Soil Plant Analysis Development Meter Research Articles

Effect of crop geometry and nitrogen levels on leaf chlorophyll content of cotton (Gossypium hirsutum) at different growth stages under rainfed Vertisols

A field experiment was conducted during the rainy (kharif) season of 2018–19 at the Regional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Lam, Gunter, Andhra Pradesh on Vertisols under rainfed conditions. To study the effect of crop geometry and nitrogen levels on compact cotton (Gossypium hirsutum L.). Leaf chlorophyll content was measured with the Soil Plant Analysis Development meter (SPAD 502 Plus Chlorophyll Meter) at different crop growth stages. Among the various crop geometry studied, maximum chlorophyll (SPAD index) content, functional leaves per square meter and leaf area index (LAI) were recorded with close crop geometry 60 cm × 10 cm. Among nitrogen levels application of 180 kg N/ha recorded maximum chlorophyll content, functional leaves per square meter and LAI. Combined effect on crop geometry and nitrogen levels on chlorophyll content was recorded significant with closer geometry 60cm x10cm along with an application of 180kg N ha-1 at 90 days after sowing. Dry matter accumulation (kg/ha) at 30 DAS, 60 DAS, 90 DAS and at harvest was significantly affected by crop geometry and levels of nitrogen but not by their interaction.

Optimal Timing of Carrot Crop Monitoring and Yield Assessment Using Sentinel-2 Images: A Machine-Learning Approach

Remotely sensed images provide effective sources for monitoring crop growth and the early prediction of crop productivity. To monitor carrot crop growth and yield estimation, three 27 ha center-pivot irrigated fields were studied to develop yield prediction models using crop biophysical parameters and vegetation indices (VIs) extracted from Sentinel-2A (S2) multi-temporal satellite data. A machine learning (ML)-based image classification technique, the random forest (RF) algorithm, was used for carrot crop monitoring and yield analysis. The VIs (NDVI, RDVI, GNDVI, SIPI, and GLI), extracted from S2 satellite data for the crop ages of 30, 45, 60, 75, 90, 105, and 120 days after plantation (DAP), and the chlorophyll content, SPAD (Soil Plant Analysis Development) meter readings, were incorporated as predictors for the RF algorithm. The RMSE of the five RF scenarios studied ranged from 7.8 t ha−1 (R2 ≥ 0.82 with Scenario 5) to 26.2 t ha−1 (R2 ≤ 0.46 with Scenario 1). The optimal window for monitoring the carrot crop for yield prediction with the use of S2 images could be achieved between the 60 DAP and 75 DAP with an RMSE of 8.6 t ha−1 (i.e., 12.4%) and 11.4 t ha−1 (16.2%), respectively. The developed RF algorithm can be utilized in carrot crop yield monitoring and decision-making processes for the self-sustainability of carrot production.

UAV-based multispectral image analytics and machine learning for predicting crop nitrogen in rice

Assessment of crop nitrogen status is essential for efficient crop growth management. Existing nitrogen measurements are accurate but destructive, laborious, and time-consuming. Therefore, the soil plant analysis development (SPAD) meter approach is commonly used to address these challenges along with location-specific measurements. The study aims to develop a robust machine learning-based model for predicting rice crop SPAD values using spectral data and to generate spatial maps of SPAD values and nitrogen content. The SPAD meter data, UAV-based multispectral images, and spectroradiometer-based data were collected during Rabi 2021/22 and 2022/23 seasons. The red and red-edge bands, Normalized Difference Vegetation Index, and Normalized Pigment Chlorophyll Index correlated well with SPAD values. The random forest regressor model performed well with UAV-based data compared to support vector regression and partial least square regression and achieved good accuracy with the ground truth spectroradiometer data. This generalized model demonstrates adaptability in precisely assessing crop nitrogen status.

