scholarly journals Effect of Cultivar on Chlorophyll Meter and Canopy Reflectance Measurements in Cucumber

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 509 ◽  
Author(s):  
Romina de Souza ◽  
Rafael Grasso ◽  
M. Teresa Peña-Fleitas ◽  
Marisa Gallardo ◽  
Rodney B. Thompson ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Optical Sensor ◽  
Chlorophyll Concentration ◽  
Vegetation Indices ◽  
Canopy Reflectance ◽  
Cucumis Sativus L ◽  
N Content ◽  
Chlorophyll Meter ◽  
Leaf N Content ◽  
Leaf N

Optical sensors can be used to assess crop N status to assist with N fertilizer management. Differences between cultivars may affect optical sensor measurement. Cultivar effects on measurements made with the SPAD-502 (Soil Plant Analysis Development) meter and the MC-100 (Chlorophyll Concentration Meter), and of several vegetation indices measured with the Crop Circle ACS470 canopy reflectance sensor, were assessed. A cucumber (Cucumis sativus L.) crop was grown in a greenhouse, with three cultivars. Each cultivar received three N treatments, of increasing N concentration, being deficient (N1), sufficient (N2) and excessive (N3). There were significant differences between cultivars in the measurements made with both chlorophyll meters, particularly when N supply was sufficient and excessive (N2 and N3 treatments, respectively). There were no consistent differences between cultivars in vegetation indices. Optical sensor measurements were strongly linearly related to leaf N content in each of the three cultivars. The lack of a consistent effect of cultivar on the relationship with leaf N content suggests that a unique equation to estimate leaf N content from vegetation indices can be applied to all three cultivars. Results of chlorophyll meter measurements suggest that care should be taken when using sufficiency values, determined for a particular cultivar

scholarly journals Relationship between vegetation indices and agronomic performance of maize varieties under different nitrogen rates

2020 ◽  
Vol 36 (5) ◽  
Author(s):  
Marcela da Silva Flores ◽  
Willian Meniti Paschoalete ◽  
Fabio Henrique Rojo Baio ◽  
Cid Naudi Silva Campos ◽  
Ariane de Andréa Pantaleão ◽  
...  
Keyword(s):  
Plant Height ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Block Design ◽  
Vegetation Indices ◽  
Agronomic Performance ◽  
N Content ◽  
Maize Varieties ◽  
Leaf N Content ◽  
Leaf N

Precision agriculture is a set of techniques that assist the monitoring of the agronomic performance of the maize crop by using vegetation indices. This study aimed to verify the relationship between vegetation indices, plant height, leaf N content, and grain yield of three maize varieties, grown under high and low N as topdressing. The experiment was carried out at the Fundação de Apoio à Pesquisa Agropecuária de Chapadão (Fundação Chapadão), located in the municipality of Chapadão do Sul, during the 2017/2018 season. The experiment consisted of a randomized block design with four replications, arranged in a 3x2 split-plot scheme. The first factor (plots) corresponded to three open-pollinated maize varieties (BRS 4103, BRS Gorotuba, and SCS 154), and the second factor (subplots) consisted of two N rates applied as topdressing (80 and 160 kg- 1). All the evaluated variables showed varieties x N interaction. Vegetation indices in maize varieties were influenced by the N rate applied as topdressing. Normalized Difference Vegetation Index (NDVI) and Soil-adjusted Vegetation Index (SAVI) showed a higher correlation with plant height. At the same time, Normalized Difference Red Edge (NDRE) had a stronger association with leaf N content.

scholarly journals Field Assessment of Macronutrients and Nitrogen in Apple Leaves Using a Chlorophyll Meter

2019 ◽  
Vol 29 (3) ◽  
pp. 300-307 ◽  
Author(s):  
Youngsuk Lee ◽  
Hun Joong Kweon ◽  
Moo-Yong Park ◽  
Dongyong Lee
Keyword(s):  
Nutrient Status ◽  
Nutrient Content ◽  
N Content ◽  
Chlorophyll Meter ◽  
Content Assessment ◽  
Growing Period ◽  
Leaf Chlorophyll ◽  
Nutrient Profiles ◽  
Leaf N Content ◽  
Leaf N

Nutrient content assessment of plant tissues is widely performed by farmers to determine the appropriate amount of fertilization to use for their crops. A nondestructive leaf chlorophyll meter is one of the most commonly used devices for performing field assessments of the nutrient status of leaves. However, it is challenging to use a chlorophyll meter to assess the nutritional status of perennial plants, such as the apple (Malus ×domestica) tree, because of the difficulty estimating nitrogen (N) during the entire growing period. We compared the chlorophyll meter readings with leaf nutrient profiles collected from young ‘Arisoo’/M.9 apple trees throughout the growing period. A significant positive correlation between the chlorophyll meter readings and leaf N content was found from May to August during the midseason. Regression analysis indicated that the best sampling time for predicting the foliar N content of apple tress is from late June to late July. This result suggests that a reliable leaf N assessment can be performed in a rapid, nondestructive way in apple orchards.

