Disease Prediction in Plants: An Application of Machine Learning in Agriculture Sector

Shah, Zankhana; Vania, Ravi; Vegad, Sudhir

doi:10.1007/978-981-15-4474-3_11

Cited by 1 publication

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“…A deep learning methodology is followed and achieved an accuracy of 87%. Shah et al, (2021), illustrated a model utilizing image processing techniques such as segmentation, feature extraction and disease classification to detect the infections affecting the plants. Cotton leaves images are considered for the process.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques in Plant Disease Detection and Classification – A State of the Art

John

Arul

2021

INMATEH

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As we belong to a developing country, the agricultural importance is a known criterion. Majority of the Indians depend on agriculture for their basic living. It also serves as the backbone of the Indian economy. Therefore this sector should be considered important and taken care of. Diseases affecting the plants and pest are the two major threats of agriculture production. Naked eye observation followed by the addition of chemical fertilizers is the traditional method adopted by most of the farmers to avoid plant diseases. But the main limitation to this method is that it works only in the case of small scale farming. In order to tackle this issue many automatic plant disease detection systems have been developed from the early 70s. This paper is intended to survey some of the existing works in plant disease recognition that include various procedures, materials and approaches. They use different machine learning algorithms, image processing techniques and deep learning methods for disease detection. This paper also compares and suggests novel methods to recognize and classify the various kinds of infections affecting agricultural plants.

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Section: Introductionmentioning

confidence: 99%