Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging

Vesali, Farshad; Omid, Mahmoud; Kaleita, Amy L.; Mobli, Hossein

doi:10.1016/j.compag.2015.06.012

Cited by 97 publications

(68 citation statements)

References 34 publications

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“…Smartphones would help to make this possible, due to better and faster processors and sensors which turn them into handy meters ready for field use [5]. Since smartphones and free apps are available around the world today; this device might have applications beyond communications.…”

Section: Resultsmentioning

confidence: 99%

“…and Pérez, J.C., Fast estimation of chlorophyll content on plant leaves using the light sensor of a smartphone DYNA, 84(203), pp. 233-239, December, 2017. as cameras in smartphones [5] or from more sophisticated devices such as Google glasses, which send images through the internet to further receive chlorophyll related values from a remote server [6]. Because of the high correlation with nitrogen content, chlorophyll content is also used to address the nutritional status of N (nitrogen) in plants [7].…”

Section: Introductionmentioning

confidence: 99%

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Fast estimation of chlorophyll content on plant leaves using the light sensor of a smartphone

Pineda-Tobón¹,

Pérez²,

Gaviria-Palacio³

et al. 2017

DYNA

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Plant chlorophyll measurements can support nitrogen fertilization decisions. Using a 3D printed device and a red LED, here we tested the feasibility of using a smartphone ambient light sensor (ALS) to estimate leaf chlorophyll by light transmission. Its performance was evaluated by comparing 30 leaf sorghum (Sorghum bicolor (L.) Moench) transmission readings of the red LED (663 nm) obtained from the smartphone and a standard spectrometer, which showed a good coefficient of determination (r 2 = 0.9067). Additionally, a comparison between the ALS and a SPAD 502 TM (a commercial device for chlorophyll content estimation) was made with chrysanthemum (Dendranthema grandiflora Tzvelev) leaves, obtaining a good correlation between both measurements. Light transmission was also measured in S. bicolor flag leaves from plants growing in the greenhouse under increasing nitrogen fertilization. A clear fit between leaf light transmission and plant height was also observed, suggesting a simple, smartphone based estimation of plant's chlorophyll.Keywords: Plant nutrition; non-destructive chlorophyll meter; leaf light transmittance.Estimación rápida del contenido de clorofila en hojas usando el sensor de luz de un teléfono celular ResumenMedir clorofila puede apoyar decisiones de fertilización nitrogenada de plantas. Usando un dispositivo impreso en 3D y un LED rojo, aquí probamos la factibilidad de usar un teléfono celular para estimar clorofila por transmisión de luz. Al comparar 30 lecturas de trasmisión de luz roja (663 nm) por hojas de sorgo Sorghum bicolor (L.) Moench, obtenidas del teléfono y de un espectrómetro estándar, se encontró un buen coeficiente de determinación (r 2 = 0.9067). Adicionalmente, se hizo una comparación entre las lecturas del teléfono y un SPAD 502 TM (un dispositivo comercial para la estimación del contenido de clorofila) en hojas de crisantemo Dendranthema grandiflora Tzvelev, obteniendo una buena correlación entre ambas medidas. La transmisión medida en hojas de S. bicolor de plantas de invernadero fertilizadas con nitrógeno también presentó buen ajuste con la altura de las plantas, sugiriendo la posibilidad de estimar fácilmente el contenido de clorofila en hojas usando teléfonos inteligentes.Palabras clave: Nutrición vegetal; medición no destructiva de clorofila; transmisión de luz por hojas.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast estimation of chlorophyll content on plant leaves using the light sensor of a smartphone

Pineda-Tobón¹,

Pérez²,

Gaviria-Palacio³

et al. 2017

DYNA

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“…Features like mobility and processing capacity available on mobile devices have favored the development and the insertion of applications in many fields such as entertainment (Kurniati, Tanzil, & Purnomo, 2015), education (Chappel & Paliwal, 2014), health (Taylor, Abdulla, Helmer, Lee, & Blanchonette, 2011;Agarwal, Abhishek, Kumar, Prasad, & Singh, 2015) and agriculture (Gong, Yu, He, & Qiu, 2013;Lantzos et al, 2013;Sesma, Molina-Martínez, Cavas-Martínez, & Fernández-Pacheco, 2015;Vesali, Omid, Kaleita, & Mobli, 2015).…”

Section: Introductionmentioning

confidence: 99%

<b>Simtrac – an application for simulation of traction efficiency of agricultural tractors with front wheel assist

Santos

Queiroz

2016

Acta Sci. Technol.

