements obtained at the 16th and 20th leaf growth stages were significantly correlated with tuber yield. Multiple regression analysis (potato yield as a dependent variable and vegetation indices, NDVI and CI, as independent variables) could make a remarkable improvement to the accuracy of the prediction model and increase the determination coefficient. The exponential and linear models showed a better fit of the data. Soil organic matter content increased the yield significantly but did not affect the prediction models. The 18th and 20th leaf growth stages are the best time to use the sensors for yield prediction.

Application of GIS Means for the Research Competencies Development of Ecological Specialties Students

The Modern teaching concepts of students at the university provide for the training of specialists who can independently solve problems, that may arise in their further research activities. Application only traditional methods in the educational process is not enough for the scientific research competencies development of students. This article shows one of the possible ways means of geographic information systems (GIS) using in the educational process for developing research competencies students for the proposed methodology. As a result of the experiment, it was found that the combined use of GIS means in the educational process, in particular, the Normalized Difference Vegetation Index (NDVI) and the Green Seeker Handheld device, allows students to see result the practical application of the formed research competencies and the importance of their development for society. The proposed methodic for applying GIS in the educational process can be useful for studies aimed at solving the problems of introducing other tools into the educational process, in particular, the Vegetation Soil Salinity Index (VSSI) and Normalize Difference Snow Index (NDSI).

Growth and Yield of Transplanted Rice [Orzya sativa (L.)] as Influenced by NDVI Values of Green Seeker and Graded Levels of Nitrogen

Growth and Yield of Transplanted Rice [Orzya sativa (L.)] as Influenced by NDVI Values of Green Seeker and Graded Levels of Nitrogen

Development of a low-cost quadrotor UAV based on ADRC for agricultural remote sensing.

Unmanned aerial vehicle (UAV) has the advantages of good repeatability and high remote sensing (RS) information acquisition efficiency, as an important supplement bridging the gap of high-altitude and ground RS platforms. A quadrotor UAV was developed for the agricultural RS application in this study. The control system consists of a main processor and a coprocessor, integrating a three-axis gyroscope, a three-axis accelerometer, an air pressure sensor and a global positioning system (GPS) module. Engineering trial method (ETM) was used to tune the parameters based on the active disturbance rejection control (ADRC) method. Also a ground control station (GCS) adapted to the quadrotor was developed realizing autonomously take-off and landing, flight route planning, data recording. To investigate the performances of the UAV, several flight tests were carried out. The test results showed that the pitch angle control accuracy error was less than 4°, the flight height control accuracy error was less than 0.86 m, the flight path control accuracy error was less than 1.5 m overall. Aerial multispectral images were acquired and processed. The reflected digital number (DN) values obtained from a height of 10-100 m with 10 m interval could be referenced to classify objects. The normalized-difference-vegetation index (NDVI) values obtained from the aerial multispectral images acquired at 15 m were compared with those obtained by the GreenSeeker (GS) and PSR-1100F. The maximum error was 20.37% while the minimum error was 1.99%, which demonstrated the developed quadrotor UAV’s satisfactions for low altitude remote sensing practice. This study provided a low-cost platform for agricultural remote sensing. Keywords: UAV, quadrotor, ADRC, agricultural remote sensing, NDVI, aerial spraying application DOI: 10.25165/j.ijabe.20191204.4641 Citation: Zhang S C, Xue X Y, Chen C, Sun Z, Sun T. Development of a low-cost quadrotor UAV based on ADRC for agricultural remote sensing. Int J Agric & Biol Eng, 2019; 12(4): 82–87.

Weed and nitrogen management effects on weed suppression, soil properties and crop productivity in a maize (Zea mays) - wheat (Triticum aestivum) cropping system under conservation agriculture