Response of Wheat to Farmyard Manure and Zinc Application in a Vertisol of Central India

Zinc (Zn) deficiency is one of the major global nutrition concerns, particularly in regions where diets are cereal dominant. About 50% of soils poor in zinc have resulted in zinc deficiency in cereal grains. Farmyard manure (FYM) with Zn application is the way to meet the Zn needs of crops and to raise the Zn content of grain that is ultimately consumed by people. Therefore, a pot experiment was conducted at the Department of Soil Science and Agricultural Chemistry of JNKVV, Jabalpur, in the Rabi season of 2020-2021 with three levels of FYM (0, 5.0, and 10 t ha-1) and three levels of zinc (0, 5 and 10 kg Zn ha-1) through ZnSO4.7H2O in a completely randomized design (CRD). Results revealed that the addition of 5 t FYM ha-1 + 10 kg Zn ha-1 produced significantly better results for the plant height compared to the control. The combined application of 10 t FYM ha-1 + 10 kg Zn ha-1 showed the highest SPAD (Soil Plant Analysis Development) meter reading at 21 DAS (Days after sowing), but it was the highest at the sole application of 10 t FYM ha-1 at 45 DAS. Significant enhancements in spike length (7.65 cm), weight and number of grains per spike (23.90), and DMP of 7.12 g pot-1 were recorded with the 5 kg Zn ha-1 sole application. However, the highest grain weight (2.50 g pot-1) was recorded with 5 t FYM ha-1 + 5 kg Zn ha-1, which was on par with 5 t FYM ha-1, 5 kg Zn ha-1, and 10 t FYM ha-1+10 kg Zn ha-1. The highest Zn content (48.95 mg kg-1) in wheat grain was recorded with 10 kg Zn ha-1, followed by 5 t FYM ha-1 + 10 kg Zn ha-1 (44.45 mg kg-1). The highest Zn uptake of 99.53 µg pot-1 by grain of wheat was recorded with 5 t FYM ha-1 + 5 kg Zn ha-1, which was significantly superior to all the treatments but on par with 5 kg Zn ha-1 (98.82 µg pot-1). Combined application of 5 t FYM ha-1 +10 kg Zn ha-1 maintain extractable zinc in soil as compared to control.

Effects of optimized nitrogen fertilizer management on the yield, nitrogen uptake, and ammonia volatilization of direct-seeded rice.

Direct-seeded rice has been developed rapidly because of labor savings. Changes in rice cultivation methods put forward new requirements for nitrogen (N) fertilizer management practices. Field experiments with five different fertilizer ratios of basal, tillering and panicle fertilizer, namely N1 (10:0:0), N2 (6:2:2), N3 (4:3:3), N4 (2:4:4) and N5 (0:5:5), were conducted to investigate the effects of different N fertilizer management practices on yield formation, N uptakes, and ammonia (NH3 ) volatilization from paddy fields in direct-seeded rice. The results showed that the N4 treatment improved grain yield by 5.1% while decreasing NH3 volatilization by 20.4% compared with that of conventional fertilizer treatment (N2). The panicle number per unit area was the key factor to determine the yield of direct-seeded rice (72%). Excessive N application of basal fertilizer (N1) reduced seedling emergence, N use efficiency, and yield by 45.3%, 160.6%, and 6.9% respectively and increased NH3 volatilization by 28.1% compared with that of the N4 treatment. Removal of basal N fertilizer (N5) N reduced spike number and yield by 13.0% and 6.9% respectively, minimizing NH3 volatilization while affecting the construction of high-yielding populations compared with that of the N4 treatment. Optimized N fertilizer management achieved delayed senescence (maintenance of higher leaf Soil Plant Analysis Development meter values in late reproduction), higher canopy photoassimilation (suitable leaf area), higher N fertilizer use efficiency, and less N loss (lower cumulative NH3 volatilization). © 2023 Society of Chemical Industry.