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ABSTRACT. Process mechanization has contributed significantly to the increase of agricultural productivity, although the correct selection of machines must take into account a set of technical, administrative and economic parameters. Mathematical modeling of the soil and traction device interaction allows researchers and designers to analyze problems associated to traction performance in tractors and to optimize operational parameters. Mobile devices have currently dominated the technological market. Smartphones and tablets feature mobility and great processing capacity and these characteristics have favored the development and the insertion of applications in many fields. Current research developed an application to simulate the traction capacity of agricultural tractors with front wheel assist. The application was developed with Android platform, version 2.3 'Gingerbread'. The application is intuitive and easy to operate, besides showing great flexibility. It may be used in schools and in research to optimize agricultural operations. Concordance between simulated and observed results, obtained from validation process, demonstrated the prediction capacity of the traction efficiency of agricultural tractors. Further, it has been verified that input parameters associated to surface conditions are fundamental for the simulation of traction efficiency.Keywords: modeling, mechanization, mobile technology.Simtrac -um aplicativo para a simulação da eficiência de tração de tratores agrícolas com tração dianteira auxiliar RESUMO. A mecanização de processos tem contribuído significativamente para o aumento da produtividade agrícola. A correta seleção de máquinas deve considerar um conjunto de parâmetros de natureza técnica, administrativa e econômica. Modelagem matemática da interação solo e dispositivo tração possibilita a pesquisadores e projetistas analisar problemas associados ao desempenho de tração de tratores e otimizar parâmetros operacionais. Atualmente, os dispositivos móveis têm dominado o mercado de tecnologia. Celulares inteligentes (smartphones) e tablets possuem características como mobilidade e grande capacidade de processamento, o que têm favorecido o desenvolvimento e a inserção no mercado de aplicativos destinados a diversas áreas. Este trabalho foi realizado com o objetivo de desenvolver um aplicativo para simular a capacidade de tração de tratores agrícolas com tração dianteira auxiliar. Para o desenvolvimento dos aplicativos, empregou-se a plataforma Android versão 2.3 'Gingerbread'. O aplicativo desenvolvido apresentou grande flexibilidade, sendo intuitivo e de fácil operação, podendo ser empregado para otimização da execução de operações agrícolas, para pesquisa e educação. A boa concordância entre os resultados observados e simulados, no processo de validação, demonstrou a capacidade de predição da eficiência de tração de tratores agrícolas. Adicionalmente, verificou-se que os parâmetros de entrada associados às condições de superfície são fundamentais para a simulação da capacidade de tração.Pal...

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“…Selected features were applied to generate and use ANNs. Among various available types of ANNs, a multilayer perceptron (MLP) was used as a general structure in the prediction problems(Vesali, Omid, Kaleita, & Mobli, 2015). The structure of MLP networks include of three layers; the input layer, hidden layer(s), and an output layer.…”

mentioning

confidence: 99%

A nondestructive intelligent approach to real‐time evaluation of chicken meat freshness based on computer vision technique

Taheri‐Garavand

Fatahi

Shahbazi

et al. 2019

J Food Process Engineering

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In this study, the capability of a procedure based on combination of computer vision (CV) and artificial intelligence techniques examined for intelligent and nondestructive prediction of chicken meat freshness during the spoilage process at 4°C. The proposed system comprises the following stages: capture images, image preprocessing, image processing, computing channels, feature extraction, feature selection by a hybrid of genetic algorithm (GA) and artificial neuronal network (ANN), and prediction by using ANN. The number of neurons in input layer was determined 33 (selected features) and freshness used as the output. The ideal ANN model was obtained with 33‐10‐1 topology. The high performance of the model was provided with a correlation coefficient of 0.98734 and MSE of 0.002045. The encouraging results of the current study obviously indicated the high potential of CV‐based system combined with an intelligence method as a smart, nondestructive, and reliable technique for online evaluation of chicken meat freshness. Practical application Diagnosis and estimation of chicken meat freshness are considered a significant concern in meat quality for consumers. Computer Vision as a novel nondestructive technique can be utilized to evaluate the quality of products. We present the potential of computer vision‐based method as a smart, nondestructive, and reliable method for online prediction of the freshness of chicken meat.

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Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging

Cited by 97 publications

References 34 publications

Fast estimation of chlorophyll content on plant leaves using the light sensor of a smartphone

Fast estimation of chlorophyll content on plant leaves using the light sensor of a smartphone

<b>Simtrac – an application for simulation of traction efficiency of agricultural tractors with front wheel assist

A nondestructive intelligent approach to real‐time evaluation of chicken meat freshness based on computer vision technique

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