Weeds and nutrients, particularly N, are two crucial aspects of conservation agriculture (CA), whose management often poses challenge. The combined effects of weed and N management have hardly been studied under CA. This experiment was undertaken to evaluate their effects on weed suppression, soil properties and productivity in a maize (Zea mays L.) – wheat (Triticum aestivum L. emend Fiori & Paol) system under conservation agriculture during 2013-14 and 2014-15. Three weed control treatments as main plots and four nitrogen levels as sub-plots treatments were based on integrated weed management, soil test and plant sensor–GreenSeeker (GS)-aided approach. It was observed that the herbicide combination (atrazine + pendimethalin) and the brown manuring + 2,4-D weed management in maize resulted in 66% and 31% weed control index, respectively over weedy check. But, in wheat, clodinafop-propargyl + carfentrazone-ethyl (post-emergent) and pendimethalin + carfentrazone (pre-emergent) resulted in 81% and 58% weed control index, respectively. The mean maize grain and stover yields were increased by 12% and 8%, respectively due to the optimised GS–N treatments (N2, N3, and N4) than entire N basal application (N1). Also, mean wheat grain and straw yields increased by 9% and 8%, respectively over whole N basal application. The ‘best optimised’ GS–N (N2– 50% basal + 25% broadcast at 25 DAS + rest N guided by GS) had 6%, 7% and 15% greater mean weight diameter, saturated hydraulic conductivity and microbial biomass carbon over whole N basal application after two years of cropping. While brown manuring (maize) + herbicide combination (wheat) had 4%, 7% and 6% greater mean weight diameter, saturated hydraulic conductivity and microbial biomass carbon, respectively over herbicide combinations alone. Available N, P, and K in soil were 8%, 11% and 2% higher in the optimised GS–N treatments over entire N applied as basal. It may be concluded that the integration of brown manuring (in maize)+ herbicide combinations (in wheat), and the optimisation and synchronisation of N fertilisation can suppress weeds, enhance soil fertility with improved maize and wheat productivity.

A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform

Wheat improvement programs require rapid assessment of large numbers of individual plots across multiple environments. Vegetation indices (VIs) that are mainly associated with yield and yield-related physiological traits, and rapid evaluation of canopy normalized difference vegetation index (NDVI) can assist in-season selection. Multi-spectral imagery using unmanned aerial vehicles (UAV) can readily assess the VIs traits at various crop growth stages. Thirty-two wheat cultivars and breeding lines grown in limited irrigation and full irrigation treatments were investigated to monitor NDVI across the growth cycle using a Sequoia sensor mounted on a UAV. Significant correlations ranging from R2 = 0.38 to 0.90 were observed between NDVI detected from UAV and Greenseeker (GS) during stem elongation (SE) to late grain gilling (LGF) across the treatments. UAV-NDVI also had high heritabilities at SE (h2 = 0.91), flowering (F)(h2 = 0.95), EGF (h2 = 0.79) and mid grain filling (MGF) (h2 = 0.71) under the full irrigation treatment, and at booting (B) (h2 = 0.89), EGF (h2 = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF (R2 = 0.86), MGF (R2 = 0.83) and LGF (R2 = 0.89) stages, and results were consistent with GS-NDVI. Higher correlations between UAV-NDVI and GY were observed under full irrigation at three different grain-filling stages (R2 = 0.40, 0.49 and 0.45) than the limited irrigation treatment (R2 = 0.08, 0.12 and 0.14) and GY was calculated to be 24.4% lower under limited irrigation conditions. Pearson correlations between UAV-NDVI and GY were also low ranging from r = 0.29 to 0.37 during grain-filling under limited irrigation but higher than GS-NDVI data. A similar pattern was observed for normalized difference red-edge (NDRE) and normalized green red difference index (NGRDI) when correlated with GY. Fresh biomass estimated at late flowering stage had significant correlations of r = 0.30 to 0.51 with UAV-NDVI at EGF. Some genotypes Nongda 211, Nongda 5181, Zhongmai 175 and Zhongmai 12 were identified as high yielding genotypes using NDVI during grain-filling. In conclusion, a multispectral sensor mounted on a UAV is a reliable high-throughput platform for NDVI measurement to predict biomass and GY and grain-filling stage seems the best period for selection.

USING PASSIVE AND ACTIVE MULTISPECTRAL SENSORS ON THE CORRELATION WITH THE PHENOLOGICAL INDICES OF COTTON

ABSTRACT: The investment in precision agriculture technologies has been growing as well the investment in vegetation index sensors with different types of data collection. This study aimed to evaluate the NDVI potential use obtained from passive and active multispectral sensors as phenological parameters indicator of cotton growth. The experiment was conducted in cultivated cotton fields during the harvest seasons of 2013/14 and 2014/15. The phenological variables analyzed in the field, were cotton plant height and number of branches per sampled cotton plant. The active sensors Crop Circle and Green Seeker were mounted on a manual support. The passive sensor Tetracam was installed on a support positioning the sensor at 4 m height from the ground. NDVI readings are positively correlated with plant height and number of branches per cotton plant. Active sensors present exponential correlations between NDVI and phenological readings. Passive sensor, when installed in a support above the cotton canopy level, presents linear correlation between NDVI and phenological readings. NDVI values obtained by the passive sensor are higher than the active sensors. Filtering the raw NDVI values obtained by passive sensor decreases the adjustment of the regression analyses with cotton height and number of branches per plant.

