Design of agricultural ontology based on levy flight distributed optimization and Naïve Bayes classifier

Deepa, Rajendran; Vigneshwari, S.

doi:10.1007/s12046-021-01652-x

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…In 2021, Rajendran et al, [ 52 ] discovered an ontology design, that present a NB Classifier. Details for the dataset were gathered from a variety of government agriculture documents and websites.…”

Section: Crop Pest Detection By Naive Bayesmentioning

confidence: 99%

“…The leading feature set for lady's finger and bitter gourd leaf photos was then determined using the Pearson Correlation Coefficient approach. [52] In order to develop an agricultural ontology, this study presented the Naive Bayes Classifier using Levy flight distributed optimization algorithm (NBC-LFDOA). The soil, insect, and climatic classes are validated based on total class outcomes for the projected task performance.…”

Section: Crop Pest Detection By Naive Bayesmentioning

confidence: 99%

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Survey on crop pest detection using deep learning and machine learning approaches

Chithambarathanu

Jeyakumar

2023

Multimed Tools Appl

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The most important elements in the realm of commercial food standards are effective pest management and control. Crop pests can make a huge impact on crop quality and productivity. It is critical to seek and develop new tools to diagnose the pest disease before it caused major crop loss. Crop abnormalities, pests, or dietetic deficiencies have usually been diagnosed by human experts. Anyhow, this was both costly and time-consuming. To resolve these issues, some approaches for crop pest detection have to be focused on. A clear overview of recent research in the area of crop pests and pathogens identification using techniques in Machine Learning Techniques like Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT), Naive Bayes (NB), and also some Deep Learning methods like Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Deep convolutional neural network (DCNN), Deep Belief Network (DBN) was presented. The outlined strategy increases crop productivity while providing the highest level of crop protection. By offering the greatest amount of crop protection, the described strategy improves crop efficiency. This survey provides knowledge of some modern approaches for keeping an eye on agricultural fields for pest detection and contains a definition of plant pest detection to identify and categorise citrus plant pests, rice, and cotton as well as numerous ways of detecting them. These methods enable automatic monitoring of vast domains, therefore lowering human error and effort.

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Section: Crop Pest Detection By Naive Bayesmentioning

confidence: 99%

Section: Crop Pest Detection By Naive Bayesmentioning

confidence: 99%