Comparison of Machine Learning Methods for Leaf Nitrogen Estimation in Corn Using Multispectral UAV Images

2021 ◽  
Vol 64 (6) ◽  
pp. 2089-2101
Author(s):  
Razieh Barzin ◽  
Hamid Kamangir ◽  
Ganesh C. Bora
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Vegetation Indices ◽  
Leaf Nitrogen ◽  
Gradient Boosting ◽  
N Content ◽  
Spectral Bands ◽  
Red Edge ◽  
Leaf N Content ◽  
Leaf N

HighlightsLeaf nitrogen percentage in corn was estimated using various vegetation indices derived from UAVs.Eight machine learning methods were compared to find the most accurate model for nitrogen estimation.The most influential vegetation indices were determined for estimation of leaf nitrogen.Abstract. Nitrogen (N) is the most critical component of healthy plants. It has a significant impact on photosynthesis and plant reproduction. Physicochemical characteristics of plants such as leaf N content can be estimated spatially and temporally because of the latest developments in multispectral sensing technology and machine learning (ML) methods. The objective of this study was to use spectral data for leaf N estimation in corn to compare different ML models and find the best-fitted model. Moreover, the performance of vegetation indices (VIs) and spectral wavelengths were compared individually and collectively to determine if combinations of VIs substantially improved the results as compared to the original spectral data. This study was conducted at a Mississippi State University corn field that was divided into 16 plots with four different N treatments (0, 90, 180, and 270 kg ha-1). The bare soil pixels were removed from the multispectral images, and 26 VIs were calculated based on five spectral bands: blue, green, red, red-edge, and near-infrared (NIR). The 26 VIs and five spectral bands obtained from a red-edge multispectral sensor mounted on an unmanned aerial vehicle (UAV) were analyzed to develop ML models for leaf %N estimation of corn. The input variables used in these models had the most impact on chlorophyll and N content and high correlation with leaf N content. Eight ML algorithms (random forest, gradient boosting, support vector machine, multi-layer perceptron, ridge regression, lasso regression, and elastic net) were applied to three different categories of variables. The results show that gradient boosting and random forest were the best-fitted models to estimate leaf %N, with about an 80% coefficient of determination for the different categories of variables. Moreover, adding VIs to the spectral bands improved the results. The combination of SCCCI, NDRE, and red-edge had the largest coefficient of determination (R2) in comparison to the other categories of variables used to predict leaf %N content in corn. Keywords: Corn, Gradient boosting, Machine learning, Multispectral imagery, Nitrogen estimation, Random forest, UAV, Vegetation index.

scholarly journals Use of an Active Canopy Sensor and SPAD Chlorophyll Meter to Quantify Geranium Nitrogen Status

HortScience ◽  
2012 ◽  
Vol 47 (1) ◽  
pp. 45-50 ◽  
Author(s):  
Yun-wen Wang ◽  
Bruce L. Dunn ◽  
Daryl B. Arnall ◽  
Pei-sheng Mao
Keyword(s):  
Green Leaf ◽  
Fertilizer Treatment ◽  
N Content ◽  
Chlorophyll Meter ◽  
N Concentration ◽  
Flower Traits ◽  
Leaf N Content ◽  
N Rates ◽  
N Status ◽  
Leaf N

This research was conducted to investigate the potentials of normalized difference vegetation index (NDVI), a Soil-Plant Analyses Development (SPAD) chlorophyll meter, and leaf nitrogen (N) concentration [% dry matter (DM)] for rapid determination of N status in potted geraniums (Pelargonium ×hortorum). Two F1 cultivars were chosen to represent a dark-green leaf cultivar, Horizon Deep Red, and a light-green leaf cultivar, Horizon Tangerine, and were grown in a soilless culture system. All standard 6-inch (15.24-cm) pots filled with a medium received an initial top-dress application of 5 g controlled-release fertilizer (15N–9P–12K), then plants were supplemented with additional N in the form of urea at 0, 50, 100, or 200 mg·L−1 N every few days to produce plants ranging from N-deficient to N-sufficient. The NDVI readings of individual plants from a NDVI pocket sensor developed by Oklahoma State University were collected weekly until the flowering stage. Data on flower traits, including number of pedicels (NOP), number of full umbels per pot (NOFU), total number of flowers per pot (TNF), number of flowers per pedicel (NOF), and inflorescences diameter (IFD), were collected 3 months after initial fertilizer treatment. After measuring flower traits, pedicels were removed from each pot, and SPAD value, NDVI, and leaf N concentration (g·kg−1 DM) were measured simultaneously. Cultivar and N rate significantly affected all but two flower and one N status parameters studied. The coefficient of determination R2 showed that NOP, NOFU, and TNF traits were more related to the N rates and the status parameters studied for ‘Horizon Tangerine’ than for ‘Horizon Deep Red’. For the latter cultivar, NOP and TNF traits were highly related to NDVI and SPAD values than N rates and leaf N content parameters. Correlation analysis indicated that the NDVI readings (R2 = 0.848 and 0.917) and SPAD values (R2 = 0.861 and 0.950) were significantly related to leaf N content (g·kg−1 DM) between cultivars. However, sensitivity of the NDVI and chlorophyll values to N application rate in geranium was slightly less than leaf N content. Strong correlations (R2 = 0.974 and 0.979, respectively) between NDVI and SPAD values were found within cultivars. The results demonstrated NDVI and SPAD values can be used to estimate N status in geranium. Because the pocket NDVI sensor will be cheaper than the SPAD unit, it has an advantage in determining N content in potted ornamentals.