Nitrogen management of wheat cultivars for higher productivity - A review

The increased population pressure has led to the maximum use of chemical fertilizers especially in the major crops such as rice, wheat and maize production. India has an ever growing population needing food and also the growing urban middle class with higher standards of living is on the lookout for better quality product. The introduction of improved seeds increases the utility of traditional inputs and their consumption as well. Because of genetic variation, BVC 223, Qingnong 8, IPA99, CT 01217, Luomai 8, Seher 06, Sistan, Punjab 2011, Rasco 2005, PBW 343, Halna, HP 1744, VL Gehun 892, WH 1022, PBW 621, and PBW 550 cultivars of wheat crop differ in growth and development behaviour and respond higher to different nitrogen management practices. However, ever increasing prices of Nitrogen (N) fertilizers and possibilities of environmental pollution and groundwater contamination warn for their judicious and efficient use. The application of essential plant nutrients particularly N nutrient in optimum quantity (120-150 kg/ha) and right proportion (3-4 splits) through correct methods and time of application (LCC and green seeker based) is the key to increased and sustained crop production. The increase in quality due to nitrogen fertilization (120-330 kg/ha) may be due to its role in activation of cells division, metabolic and photosynthesis process and nutritive status of wheat plant. Keeping in view above all facts in mind, performance of wheat cultivars as influenced by different nitrogen rates will be discussed in this review.

Mapping of spot blotch disease resistance using NDVI as a substitute to visual observation in wheat (Triticum aestivum L.)

Evaluation of wheat for spot blotch disease resistance relies on various visual observation methods. The person evaluating the lines needs to be experienced in scoring disease severity. To facilitate high-throughput phenotyping, a hand-held green seeker NDVI sensor was used to map spot blotch disease resistance QTLs. A total of 108 germplasm lines along with 335 SSD-derived lines (F4 and F5 generations) originating from the cross ‘YS116 × Sonalika’ were used. The population was evaluated at BISA, Pusa Bihar, a hot spot for spot blotch, for 2 consecutive years. Data were recorded using the NDVI as well as by visual observation as % disease severity. The correlation coefficient was calculated between two scoring methods (NDVI and % DS) recorded at different growth stages. High negative correlation was observed between the NDVI and % DS at GS69 and GS77 on Zadoks' scale. With both methods, the QTL was mapped in the same chromosomal region on 5BL. Using the NDVI value, the detected QTL explained up to 54.9 % of phenotypic variation while up to 56.1 % using the % DS. The Sb2 gene was mapped between the markers Xgwm639 and Xgwm1043 with an interval of 0.62 cM. The markers linked to the Tsn1 gene (Xfcp1 and Xfcp623) were mapped 1.1 cM apart from the sb2 gene. It is concluded that the NDVI the can be used as an alternative to visual scoring of spot blotch disease in wheat and create a new avenue for high-throughput phenotyping.

Calibration of Nitrogen Fertilizer for Quality Protein Maize (Zea mays L.) Based on In-Season Estimated Yield using a Handheld NDVI Sensor in the Central Rift Valley of Ethiopia

Maize is one of a highly valued crop in the national diet of Ethiopians especially in southern and southeastern regions of the country; it is produces across various agro-ecologies of the country. However, its productivity is constrained by blanket application of mineral nutrients, in particular, nitrogen (N). In this study, we aimed to test site-specific nutrient management supported by precision agriculture tools can provide extension agents and farmers better ways to manage nitrogen, to increase net benefit and reduce environmental pollution. The drastic increase in N fertilizer cost, environmental pollution and increased public scrutiny have encouraged development and implementation of improved N management practices. In this context, on-farm and on-station experiment was conducted during 2014 main cropping season in the central rift valley of Ethiopia. The objective of the study was calibrating N fertilizer rate using the handheld NDVI sensor for efficient management of N to optimize grain productivity. The trials were layout in a randomized complete block design with seven levels of N fertilizer with phosphorus applied uniformly. The study evaluated the relationship between grain yield and NDVI using the handheld Green seeker sensor. The result shows that a strong nonlinear relationship (R2=0.79) were exist between NDVI values and harvest grain yield. In similarly fashion positive exponential relationship (R2=0.71) between INSEY and grain yield during vegetative growth stage six (V6) at both locations. Overall this study shows that the handheld NDVI sensor can be an indicator to better manage N fertilizer application.