Prediction of SPAD values for paddy leaves using smartphone-based direct contact imaging method

The Soil Plant Analysis Development (SPAD) value obtained from the SPAD meter is directly related to leaf chlorophyll content. The chlorophyll content is related to nitrogen content means the amount of fertilizer of a crop. Therefore, determining SPAD value is directly involved with crop health. Minolta SPAD meter can directly measure this value, and this is a well-established method in the research field for measuring chlorophyll content. Still, this instrument is too costly, which is beyond a farmers’ reach in the perspective of Bangladesh. The purpose of this study is to predict the SPAD value for paddy leaves using the smartphone-based direct contact imaging method to estimate the chlorophyll content of a paddy leaf. Numerous features were extracted from each image to predict the SPAD values. The features were then used as parameters in the multiple linear regression model. The models' performance was evaluated using images captured from a paddy field using a Minolta SPAD-502 Chlorophyll Meter. The multiple linear regression model's R2 and root mean square error (RMSE) values were 0.71 and 3.6512, respectively. Therefore, this result confirms that the direct digital contact imaging method has the potential to quantify the SPAD value of paddy leaves accurately. However, these results could be more accurate if the image acquisition was made from the seedling to mature stage of the paddy. In the future, an android app will be developed using this value which can directly measure the chlorophyll content of paddy leaves.

Whole-plant compensatory responses of isoprene emission from hybrid poplar seedlings exposed to elevated ozone

It is still unclear whether the responses of isoprene (ISO) emission to elevated O3 vary with biological organization level (i.e. leaf and whole-plant). To study such responses and the possible reasons explaining their variation, we investigated the effect of O3 (CF: charcoal-filtered ambient air; E-O3: non-filtered ambient air enriched with O3) on ISO emission rate (ISOrate), net photosynthetic rate (Pn), leaf nitrogen and carbon contents, and leaf growth traits in poplar seedlings (Populus deltoides cv. 55/56 × P. deltoides cv. Imperial) during one growing season. Opposite effects of E-O3 on Pn were found between upper leaves (positive effect) and lower leaves (negative effect). Compared to CF, E-O3 significantly decreased leaf mass per area, number of leaves, and leaf biomass, but increased leaf nitrogen content and individual leaf size. In the framework of such compensatory responses, poplar seedlings further increased ISOrate in upper leaves and decreased ISOrate in lower leaves, thus preventing significant decrease in the overall whole-plant ISOrate by E-O3. The measured whole-plant ISOrate also showed that the simplistic estimation approaches based on the linear regression between chlorophyll content indicated by soil plant analysis development meter (SPAD value) and leaf-level ISOrate could not accurately reflect the true response of whole plant to elevated O3. For more accurate predictions, the potential ISO compensatory response to increasing O3 concentration should be incorporated into the climate biogeochemical models related to ISO emission.

Effect of Vermicompost Enriched with Biofertilizers, Bioagents and Micronutrients on Growth and Yield of Groundnut (Arachis hypogaea L.)

