A Maize Crop Yield Optimization and Healthcare Monitoring Framework Using Firefly Algorithm through IoT

Kanna, Sithan; Nagaraju, V.; Jayashree, D.; Munaf, Abdul; Ashok, M.

doi:10.1002/9781119760429.ch13

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…Deep learning approaches based on Convolutional Neural Networks (CNN) [25][26][27][28] are widely utilized for efficient classification with increased accuracy and convergence speed. This classification approach is optimized by various statistical algorithms, among which Firefly (FF) [29] and Grey Wolf Optimization (GWO) [30] are utilized in this approach. The hybridization of GWO and FF enhances the CNN classifier's performance, resulting in improved accuracy and a high convergence rate.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm

Divya¹,

Santhi²

2023

Intelligent Automation &Amp; Soft Computing

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Pest detection in agricultural crop fields is the most challenging task, so an effective pest detection technique is required to detect insects automatically. Image processing techniques are widely preferred in agricultural science because they offer multiple advantages like maximal crop protection, improved crop management and productivity. On the other hand, developing the automatic pest monitoring system dramatically reduces the workforce and errors. Existing image processing approaches are limited due to the disadvantages like poor efficiency and less accuracy. Therefore, a successful image processing technique based on FF-GWO-CNN classification algorithm is introduced for effective pest monitoring and detection. The four-step image processing technique begins with image pre-processing, removing the insect image's noise and sunlight illumination by utilizing an adaptive median filter. The insects' size and shape are identified using the Expectation Maximization Algorithm (EMA) based clustering technique, which involves not only clustering the data but also uncovering the correlations by visualizing the global shape of an image. Speeded up robust feature (SURF) method is employed to select the best possible image features. Eventually, the image with best features is classified by introducing a hybrid FF-GWO-CNN algorithm, which combines the benefits of Firefly (FF), Grey Wolf Optimization (GWO) and Convolutional Neural Network (CNN) classification algorithm for enhancing the classification accuracy. The entire work is executed in MATLAB simulation software. The test result reveals that the suggested technique has delivered optimal performance with high accuracy of 97.5%, precision of 94%, recall of 92% and F-score value of 92%.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm

Divya¹,

Santhi²

2023

Intelligent Automation &Amp; Soft Computing

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Segmentation of farmlands in aerial images by deep learning framework with feature fusion and context aggregation modules

Khan

Alarabi

Basalamah

2023

Multimed Tools Appl

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FSRW: fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare

Xu,

Liu,

et al. 2024

Sci Rep

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The Internet of Things (IoT) is an extensive system of interrelated devices equipped with sensors to monitor and track real world objects, spanning several verticals, covering many different industries. The IoT's promise is capturing interest as its value in healthcare continues to grow, as it can overlay on top of challenges dealing with the rising burden of chronic disease management and an aging population. To address difficulties associated with IoT-enabled healthcare, we propose a secure routing protocol that combines a fuzzy logic system and the Whale Optimization Algorithm (WOA) hierarchically. The suggested method consists of two primary approaches: the fuzzy trust strategy and the WOA-inspired clustering methodology. The first methodology plays a critical role in determining the trustworthiness of connected IoT equipment. Furthermore, a WOA-based clustering framework is implemented. A fitness function assesses the likelihood of IoT devices acting as cluster heads. This formula considers factors such as centrality, range of communication, hop count, remaining energy, and trustworthiness. Compared with other algorithms, the proposed method outperformed them in terms of network lifespan, energy usage, and packet delivery ratio by 47%, 58%, and 17.7%, respectively.

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A Maize Crop Yield Optimization and Healthcare Monitoring Framework Using Firefly Algorithm through IoT

Cited by 5 publications

References 9 publications

Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm

Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm

Segmentation of farmlands in aerial images by deep learning framework with feature fusion and context aggregation modules

FSRW: fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare

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