Novel algorithm for segmentation and automatic identification of pests on plants using image processing

Huddar, Sushma; Gowri, S.; Keerthana, K.; Vasanthi, S.; Rupanagudi, Sudhir Rao

doi:10.1109/icccnt.2012.6396012

Cited by 49 publications

(22 citation statements)

References 4 publications

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“…It is due to this fact that a lot of money is spent each year by governments across the globe, for utilizing new technologies, discovering new methodologies of farming and also new techniques for prevention of crop reduction due to pests, natural calamities and drought [2], [3]. It is also a well known fact that the production of a good quality yield not only benefits consumers, who primarily form the top end of the food chain, but also economically benefits the farmer who toils hard to produce such good yield.…”

Section: Introductionmentioning

confidence: 99%

A cost effective tomato maturity grading system using image processing for farmers

Rupanagudi¹,

Ranjani²,

Nagaraj³

et al. 2014

2014 International Conference on Contemporary Computing and Informatics (IC3I)

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Maturity grading or in other words classifying the ripeness of a fruit, based on its color or texture, forms a very important process to be carried out by agriculturists and the food processing industry worldwide. Current techniques mainly involve manual inspection, which leads to erroneous classification, which in turn would cause economical losses due to inferior produce entering the market chain. A loss of yield during storage may also occur with this type of classification, since it would lead to wrong expiry date predictions as well. Several methodologies to automate this process exist but involve highly expensive setups and complicated procedures which are not a viable solution, especially for the agriculturists of a developing nation. In this paper we discuss a cost effective maturity grading system for one of the most popular fruit in the world -the tomato. A novel setup utilizing inexpensive material and image processing algorithms to identify the six important stages of tomato ripening have been presented. All algorithms were designed and developed using Simulink, a part of MATLAB 2011b on a 2.5 GHz CPU. An overall 98% accuracy was achieved with respect to maturity grade detection and an execution speed of greater than 7.6 times was obtained in comparison with two other popular methodologies.

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Section: Introductionmentioning

confidence: 99%

A cost effective tomato maturity grading system using image processing for farmers

Rupanagudi¹,

Ranjani²,

Nagaraj³

et al. 2014

2014 International Conference on Contemporary Computing and Informatics (IC3I)

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“…For a progressively developing nation such as India, agriculture is one of the most important sources of income to the country [1]. It must be noted that agriculture alone constitutes almost 22% of the country's Gross Domestic Product (GDP), which is indeed a significant number [2].…”

Section: Introductionmentioning

confidence: 99%

A novel cloud computing based smart farming system for early detection of borer insects in tomatoes

Rupanagudi¹,

Ranjani²,

Nagaraj³

et al. 2015

2015 International Conference on Communication, Information &Amp; Computing Technology (ICCICT)

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Every year farmers experience huge losses due to pest infestation in crops & this inturn impacts his livelihood. In this paper we discuss a novel approach to solve this problem by constantly monitoring crops using video processing, cloud computing and robotics. The paper concentrates in methodologies to detect pests in one of the most popular fruits in the world -the tomato. An insight into how the idea of the Internet of Things can also be conceptualized in this project has been elaborated.

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“…Por este fato, empresas buscam investir em tecnologias que possam otimizar processos na indústria agrícola e também reduzir as pragas nas plantações de frutos (HUDDAR et al, 2012).…”

Section: Introductionunclassified

Estimativa De Peso Do Melão Amarelo Através De Visão Computacional

Neto¹,

Calixto²,

Júnior³

et al. 2017

Anais Do IV Inovagri International Meeting - 2017

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RESUMO:Estudou-se uma forma para estimar o peso dos frutos de melão amarelo por meio de técnicas de Visão Computacional (VC). A estimativa de peso foi baseada na correlação entre o peso real e a segmentação da área do melão. Para isso, a escala da imagem de entrada do melão foi determinada e, em seguida, um filtro de cores e segmentação por contornos foram aplicadas nessas imagens. Para a realização deste trabalho, foram utilizados 65 melões. Dentre estes, 45 frutos foram utilizados para gerar as equações de estimativa de peso, e o restante, 20 imagens de diferentes melões, para a realização dos testes. A melhor correlação obtida entre o peso e a área do melão foi de 0,969. Os resultados mostraram um erro médio de 0,143 (kg) e desvio padrão de 0,146 (kg), para a estimativa do peso do melão. Através de ajustes, nas técnicas implementadas, existe a possibilidade que o sistema proposto seja adaptado para dispositivos móveis ou em sistemas embarcados. Com isso, o sistema pode utilizado, como exemplo, para estimar o peso do melão antes da colheita do fruto. PALAVRAS-CHAVE:Cucumis melo L., estimativa de peso, Processamento Digital de Imagens. ESTIMATED WEIGHT OF YELLOW MELON THROUGH COMPUTER VISIONABSTRACT: We studied a way to estimate the weight of yellow melon fruits by means of Computational Vision (VC) techniques. The weight estimate was based on the correlation between the actual weight and the segmentation of the melon area. For this, the input image scale of the melon was determined and then a color filter and contour segmentation were applied to these images. For this work, 65 melons were used. Among these, 45 fruits were used to 1 Doutor, Professor efetivo do IFCE -Sobral, Ceará.

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Novel algorithm for segmentation and automatic identification of pests on plants using image processing

Cited by 49 publications

References 4 publications

A cost effective tomato maturity grading system using image processing for farmers

A cost effective tomato maturity grading system using image processing for farmers

A novel cloud computing based smart farming system for early detection of borer insects in tomatoes

Estimativa De Peso Do Melão Amarelo Através De Visão Computacional

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