Pest Detection and Extraction Using Image Processing Techniques

Miranda, Johnny L.; Gerardo, Bobby D.; Tanguilig, Bartolome T.

doi:10.7763/ijcce.2014.v3.317

Cited by 87 publications

(30 citation statements)

References 3 publications

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“…Johnny et al (2014) utilized the strategies of image analysis are widely connected to horticultural science, and it gives most extreme assurance to crops, which can at last lead to all the more likely yield the board and generation. Checking of pest pervasion depends on labor, anyway programmed observing has been progressing so as to limit human endeavors and mistakes [3]. Ganesh et al 2013 It shows the joint effort of reciprocal controls and methods, which prompted a computerized, powerful and flexible framework [10].…”

Section: Existing Methodsmentioning

confidence: 99%

Identification of Pests on Plants Using Clustering and Hybrid Approaches

Karkuzhali¹

2019

IJEAT

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Enormous agricultural yield is lost each year, because of quick pervasion by pest and insects. A great deal of research is being done worldwide to recognize logical procedures for early discovery/identification of these bio-aggressors. In the past years, a few methodologies dependent on computerization and digital image processing have become known to address this issue. The greater part of the calculations focus on pest identification and location, restricted to a greenhouse environment. Likewise, they include a few complex computations to accomplish the equivalent. In this paper, we developed a unique algorithmic approach to isolate and distinguish pest utilizing clustering and hybrid approaches. The proposed method includes decreased computational complexity and pest detection in green house environment. The whitefly, a bio-aggressor which represents a risk to a huge number of harvests, was picked as the pest of enthusiasm for this paper. The calculation was tried for a few whiteflies influencing various leaves and an accuracy of 96% of whitefly recognition was accomplished.

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Section: Existing Methodsmentioning

confidence: 99%

Identification of Pests on Plants Using Clustering and Hybrid Approaches

Karkuzhali¹

2019

IJEAT

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“…Hence authors proposed LTSRG-algorithm for segmentation of image [3]. In cucumber leaf disease diagnosis, spectrum based algorithms are used [4].…”

Section: IImentioning

confidence: 99%

Identification of Disease in Leaves using Genetic Algorithm

Suganthy¹

2019

IJTSRD

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Plant disease is an impairment of normal state of a plant that interrupts or modifies its vital functions. Many leaf diseases are caused by pathogens. Agriculture is the mains try of the Indian economy. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases & quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes image processing techniques starting from image acquisition, preprocessing, testing, and training.

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“…In case of fixed camera as acquisition module it continuously captured the images and send to local machine on average it can send frames up to 24 per second. Sometimes this continuous flow of data may also a major problem in terms of storage [9]. If it is the case then go with Storage as a Service of Cloud technology.…”

Section: Data Centrementioning

confidence: 99%

A Methodology for Automatic Detection and Classification of Pests using Optimized SVM in Greenhouse Crops

Attada*¹,

Katta²

2019

IJEAT

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Digital revolution is taking place in every industry. The technologies namely Cloud Computing and the Internet of Things (IoT) are considered to be as a digital revolution. Comparatively with other industries Agriculture industry has less usage of these digital revolutionary technologies. In recent years Agriculture industry uses such type of digital revolution technologies to counterpart traditional practices which greatly influence the productivity. The IoT is set to push the future of farming to the next level by collecting the production data which includes weather and soil data, image data of crop, pests, etc. through internet enabled communication objects. Performing computation and providing advisory on this large scale of data that is collected by communication objects by Cloud Computing technology in terms of Leaf is point of interest which has infestation problem with biological organisms such as pests observed by naked eye is time consuming. We make use of digital revolution device like Unmanned Aerial Vehicle (UAV) which collects the data from user point of inter-est, Digital Image Processing techniques, Pattern recognition Algorithms for above stated problem to develop an advisory based cloud system which provides advisory based on detection of pests present on off-seasonal crops rose, lengthy type crops cucumber which are cultivated in new agricultural farming i.e. limited space structure namely Greenhouse.

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Pest Detection and Extraction Using Image Processing Techniques

Cited by 87 publications

References 3 publications

Identification of Pests on Plants Using Clustering and Hybrid Approaches

Identification of Pests on Plants Using Clustering and Hybrid Approaches

Identification of Disease in Leaves using Genetic Algorithm

A Methodology for Automatic Detection and Classification of Pests using Optimized SVM in Greenhouse Crops

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