Evaluation of optical sensor measurements of canopy reflectance and of leaf flavonols and chlorophyll contents to assess crop nitrogen status of muskmelon

2014 ◽  
Vol 58 ◽  
pp. 39-52 ◽  
Author(s):  
Francisco M. Padilla ◽  
M. Teresa Peña-Fleitas ◽  
Marisa Gallardo ◽  
Rodney B. Thompson
Keyword(s):  
Optical Sensor ◽  
Canopy Reflectance ◽  
Nitrogen Status ◽  
Chlorophyll Contents

ESTIMATION OF SOIL NITROGEN STATUS WITH CANOPY REFLECTANCE SPECTRA IN RICE

2006 ◽  
Vol 30 (4) ◽  
pp. 675-681 ◽  
Author(s):  
XUE Li_Hong ◽  
◽  
LU Ping ◽  
YANG Lin_Zhang ◽  
SHAN Yu_Hua ◽  
...  
Keyword(s):  
Soil Nitrogen ◽  
Reflectance Spectra ◽  
Canopy Reflectance ◽  
Nitrogen Status

scholarly journals Assessment of RGB Vegetation Indices to Estimate Chlorophyll Content in Sugar Beet Leaves in the Final Cultivation Stage

2020 ◽  
Vol 2 (1) ◽  
pp. 128-149 ◽  
Author(s):  
Luis Fernando Sánchez-Sastre ◽  
Nuno M. S. Alte da Veiga ◽  
Norlan Miguel Ruiz-Potosme ◽  
Paula Carrión-Prieto ◽  
José Luis Marcos-Robles ◽  
...  
Keyword(s):  
Sugar Beet ◽  
Chlorophyll Content ◽  
Vegetation Indices ◽  
Principal Component ◽  
Harvest Time ◽  
Light Conditions ◽  
Nitrogen Status ◽  
Chlorophyll Contents ◽  
Aerial Vehicle ◽  
Good Agreement

Estimation of chlorophyll content with portable meters is an easy way to quantify crop nitrogen status in sugar beet leaves. In this work, an alternative for chlorophyll content estimation using RGB-only vegetation indices has been explored. In a first step, pictures of spring-sown ‘Fernanda KWS’ variety sugar beet leaves taken with a commercial camera were used to calculate 25 RGB indices reported in the literature and to obtain 9 new indices through principal component analysis (PCA) and stepwise linear regression (SLR) techniques. The performance of the 34 indices was examined in order to evaluate their ability to estimate chlorophyll content and chlorophyll degradation in the leaves under different natural light conditions along 4 days of the canopy senescence period. Two of the new proposed RGB indices were found to improve the already good performance of the indices reported in the literature, particularly for leaves featuring low chlorophyll contents. The 4 best indices were finally tested in field conditions, using unmanned aerial vehicle (UAV)-taken photographs of a sugar beet plot, finding a reasonably good agreement with chlorophyll-meter data for all indices, in particular for I2 and (R−B)/(R+G+B). Consequently, the suggested RGB indices may hold promise for inexpensive chlorophyll estimation in sugar beet leaves during the harvest time, although a direct relationship with nitrogen status still needs to be validated.

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

Evaluating Wheat Nitrogen Status with Canopy Reflectance Indices and Discriminant Analysis

Crop Science ◽  
1995 ◽  
Vol 35 (5) ◽  
pp. 1400-1405 ◽  
Author(s):  
I. Filella ◽  
L. Serrano ◽  
J. Serra ◽  
J. Peñuelas
Keyword(s):  
Discriminant Analysis ◽  
Canopy Reflectance ◽  
Nitrogen Status ◽  
Reflectance Indices

Estimation of Nitrogen Status in Middle and Bottom Layers of Winter Wheat Canopy by Using Ground‐Measured Canopy Reflectance

2005 ◽  
Vol 36 (17-18) ◽  
pp. 2289-2302 ◽  
Author(s):  
Zhijie Wang ◽  
Jihua Wang ◽  
Liangyun Liu ◽  
Wenjiang Huang, ◽  
Chunjiang Zhao ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Canopy Reflectance ◽  
Nitrogen Status

Comparison of crop canopy reflectance sensors used to identify sugarcane biomass and nitrogen status

2014 ◽  
Vol 16 (1) ◽  
pp. 15-28 ◽  
Author(s):  
Lucas R. Amaral ◽  
José P. Molin ◽  
Gustavo Portz ◽  
Felipe B. Finazzi ◽  
Lucas Cortinove
Keyword(s):  
Canopy Reflectance ◽  
Crop Canopy ◽  
Nitrogen Status

scholarly journals On-The-Go Variable Rate Fertilizer Application Method for Rice Through Classification of Crop Nitrogen Nutrition Index (NNI)

2021 ◽  
Vol 15 ◽  
pp. 1-10
Author(s):  
Badril Hisham Abu Bakar ◽  
Jusnaini Muslimin ◽  
Muhammad Naim Fadzli Abd. Rani ◽  
Mohammad Aufa Mhd Bookeri ◽  
Mohd. Taufik Ahmad ◽  
...  
Keyword(s):  
Nitrogen Nutrition ◽  
Vegetation Indices ◽  
Nutrient Status ◽  
Fertilizer Application ◽  
Application Rate ◽  
Canopy Reflectance ◽  
Variable Rate ◽  
Nitrogen Status ◽  
Rice Plants ◽  
Rice Farmers

The standard practice among rice farmers in Malaysia is to apply fertilizer using a single application rate for the whole field. However, fertility conditions vary across the field. The excess use of fertilizer leads to increased input cost and can be damaging to the environment. The focus of this research was to develop a method to apply fertilizer on-the-go while sensing the crop nutrient status of rice plants. A machine learning approach was used to develop a crop nitrogen status prediction model. The model used spectral data from an active canopy reflectance sensor and several vegetation indices as inputs. The model was then incorporated into an on-the-go variable rate fertilizer application system. System performance was then evaluated in the field. The results from this work showed that the model had and accuracy of 83% in classifying the nitrogen status of the rice plants. The results also showed that our method was able to save up to 20% fertilizer use while maintaining yield. These findings are important for large estate farmers who are looking to increase productivity and efficiency.

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