scholarly journals Nitrogen variable rate in pastures using optical sensors

2019 ◽  
Vol 40 (6Supl2) ◽  
pp. 2917 ◽  
Author(s):  
Lucas de Paula Corrêdo ◽  
Francisco de Assis de Carvalho Pinto ◽  
Domingos Savio Queiroz ◽  
Domingos Sárvio Magalhães Valente ◽  
Flora Maria de Melo Villar
Keyword(s):  
Optical Sensors ◽  
Precision Agriculture ◽  
Low Cost ◽  
Vegetation Indices ◽  
Digital Camera ◽  
Dry Mass ◽  
Chlorophyll Meter ◽  
Randomized Design ◽  
Leaf N Content ◽  
N Management

The use of optical sensors to identify the nutritional needs of agricultural crops has been the subject of several studies using precision agriculture techniques. In this work, we sought to overcome the lack of research evaluating the use of these techniques in the management of nitrogen (N) fertilizer in pastures. We evaluated the methodology of the nitrogen sufficiency index (NSI) in N management at variable rates (VR) using a portable chlorophyll meter. In addition, the use of color vegetation indices generated from a digital camera was evaluated as a low-cost alternative. The work was conducted in four management cycles at different times of year, evaluating the productivity and quality of Brachiaria brizantha cv. Xaraés grass. Three NSIs (0.85, 0.90 and 0.95) were evaluated, applying complementary doses of N according to the response of monitored plots using a chlorophyll meter and comparing the productivity and leaf N content of these treatments to the reference treatment (TREF), which received a single dose of N (150 kg ha-1). Together with these treatments, plots without N application (control) were analyzed, totaling five treatments with six replications in a completely randomized design. The dry mass productivity and N leaf concentration of the VR treatments were statistically equal to TREF in all management cycles (P < 0.05). Most color vegetation indices correlated significantly (P < 0.05) to the chlorophyll readings. The use of NSI methodology in pastures allows the same productivity gains, with significant input savings. In addition, the use of digital cameras presents itself as a viable alternative to monitoring the N status in pastures.

scholarly journals A novel approach for nitrogen diagnosis of wheat canopies digital images by mobile phones based on histogram

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Qi ◽  
Yanan Zhao ◽  
Yufang Huang ◽  
Yang Wang ◽  
Wei Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Phones ◽  
Mean Value ◽  
Wheat Cultivars ◽  
N Content ◽  
Color Parameters ◽  
The Neural Network ◽  
The Mean ◽  
Leaf N Content ◽  
Leaf N

AbstractThe accurate and nondestructive assessment of leaf nitrogen (N) is very important for N management in winter wheat fields. Mobile phones are now being used as an additional N diagnostic tool. To overcome the drawbacks of traditional digital camera diagnostic methods, a histogram-based method was proposed and compared with the traditional methods. Here, the field N level of six different wheat cultivars was assessed to obtain canopy images, leaf N content, and yield. The stability and accuracy of the index histogram and index mean value of the canopy images in different wheat cultivars were compared based on their correlation with leaf N and yield, following which the best diagnosis and prediction model was selected using the neural network model. The results showed that N application significantly affected the leaf N content and yield of wheat, as well as the hue of the canopy images and plant coverage. Compared with the mean value of the canopy image color parameters, the histogram could reflect both the crop coverage and the overall color information. The histogram thus had a high linear correlation with leaf N content and yield and a relatively stable correlation across different growth stages. Peak b of the histogram changed with the increase in leaf N content during the reviving stage of wheat. The histogram of the canopy image color parameters had a good correlation with leaf N content and yield. Through the neural network training and estimation model, the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the estimated and measured values of leaf N content and yield were smaller for the index histogram (0.465, 9.65%, and 465.12, 5.5% respectively) than the index mean value of the canopy images (0.526, 12.53% and 593.52, 7.83% respectively), suggesting a good fit for the index histogram image color and robustness in estimating N content and yield. Hence, the use of the histogram model with a smartphone has great potential application in N diagnosis and prediction for wheat and other cereal crops.