Developing a new Crop Circle active canopy sensor-based precision nitrogen management strategy for winter wheat in North China Plain

Active canopy sensor (ACS)—based precision nitrogen (N) management (PNM) is a promising strategy to improve crop N use efficiency (NUE). The GreenSeeker (GS) sensor with two fixed bands has been applied to improve winter wheat (Triticum aestivum L.) N management in North China Plain (NCP). The Crop Circle (CC) ACS-470 active sensor is user configurable with three wavebands. The objective of this study was to develop a CC ACS-470 sensor-based PNM strategy for winter wheat in NCP and compare it with GS sensor-based N management strategy, soil Nmin test-based in-season N management strategy and conventional farmer’s practice. Four site-years of field N rate experiments were conducted from 2009 to 2013 to identify optimum CC vegetation indices for estimating early season winter wheat plant N uptake (PNU) and grain yield in Quzhou Experiment Station of China Agricultural University located in Hebei province of NCP. Another nine on-farm experiments were conducted at three different villages in Quzhou County in 2012/2013 to evaluate the performance of the developed N management strategy. The results indicated that the CC ACS-470 sensor could significantly improve estimation of early season PNU (R2 = 0.78) and grain yield (R2 = 0.62) of winter wheat over GS sensor (R2 = 0.60 and 0.33, respectively). All three in-season N management strategies achieved similar grain yield as compared with farmer’s practice. The three PNM strategies all significantly reduced N application rates and increased N partial factor productivity (PFP) by an average of 61–67 %. It is concluded that the CC sensor can improve estimation of early season winter wheat PNU and grain yield as compared to the GS sensor, but the PNM strategies based on these two sensors perform equally well for improving winter wheat NUE in NCP. More studies are needed to further develop and evaluate these active sensor-based PNM strategies under more diverse on-farm conditions.

Use of optical sensor for in-season nitrogen management and grain yield prediction in maize

Precision agriculture technologies have developed optical sensors which can determine plant’s normalized difference vegetation index (NDVI).To evaluate the relationship between maize grain yield and early season NDVI readings, an experiment was conducted at farm land of National Maize Research Program, Rampur, Chitwan during winter season of 2012. Eight different levels of N 0, 30, 60, 90, 120, 150, 180 and 210 kg N/ha were applied for hybrid maize RML 32 × RML 17 to study grain yield response and NDVI measurement. Periodic NDVI was measured at 10 days interval from 55 days after sowing (DAS) to 115 DAS by using Green seeker hand held crop sensor. Periodic NDVI measurement taken at a range of growing degree days (GDD) was critical for predicting grain yield potential. Poor exponential relationship existed between NDVI from early reading measured before 208 GDD (55 DAS) and grain yield. At the 261GDD (65DAS) a strong relationship (R2 = 0.70) was achieved between NDVI and grain yield. Later sensor measurements after 571 GDD (95DAS) failed to distinguish variation in green biomass as a result of canopy closure. N level had significantly influenced on NDVI reading, measured grain yield, calculated in season estimated yield (INSEY), predicted yield with added N (YPN), response index (RI) and grain N demand. Measuring NDVI reading by GDD (261–571 GDD) allow a practical window of opportunity for side dress N applications. This study showed that yield potential in maize could be accurately predicted in season with NDVI measured with the Green Seeker crop sensor.Journal of Maize Research and Development (2015) 1(1):64-70DOI: http://dx.doi.org/10.5281/zenodo.34261

Tillage, residue and nitrogen management effects on methane and nitrous oxide emission from rice–wheat system of Indian Northwest Indo-Gangetic Plains

