A Novel Method of Butterfly Optimization Algorithm for Load Balancing in Cloud Computing

Yadav, Priya; Kumar, Sunil; Saini, Dilip Kumar Jang Bahadur

doi:10.17762/ijritcc.v10i8.5683

Cited by 11 publications

(1 citation statement)

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“…[32]. • Pre-processing and detection of plant leaf diseases ( in Pomegranate , wheat &Brinjal) using neural network, image processing and K-means clustering methods [33,34,35]. • Early detection of potato plant leaves by SVM and machine learning [36].…”

Section: Challenges and Future Directionmentioning

confidence: 99%

A Survey on the State of Art Approaches for Disease Detection in Plants

Sharma

Yadav²,

Saini

2022

IJRITCC

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Agriculture is the main factor for economy and contributes to GDP. The growth of the economy of many countries is based on agriculture. As a result, the yield factor, quality and volume of agricultural products, play a critical role in economic development. Plant diseases and pests have become a major determinant of crop yields throughout the years, as such illnesses in plants offer a serious threat and impediment to higher yields or production in the agriculture industry. As a result, From the outset, it becomes the major duty to correctly monitor the plants, to detect diseases thoroughly, and to determine methods of controlling or monitoring these plant diseases pests in order to achieve a higher rate of production growth and minimal crop damage. Using machine vision, deep learning methods and tools for extracting and classifying features, It could be possible to build a reliable disease detection system. Numerous researchers have created and deployed various ways for detecting plant diseases and pests. The potential of these methods has been examined in this work.

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Section: Challenges and Future Directionmentioning

confidence: 99%