scholarly journals Effects of ontogenetic stage and leaf age on leaf functional traits and the relationships between traits in Pinus koraiensis

2021 ◽  
Author(s):  
Meng Ji ◽  
Guangze Jin ◽  
Zhili Liu
Keyword(s):  
Significant Positive Correlation ◽  
Leaf Age ◽  
Leaf Traits ◽  
Ontogenetic Stage ◽  
Pinus Koraiensis ◽  
Mature Trees ◽  
N Content ◽  
Ontogenetic Stages ◽  
Leaf N Content ◽  
Leaf N

AbstractInvestigating the effects of ontogenetic stage and leaf age on leaf traits is important for understanding the utilization and distribution of resources in the process of plant growth. However, few studies have been conducted to show how traits and trait-trait relationships change across a range of ontogenetic stage and leaf age for evergreen coniferous species. We divided 67 Pinus koraiensis Sieb. et Zucc. of various sizes (0.3–100 cm diameter at breast height, DBH) into four ontogenetic stages, i.e., young trees, middle-aged trees, mature trees and over-mature trees, and measured the leaf mass per area (LMA), leaf dry matter content (LDMC), and mass-based leaf nitrogen content (N) and phosphorus content (P) of each leaf age group for each sampled tree. One-way analysis of variance (ANOVA) was used to describe the variation in leaf traits by ontogenetic stage and leaf age. The standardized major axis method was used to explore the effects of ontogenetic stage and leaf age on trait-trait relationships. We found that LMA and LDMC increased significantly and N and P decreased significantly with increases in the ontogenetic stage and leaf age. Most trait-trait relationships were consistent with the leaf economic spectrum (LES) at a global scale. Among them, leaf N content and LDMC showed a significant negative correlation, leaf N and P contents showed a significant positive correlation, and the absolute value of the slopes of the trait-trait relationships showed a gradually increasing trend with an increasing ontogenetic stage. LMA and LDMC showed a significant positive correlation, and the slopes of the trait-trait relationships showed a gradually decreasing trend with leaf age. Additionally, there were no significant relationships between leaf N content and LMA in most groups, which is contrary to the expectation of the LES. Overall, in the early ontogenetic stages and leaf ages, the leaf traits tend to be related to a "low investment-quick returns" resource strategy. In contrast, in the late ontogenetic stages and leaf ages, they tend to be related to a "high investment-slow returns" resource strategy. Our results reflect the optimal allocation of resources in Pinus koraiensis according to its functional needs during tree and leaf ontogeny.

scholarly journals Variation of nitric oxide emission potential in plants: a possible link to leaf N content and net photosynthetic activity

2014 ◽  
Vol 8 (3) ◽  
pp. 313-320 ◽  
Author(s):  
Juan Chen ◽  
Chao Wang ◽  
Fei-Hua Wu ◽  
Wen-Hua Wang ◽  
Ting-Wu Liu ◽  
...  
Keyword(s):  
Nitric Oxide ◽  
Photosynthetic Activity ◽  
N Content ◽  
Nitric Oxide Emission ◽  
Leaf N Content ◽  
Leaf N

scholarly journals Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hiroto Yamashita ◽  
Rei Sonobe ◽  
Yuhei Hirono ◽  
Akio Morita ◽  
Takashi Ikka
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Visible Region ◽  
Machine Learning Algorithms ◽  
Hyperspectral Reflectance ◽  
N Content ◽  
Green Leaves ◽  
Leaf N Content ◽  
N Status ◽  
Leaf N

Abstract Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problematic. Previously, hyperspectral indices for chlorophyll (Chl) estimation, namely a green peak and red edge in the visible region, have been identified and used for N estimation because leaf N content closely related to Chl content in green leaves. Herein, datasets of N and Chl contents, and visible and near-infrared hyperspectral reflectance, derived from green leaves under various N nutrient conditions and albino yellow leaves were obtained. A regression model was then constructed using several machine learning algorithms and preprocessing techniques. Machine learning algorithms achieved high-performance models for N and Chl content, ensuring an accuracy threshold of 1.4 or 2.0 based on the ratio of performance to deviation values. Data-based sensitivity analysis through integration of the green and yellow leaves datasets identified clear differences in reflectance to estimate N and Chl contents, especially at 1325–1575 nm, suggesting an N content-specific region. These findings will enable the nondestructive estimation of leaf N content in tea plants and contribute advanced indices for nondestructive tracking of N status in crops.

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