Zero-tillage, residue management and precision nutrient management techniques are being promoted in the rice–wheat (RW) production system of Indo-Gangetic Plains (IGPs) to enhance climate change adaptation and increase food production. These management practices may also influence greenhouse gas emissions through their effects on various soil processes such as oxidation-reduction and nitrification–denitrification. We measured soil fluxes of CH4 and N2O in RW system under three tillage and residue management systems layered with four nitrogen (N) management treatments. The tillage and residue management systems comprised: conventional tillage (CT), zero-tillage without residue retention (ZT − R) and ZT with full residue retention (ZT + R) for both the crops. The four N management treatments for rice were: (a) basmati cultivar with recommended dose of nitrogen (RDN) applied in three splits, (b) basmati cultivar with 80% RDN as basal dose followed by Green Seeker (GS) guided N application, (c) hybrid cultivar with RDN applied in three splits and (d) hybrid with 80% RDN as basal dose followed by GS guided N application. The four N management treatments for wheat comprised combinations of RDN with and without relay green gram (GG), and 80% of RDN as basal dose followed by GS guided N application with and without relay GG. We employed the static chamber method to collect gas samples from the experimental plots which were subsequently analysed using gas chromatograph. Significant CH4 emissions were detected only in the CT rice system during the initial phase of continuous flooding, irrespective of N management strategies. N fertilization management affected the pattern of N2O emission with higher emission rates during crop establishment phase under 80% RDN as basal followed by GS guided N application than conventional RDN. In case of wheat, 80% RDN as basal followed by GS guided N application also induced higher cumulative N2O emissions than applying RDN at three regular splits. In rice, ZT-based RW system emitted more N2O than CT-based system. Overall ZT-based RW system reduced CH4 emission but this benefit is counterbalanced by higher N2O production compared to CT-based RW system.

Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought-stressed barley cultivars

Abstract High-throughput precision phenotyping, using spectral reflectance measurements, has the potential to provide more information for making better-informed management decisions at the canopy scale in real time. Active and passive spectral reflectance sensors are available for ground-based remote sensing; however, they have not been compared in their performance for assessing the normalized relative canopy temperature (NRCT) and the grain yield of drought-stressed plants. In this study, five spectral passive and active reflectance sensors, including a hyperspectral passive sensor (HPS), a hyperspectral active sensor (HAS), an active flash sensor (AFS), the Crop Circle (CC) and the GreenSeeker (GS), were tested to assess the NRCT and grain yield of barley cultivars under mild and severe drought stress in 2012 and 2013. Simple linear regression and partial least squares regression models were used for analysing the spectral data. The results showed that the spectral indices of all sensors were more closely related to NRCT and grain yield under mild drought stress (R2 up to 0.70, significant correlation at p ≤ 0.001) than under severe drought stress (R2 up to 0.53, significant correlation at p ≤ 0.001). Closer relationships between three normalized water indices (NWI-1, NWI-3 and NWI-4) and NRCT and grain yield were obtained for the hyperspectral passive sensor compared to the same indices of the hyperspectral active sensor and the active flash sensor under both mild and severe drought stress. Multivariate analysis using partial least square regression improved the relationship (R2 up to 0.77, significant correlation at p ≤ 0.001) compared to the individual spectral indices and the single reflectance bands for each sensor. In conclusion, both the selection of adapted measurement devices and advanced statistical methods can improve assessments of NRCT and grain yield.

Model of soybean NDVI change based on time series.

Abstract: Normalized Difference Vegetation Index (NDVI) has been found to have good correlations with many physical properties of soybean surfaces. Due to the factors of air temperature, humidity, solar radiation, soil moisture, etc., NDVI of soybean varies dynamically in a day. The establishment of the soybean NDVI prediction model at different times in a day can effectively modify this variation. The soybean NDVI values are continuously monitored in hours during soybean seeding, flowering & podding and maturating stages by way of Green Seeker. Results show that the trend of NDVI change every day in the three stages is taken on as a reverse parabola. The NDVI value reaches to the maximum at 8 am or 9 am and decreases to its minimum at 2 pm before a moderate rise. A model for intraday and long-term NDVI change for soybean is built. The test of the model with independent data indicates that the precision meets the demands, with the root mean square error (RMSE) of each day being 3.95, 5.45 and 2.86 for the seeding stage, the bean podding stage and the maturation period, respectively. The prediction RMSEs of the soybean NDVI model for soybeans of the three stages for the fifth day are 5.75, 2.65 and 5.51, respectively and the prediction RMSEs for the sixth day are 9.74, 2.82 and 14.04, respectively according to the data from the first four days. Keywords: model, NDVI, monitoring time, time series, atmospheric radiation, soybean DOI: 10.3965/j.ijabe.20140705.007 Citation: Zhang Z T, Lan Y, Wu P T, Han W T. Model of soybean NDVI change based on time series. Int J Agric & Biol Eng, 2014; 7(5): 64－70.