Aims: To study the response of groundnut (Arachis hypogaea L.) to vermicompost enriched with biofertilizers, bioagents and micronutrients.
 Study Design: Field experiment was conducted at Junagadh (Gujarat) with ten treatments comprising of vermicompost enriched with biofertilizers, bioagents and micronutrients viz., Absolute Control (T1), 100% Recommended dose of fertilizers (T2), Vermicompost 2 t/ha (T3), Vermicompost 2 t/ha + Biofertilizers (Rhizobium Bradyrhizobium japonicum + Phosphate Solubilizing Bacteria Bacillus subtilis + Potash Solubilizing Bacteria Frateuria aurantia each at 2 L/ha) (T4), Vermicompost 2 t/ha + Trichoderma harzianum 3 kg/ha + Pseudomonas fluorescens 3 L/ha (T5), Vermicompost 2 t/ha + Beauveria bassiana 3 kg/ha (T6), Vermicompost 2 t/ha + Biofertilizers (Rhizobium + Phosphate Solubilizing Bacteria + Potash Solubilizing Bacteria each 2 L/ha) + Trichoderma harzianum 3 kg/ha + Pseudomonas fluorescens 3 L/ha + Beauveria bassiana 3 kg/ha (T7), Micronutrients (Fe + Zn + Cu + Mn) Grade-V at 40 kg/ha (T8), Vermicompost 2 t/ha + Micronutrients (Fe + Zn + Cu + Mn) Grade-V at 40 kg/ha (T9) and Vermicompost 2 t/ha + Biofertilizers (Rhizobium + Phosphate Solubilizing Bacteria + Potash Solubilizing Bacteria each 2 L/ha) + Trichoderma harzianum 3 kg/ha + Pseudomonas fluorescens 3 L/ha + Beauveria bassiana 3 kg/ha + Micronutrients (Fe + Zn + Cu + Mn) Grade-V at 40 kg/ha (T10) in Randomized Block Design with three replications.
 Place and Duration of the Study: Instructional Farm, Department of Agronomy, College of Agriculture, Junagadh Agricultural University, Junagadh (Gujarat) during kharif seasons of 2019 and 2020.
 Methodology: Five plants were selected randomly from each net experimental plot and tagged. Growth parameters viz., plant height, number of branches, SPAD meter reading and number of root nodules, and yield attributes viz., number of mature pods per plant and pods weight per plant were recorded from that tagged plants and their average was considered for final record. Pod yield and haulm yield were recorded from net plot size of each experiment plot and converted in to hectare base. Shelling percentage was counted on the basis of 150 g pod sample taken randomly from net plot produce.
 Results: The results indicated that the highest plant height (35.64 cm), number of branches per plant (8.11), number of root nodules per plant (154.5) at 45 days after sowing (DAS), dry weight of root nodules per plant at 45 DAS (0.982 g), Soil Plant Analysis Development (SPAD) meter reading at 45 DAS (39.09), number of mature pods per plant (18.40), pod weight per plant (15.37 g), 100-kernel weight (44.08 g) and shelling percentage (73.16%) with the highest pod yield (2.305 t/ha) and haulm yield (3.889 t/ha) were achieved by application of Vermicompost 2 t/ha + Biofertilizers (Rhizobium + Phosphate Solubilizing Bacteria + Potash Solubilizing Bacteria each 2 L/ha) + Trichoderma harzianum 3 kg/ha + Pseudomonas fluorescens 3 L/ha + Beauveria bassiana 3 kg/ha + Micronutrients (Fe + Zn + Cu + Mn) Grade-V at 40 kg/ha, which is considered the more effective application among all treatments improves growth, pod and haulm yield of groundnut under clay soil conditions.

Recommended nitrogen rates and the verification of effects based on leaf SPAD readings of rice.

Modern rice production systems need a reliable, easy-to-use, efficient, and environmentally-friendly method to determine plant nitrogen (N) status , predict grain yield, and optimize N management. We conducted field experiments to determine the influence of different N rates on Soil Plant Analysis Development (SPAD) readings of rice leaves. We also performed field validations to evaluate the grain yield and N use efficiency under recommended N rates. Our results showed that leaf SPAD readings increased as N rates increased. We applied the recommended N based on the relationships between the N rates and leaf SPAD readings at the tillering and booting stages. The recommended N decreased N rates and improved N use efficiency without sacrificing grain yield. When compared to farmer practices (FP), the recommended N rates of optimization (OPT) decreased by 5.8% and 10.0%, respectively. In comparison with FP, the N agronomic efficiency of OPT increased by 5.8 and 10.0% while the partial factor productivity of N increased by 6.0 and 14.2%, respectively. The SPAD meter may be a reliable tool to analyze the N in rice, estimate real-time N fertilization, and improve N use efficiency.

