Color Analysis of Leaf Images of Deficiencies and Excess Nitrogen Content in Soybean Leaves

Ma, Lili; Fang, Junlong; Chen, Yuehua; Gong, Shuipeng

doi:10.1109/iceee.2010.5661235

Cited by 11 publications

(12 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Other trends that aim to reduce cost and processing time have been previously proposed by researchers [ 20 , 21 , 22 , 23 , 24 , 25 ] who have used images with visible spectrum (300–700 nm) information and a single RGB (Red-Green-Blue) color space camera to determine the correlation between chlorophyll and nitrogen content. Several of these approaches have been tested in controlled conditions, and are not well-suited for field conditions.…”

Section: Introductionmentioning

confidence: 99%

Optical Method for Estimating the Chlorophyll Contents in Plant Leaves

Pérez-Patricio

Camas-Anzueto

Sanchez-Alegría

et al. 2018

Sensors

View full text Add to dashboard Cite

This work introduces a new vision-based approach for estimating chlorophyll contents in a plant leaf using reflectance and transmittance as base parameters. Images of the top and underside of the leaf are captured. To estimate the base parameters (reflectance/transmittance), a novel optical arrangement is proposed. The chlorophyll content is then estimated by using linear regression where the inputs are the reflectance and transmittance of the leaf. Performance of the proposed method for chlorophyll content estimation was compared with a spectrophotometer and a Soil Plant Analysis Development (SPAD) meter. Chlorophyll content estimation was realized for Lactuca sativa L., Azadirachta indica, Canavalia ensiforme, and Lycopersicon esculentum. Experimental results showed that—in terms of accuracy and processing speed—the proposed algorithm outperformed many of the previous vision-based approach methods that have used SPAD as a reference device. On the other hand, the accuracy reached is 91% for crops such as Azadirachta indica, where the chlorophyll value was obtained using the spectrophotometer. Additionally, it was possible to achieve an estimation of the chlorophyll content in the leaf every 200 ms with a low-cost camera and a simple optical arrangement. This non-destructive method increased accuracy in the chlorophyll content estimation by using an optical arrangement that yielded both the reflectance and transmittance information, while the required hardware is cheap.

show abstract

Section: Introductionmentioning

confidence: 99%

Optical Method for Estimating the Chlorophyll Contents in Plant Leaves

Pérez-Patricio

Camas-Anzueto

Sanchez-Alegría

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Se capturó una imagen de cada una de las plantas del experimento a una distancia constante (50 cm). Posteriormente las imágenes fueron cargadas en el programa R Statistics (R Core Team, 2018), por medio de la paquetería reportada por Ma et al (2010). Primero las imágenes fueron descompuestas en canales RGB, pertenecientes a los espacios del color del mismo nombre y se descartaron los pixeles que no correspondían a la planta, posteriormente se obtuvieron los promedios RGB.…”

Section: Variables Evaluadasunclassified

Uso de imágenes digitales para evaluar la respuesta colorimétrica de plantas de lisianthus, en soluciones nutritivas en sistemas hidropónicos

Rodríguez-Serrano¹,

Lara-Viveros

Sánchez-Herrera

et al. 2020

Terra

View full text Add to dashboard Cite

Lisianthus es una planta ornamental, que en los últimos años adquirió una gran importancia en el mercado nacional e internacional por su belleza y variedad de colores, este cultivo tradicionalmente se establece en suelo y hay poca información en relación con sus hábitos de crecimiento y desarrollo en sistemas hidropónicos. El presente trabajo tuvo como objetivo evaluar por medio de imágenes digitales el cambio en el color de las hojas y de las flores en el cultivo de lisianthus en hidroponía con diferentes soluciones nutritivas. Las soluciones nutritivas (SN) ensayadas en el cultivo fueron cuatro, como testigo fue la solución Steiner (SN1) y el resto de los tratamientos fueron soluciones modificadas, en la (SN2) se utilizó el doble de la concentración de la solución Steiner, mientras que la (SN3) fue adicionada con una cantidad elevada de K y la SN4 con una mayor cantidad de calcio (18 mEq L-1), se evaluó altura y número de hojas obteniendo en ambos una diferencia significativa en la SN1 con respecto a la SN4, en cuanto a las características fenotípicas de la hoja y flor mediante los valores de RGB el color de la hoja no se vio afectada por los tratamientos a partir de las modificaciones que se le realizaron a la solución Steiner, la variable R solo presentó diferencia significativa en la SN1 con respecto a la SN4 con mayor cantidad de calcio, sin embargo no ocurrió lo mismo para las demás variables y solo este fenómeno se presentó en el día 61 DDT, en cuanto al efecto de la solución nutritiva sobre el color de las flores, se encontró una diferencia significativa en las variables R en la solución 1 (Steiner) G hubo diferencia en la solución con concentraciones altas de potasio lo mismo ocurrió para las variables artif iciales utilizadas.

show abstract

“…In previous work, there exist some research for nitrogen and calcium estimations. In Ma et al, RGB images from 4 plants groups, each group with a different nitrogen level (0%, 50%, 100%, and 150% of the required daily dose) were captured at 5 different growth states (10, 20, 30, 40, and 50 days). Plant leaves were placed on a white surface.…”

Section: Related Workmentioning

confidence: 99%

“…Then, calcium deficiency is computed via statistical computations. In both cases, Ma et al and Story et al, the use of a single RGB camera allowed for a compact system design and low cost, but accuracy is low.…”

Section: Related Workmentioning

confidence: 99%

An FPGA‐based smart camera for accurate chlorophyll estimations

Pérez-Patricio

Aguilar-González

Camas-Anzueto

et al. 2018

Circuit Theory & Apps

View full text Add to dashboard Cite

In this work, a new chlorophyll estimation approach based on the reflectance/ transmittance from the leaf being analyzed is proposed. First, top/underside images from the leaf under analysis are captured, then, the base parameters (reflectance/transmittance) are extracted. Finally, a double-variable linear regression model estimates the chlorophyll content. To estimate the base parameters, a novel optical arrangement is presented. On the other hand, in order to provide a portable device suitable for chlorophyll estimation under large scale food crops, we have implemented our optical arrangement and our algorithmic formulation inside an field-programmable gate array (FPGA)-based smart camera fabric. Experimental results demonstrated that the proposed approach outperforms (in terms of accuracy and processing speed) most previous vision-based approaches, reaching more than 97% accuracy and delivering fast chlorophyll estimations (near 5 ms per estimation).

show abstract

Color Analysis of Leaf Images of Deficiencies and Excess Nitrogen Content in Soybean Leaves

Cited by 11 publications

References 1 publication

Optical Method for Estimating the Chlorophyll Contents in Plant Leaves

Optical Method for Estimating the Chlorophyll Contents in Plant Leaves

Uso de imágenes digitales para evaluar la respuesta colorimétrica de plantas de lisianthus, en soluciones nutritivas en sistemas hidropónicos

An FPGA‐based smart camera for accurate chlorophyll estimations

Contact Info

Product

Resources

About