Normalized Difference Vegetation Index as a tool for wheat yield estimation: a case study from Faisalabad, Pakistan.

For estimation of grain yield in wheat, Normalized Difference Vegetation Index (NDVI) is considered as a potential screening tool. Field experiments were conducted to scrutinize the response of NDVI to yield behavior of different wheat cultivars and nitrogen fertilization at agronomic research area, University of Agriculture Faisalabad (UAF) during the two years 2008-09 and 2009-10. For recording the value of NDVI, Green seeker (Handheld-505) was used. Split plot design was used as experimental model in, keeping four nitrogen rates (N1 = 0 kg ha−1, N2 = 55 kg ha−1, N3 = 110 kg ha−1, and N4 = 220 kg ha−1) in main plots and ten wheat cultivars (Bakkhar-2001, Chakwal-50, Chakwal-97, Faisalabad-2008, GA-2002, Inqlab-91, Lasani-2008, Miraj-2008, Sahar-2006, and Shafaq-2006) in subplots with four replications. Impact of nitrogen and difference between cultivars were forecasted through NDVI. The results suggested that nitrogen treatment N4 (220 kg ha−1) and cultivar Faisalabad-2008 gave maximum NDVI value (0.85) at grain filling stage among all treatments. The correlation among NDVI at booting, grain filling, and maturity stages with grain yield was positive (R 2 = 0.90; R 2 = 0.90; R 2 = 0.95), respectively. So, booting, grain filling, and maturity can be good depictive stages during mid and later growth stages of wheat crop under agroclimatic conditions of Faisalabad and under similar other wheat growing environments in the country.

Canopy‐Based Normalized Difference Vegetation Index Sensors for Monitoring Cotton Nitrogen Status

Crop reflectance using ground‐based sensors has the potential to provide information on crop N status in real‐time. However, the unique physiological and structural characteristics of cotton (Gossypium hirsutum, L.) complicate normalized difference vegetation index (NDVI)‐based inferences on cotton N status. Therefore, the objectives of this study were to (i) determine the sensitivity of selected sensors and resulting NDVI measurements to different levels of fertilizer N; (ii) determine relationships between sensors and biophysical measurements; and (iii) compare absolute NDVI readings reported by each sensor. Field trials were conducted during crop years conducted from 2008 through 2010 at Mississippi State, MS. Fertilizer N rates of 0, 45, 90, and 135 kg N ha–1 were applied and replicated four times to establish N‐induced growth differences. Sensors used included the Yara N Sensor (Yara International ASA, Oslo, Norway), GreenSeeker (GS) Model 505 Optical Sensor Unit (NTech Industries, Inc., Ukiah, CA) and Crop Circle (CC) Model ACS‐210 (Holland Scientific, Inc., Lincoln, NE). Sensor readings failed to consistently predict cotton leaf N status before early flowering; however, plant height relationships with NDVIs were strong. Comparison of sensor response curves to fertilizer N resulted in no significant differences in slope values, although consistent, significant differences in y intercepts were noted beginning at early flowering and continuing through peak flowering. Greater accuracy in the detection of cotton leaf N status may require the utilization of an index which is less responsive to changes in plant height or canopy architecture. Furthermore, the magnitude of canopy reflectance‐based NDVI values differed across sensors.

Crop sensors for efficient nitrogen management in sugarcane: potential and constraints

A real-time crop sensor for site specific input application is the new innovation in the field of precision agriculture. At least three different type of crop sensors,viz.. Soil Doctor, N-Senior and Green Seeker have been used in different field crops. The key advantage of all these systems is that they do not need recommendation maps. However, no published data is available on Soil Doctor adoption by farmers due to company’s aggressiveness for protective patent rights. The N-Sensor is being used mainly for wheat and other small grain crops. However, one of the key limitations of N-Sensor is ambient light source. Handheld Green Seeker sensor is the latest addition to the list of crops sensors. The active light source is a major advantage of the Green Seeker sensor. Our preliminary observations on NDVI in relation to canopy development and crop growth in sugarcane are very encouraging and we envisage a potential scope of Green Seeker optical sensor for monitoring crop growth in order to adjust timing and dose of N application for maximizing cane and sugar productivity.