Optical sensors to predict sugarbeet yield, quality, and fertilizer nitrogen application rate

Nitrogen (N) management is critical for sugarbeet (Beta vulgaris L.) because N inversely influences root yield and recoverable white sucrose per tonne (RWST). In Ontario, from 2015 to 2017, the use of optical sensors (e.g., a soil plant analysis development (SPAD) chlorophyll meter, GreenSeeker handheld crop sensor) was evaluated as a method to guide N application and harvest date (late-September, late-October) selection by predicting root yield RWST and partial profit margins. In a commercial field, 4 to 5 fertilizer N rates, and 8 to 12 cultivars were tested in a split block design experiment with three replications and two harvest dates. In all years, few cultivars (≤2) had a root yield response to applied N, which was attributed to high inherent soil fertility, and limited our evaluation of optical sensors to adjust in-season N applications. The optimal N rate to maximize RWST and profits was 0 to 45 kg N·ha−1 and confirmed their negative relationship to applied N. Optical sensor readings correlated negatively with RWST across the majority (>60%) of cultivars tested in mid-August and September. Across all cultivars, the regression model of optical sensors to predict RWST at early harvest was strongest (R2 = 0.48 for SPAD; 0.24 for GreenSeeker) when readings were taken in early September. Although future research to refine this relationship is needed, we recommend the use of optical sensors, particularly the SPAD meter, in early September to guide harvest selection to maximize RWST.

Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data

Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a variable, rate of change in reflectance between wavelengths ‘a’ and ‘b’ (RCRWa-b), derived from in situ hyperspectral remote sensing data combined with four advanced machine learning techniques, Gaussian process regression (GPR), random forest regression (RFR), support vector regression (SVR), and gradient boosting regression tree (GBRT), were used to estimate the chlorophyll content (measured by a portable soil–plant analysis development meter) of rice. The performances of the four machine learning models were assessed and compared using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results revealed that four features of RCRWa-b, RCRW551.0–565.6, RCRW739.5–743.5, RCRW684.4–687.1 and RCRW667.9–672.0, were effective in estimating the chlorophyll content of rice, and the RFR model generated the highest prediction accuracy (training set: RMSE = 1.54, MAE =1.23 and R2 = 0.95; validation set: RMSE = 2.64, MAE = 1.99 and R2 = 0.80). The GPR model was found to have the strongest generalization (training set: RMSE = 2.83, MAE = 2.16 and R2 = 0.77; validation set: RMSE = 2.97, MAE = 2.30 and R2 = 0.76). We conclude that RCRWa-b is a useful variable to estimate chlorophyll content of rice, and RFR and GPR are powerful machine learning algorithms for estimating the chlorophyll content of rice.

Chlorophyll estimation in soybean leaves infield with smartphone digital imaging and machine learning

Soybean (Glycine max (L.) Merrill) leaf chlorophyll content is indicative of the plant growth and health issues. However, chlorophyll measurement using the standard chemical procedure is laborious, while the sensor-based electronic options, such as soil plant analysis development (SPAD) meter tend to be highly expensive and made only spot measurements. Therefore, a simpler and less expensive infield method of chlorophyll measurement in soybeans using smartphone camera with image processing and machine learning models was developed. Soybean leaf images (720 images) and SPAD readings were collected from different cultivars (4), with replications (3) and sampling dates (2) from experimental plots. Of the several color vegetation indices (CVIs) tested, the dark green color index (DGCI) had the best correlation with SPAD meter readings (r=0.90), which was further improved by color calibration (r=0.93). The results of the random coefficients model showed that both cultivars and sampling dates had no significant effect (0.06≤P≤0.96), hence data were combined for the analysis. The simpler statistical linear regression (SLR) and polynomial regression (PR), multiple linear regression as well as the advanced machine learning models (support vector machine (SVM), random forest (RF)) tested with color scheme inputs (RGB, DGCI, range pixel count (RPC) of DGCI, and ‘Both’ (RPC + RGB)) produced the best chlorophyll prediction with DGCI, RPC, and ‘Both’ inputs (0.87<R2<0.89; 2.90≤RMSE≤3.41 SPAD units). Overall, these models were not significantly different, but the SVM model found to be the best (R2=0.89 and RMSE=2.90SPAD units). The simpler SLR and PR models with DGCI input (R2≥0.87 and RMSE≤3.1 SPAD units) performed as good as the advanced SVM and RF models. The SVM model had the potential of predicting the chlorophyll directly with the raw RGB input (R2=0.86 and RMSE=3.20SPAD units) without the need of using the standard calibration board. The developed methodology of image processing with machine learning modeling and conversion relationship of measuring infield soybean leaf chlorophyll is efficient, inexpensive, not requiring the standard calibration board, and can be easily extended to other large-scale aerial imaging platforms and field crops.

Leaf chlorophyll estimates of temperate deciduous shrubs during autumn senescence using a SPAD-502 meter and calibration with extracted chlorophyll

The soil plant analysis development (SPAD) meter proved effective at estimating leaf Chl content of native and non-native temperate deciduous shrubs. However, due to the change in the relationship between SPAD values and Chl content during the course of autumn senescence, it may be necessary to establish separate calibration equations to cover a range of leaf Chl concentrations. Non-destructive estimates of leaf chlorophyll (Chl) content by hand-held chlorophyll meters such as the Minolta SPAD-502 provide an effective time-efficient method of collecting field data on senescence. To establish useable species-specific calibration equations of relationships between chlorophyll meter readings and leaf Chl content. During autumn, we collected SPAD values and quantum yield of photosystem-II (ΦPSII) over a 10-week period from 1044 leaf samples representing native and non-native temperate deciduous shrub species growing in the wild. Subsequently, Chl was extracted from leaf discs (1 cm diameter), incubated at 65 °C for 2 h in dimethyl sulfoxide, and quantified by spectroscopy. Relationships between extracted Chl and SPAD values were established using linear, quadratic, and exponential equations. Quadratic functions proved the most reliable fit for the data. Interestingly, non-native species tended to have higher leaf Chl content and SPAD values but also exhibited higher variability than native species. The strength (r2) of the SPAD-Chl relationships was weaker than those reported for agricultural or greenhouse-grown plants, but strengthened over time as Chl, ΦPSII, and SPAD values declined. The SPAD meter proved effective at estimating leaf Chl content of native and non-native temperate deciduous shrubs. These estimates may be used in future to validate remote sensing (satellite and near-surface) vegetation indices in late autumn when trees are leafless.

Genome-Wide Association Mapping for Heat Stress Responsive Traits in Field Pea.

Environmental stress hampers pea productivity. To understand the genetic basis of heat resistance, a genome-wide association study (GWAS) was conducted on six stress responsive traits of physiological and agronomic importance in pea, with an objective to identify the genetic loci associated with these traits. One hundred and thirty-five genetically diverse pea accessions from major pea growing areas of the world were phenotyped in field trials across five environments, under generally ambient (control) and heat stress conditions. Statistical analysis of phenotype indicated significant effects of genotype (G), environment (E), and G × E interaction for all traits. A total of 16,877 known high-quality SNPs were used for association analysis to determine marker-trait associations (MTA). We identified 32 MTAs that were consistent in at least three environments for association with the traits of stress resistance: six for chlorophyll concentration measured by a soil plant analysis development meter; two each for photochemical reflectance index and canopy temperature; seven for reproductive stem length; six for internode length; and nine for pod number. Forty-eight candidate genes were identified within 15 kb distance of these markers. The identified markers and candidate genes have potential for marker-assisted selection towards the development of heat resistant pea cultivars.

Smartphone image based digital chlorophyll meter to estimate the value of citrus leaves chlorophyll using Linear Regression, LMBP-ANN and SCGBP-ANN

The chlorophyll of leaf can be determined using soil plant analysis development meter or spectrophometer by agriculture scientists, agriculture experts, and farmers. Usually, these methods are very costly and may not be available to all the farmers and experts. Low greenness of leaf indicates low photosynthesis in the plant and it creates many problems in the plant. This paper forwards a low-cost smartphone image-based digital chlorophyll meter to predict the chlorophyll of citrus leaf. The chlorophyll of citrus leaf is predicted using Linear Regression (LR) and Artificial Neural Network (ANN). Here, ANN provides more accuracy as compared to LR in citrus chlorophyll prediction. Both methods are validated with the actual chlorophyll of the citrus leaf. The proposed method can be used as a reasonable method for chlorophyll prediction of citrus.

Improving Maize Yield and Nitrogen Use Efficiency Using Spectral Properties of Leaves for Need-based Nitrogen Management

There is a spiral increase in maize consumption in Ghana due to high population growth and changing consumer preferences. However, maize yield in the country is declining partly due to poor soil fertility and fertilizer management, resulting in low nitrogen use efficiency. A field experiment was therefore conducted at the Soil and Irrigation Research Centre, University of Ghana, Kpong during the 2016 cropping season to evaluate the effect of nitrogen management options on growth, yield and nitrogen use efficiency of maize. The experiment was laid out in a split plot design with three replicates. Variety and nitrogen (N) management were the main and sub plot factors, respectively. The varieties were; 1. Aburohemaa (V1) 2. Abontem (V2) 3. Obaatanpa (V3). 4. Omankwa (V4), while the nitrogen management levels included; 1. no N application (control, N0), 2. Conventional practice, Conv (150 kg N ha-1) 3. Leaf Colour Chart, LCC (75 kg N ha-1) and 4. Soil Plant Analysis Development meter, SPAD (75 kg N ha-1). The results revealed that LCC and SPAD meter in maize nitrogen-based management reduced nitrogen fertilizer input by 50% without significantly affecting the growth, grain yield and nitrogen use efficiency of maize. Maize varieties significantly differed in terms of growth rate, yielding potential, N uptake and N use efficiency. The study recommended LCC to maize farmers since it is relatively cheap, easy to use and saved 50% N fertilizer input.

Replacing the Nitrogen Nutrition Index by SPAD Values and Analysis of Effect Factors for Estimating Rice Nitrogen Status

Core Ideas This study aims to understand the relationships of Na, Nw, NNI, and SPAD values.We find it is more specific to analyze the effect of factors on SPAD values.Some efficient measures may enhance the accuracy of SPAD values.Relative personnel may use more efficiently and accurately the SPAD meter in rice. Determination of the nitrogen nutrition index (NNI) of a crop requires measurement of the amount and status of N in shoot biomass, which restricts its wide application in paddy fields under field conditions. Thus, a convenient and efficient method is required. We estimated the N concentration of leaves in the rice canopy on the basis of dry matter weight, Nw (%) and per unit area, Na (mg cm−2), and measured Soil–Plant Analysis Development (SPAD) values as a potential replacement for NNI. Two rice cultivars with six N fertilization rates (0, 75, 150, 225, 300, and 375 kg N ha−1) were used in a field study from 2010 to 2012 in Hangzhou, China. The SPAD values, Na, and Nw of the upper four leaves in the rice canopy were measured at three developmental stages (tillering, booting, and heading) alongside the NNI. The results showed that with increasing N rates the SPAD values, Na, Nw, and NNI increased in all developmental stages and leaf positions each year. The SPAD values, Na, and Nw showed linear regression with NNI, but the regression parameters varied between developmental stages. Once developmental stages were determined, the equation parameters did not differ among years, leaf positions, and cultivars. The relationships between Na and NNI, and SPAD values and NNI were closer than those between Nw and NNI, suggesting that SPAD values and Na may replace NNI for a quick and approximate prediction of rice plant N nutrition in the field.

Rice Leaf Lateral Asymmetry in the Relationship between SPAD and Area-Based Nitrogen Concentration

Rice leaves display lateral asymmetry around the midrib, and the narrow side exhibits higher leaf area-based nitrogen concentration (Na) and soil plant analysis development (SPAD) values than the wider side. However, the difference in the relationship between the SPAD of each side and Na of the corresponding lateral half, and the optimal position along the leaf blade for SPAD measurements are not known. In this study, the relationship between SPAD and Na of both sides of the top three leaves was determined with 17 rice varieties grown over three growing seasons in two locations. The relationship between SPAD and Na displayed leaf lateral asymmetry, in which the wide side reflected a higher coefficient of determination than the narrow side. The ability to estimate Na of the whole leaf was slightly improved by averaging SPAD values across the leaf sides and measured points for the top two leaves. Apparently, it was more accurate and easier to measure SPAD readings on the wide side than the narrow side of rice leaf blade with respect to estimating plant N status. Due to the relatively poor relationship of the upper leaf, and the structural limit for SPAD measurements of the base, this study suggests that the most suitable and representative position for SPAD meter measurement on the leaf blade of rice is the lower-middle part from the leaf apex on the wide side.

Regularized Neural Networks Fusion and Genetic Algorithm Based On-Field Nitrogen Status Estimation of Wheat Plants

The estimation of nutrient content of plants is considerably important in agricultural practices, especially in enabling the application of precision farming. A plethora of methods has been used to estimate nitrogen amount in plants, including the utilization of computer vision. However, most of the image-based nitrogen estimation methods are conducted in controlled environments. These methods are not so practical, time consuming, and require many equipment. Therefore, there is a crucial need to develop a method to estimate nitrogen content of plants based on leaves images captured on field. It is a very challenging task since the intensity of sunlight is always changing and this leads to an inconsistent image capturing problem. In this paper, we develop a low-cost, simple, and accurate approach image-based nitrogen amount estimation. Plant images are captured directly under sunlight by using a conventional digital camera and are subject to a variation in lighting conditions. We propose a color constancy method using neural networks fusion and a genetic algorithm to normalize various plant images due to different sunlight intensities. A Macbeth color checker is utilized as the reference to normalize the color of the images. We also develop a combination of neural networks using a committee machine to estimate the nitrogen content in wheat leaves. Twelve statistical RGB color features are used as the input parameters for the nutrient estimation. The obtained result shows considerable better performance than the conventional gray-world and scale-by-max approaches, as well as linear model and single neural network methods. Finally, we show that our nutrient estimation approach is superior to the commonly used soil-plant analysis development meter based prediction.

Optimal Leaf Positions for SPAD Meter Measurement in Rice.

The Soil Plant Analysis Development (SPAD) chlorophyll meter is one of the most commonly used diagnostic tools to measure crop nitrogen status. However, the measurement method of the meter could significantly affect the accuracy of the final estimation. Thus, this research was undertaken to develop a new methodology to optimize SPAD meter measurements in rice (Oryza sativa L.). A flatbed color scanner was used to map the dynamic chlorophyll distribution and irregular leaf shapes. Calculus algorithm was adopted to estimate the potential positions for SPAD meter measurement along the leaf blade. Data generated by the flatbed color scanner and SPAD meter were analyzed simultaneously. The results suggested that a position 2/3 of the distance from the leaf base to the apex (2/3 position) could represent the chlorophyll content of the entire leaf blade, as indicated by the relatively low variance of measurements at that position. SPAD values based on di-positional leaves and the extracted chlorophyll a and b contents were compared. This comparison showed that the 2/3 position on the lower leaves tended to be more sensitive to changes in chlorophyll content. Finally, the 2/3 position and average SPAD values of the fourth fully expanded leaf from the top were compared with leaf nitrogen concentration. The results showed the 2/3 position on that leaf was most suitable for predicting the nitrogen status of rice. Based on these results, we recommend making SPAD measurements at the 2/3 position on the fourth fully expanded leaf from the top. The coupling of dynamic chlorophyll distribution and irregular leaf shapes information can provide a promising approach for the calibration of SPAD meter measurement, which can further benefit the in situ nitrogen management by providing reliable estimation of crops nitrogen nutrition status.