IoT-based pest detection and classification using deep features with enhanced deep learning strategies

Prasath, B.; Akila, M.

doi:10.1016/j.engappai.2023.105985

Cited by 10 publications

(4 citation statements)

References 34 publications

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“…Our technique is effective in performance and report measurements. Drawing upon an extensive review of relevant academic literature, a majority of the research conducted on images [28][29][30][31][32][33][34][35][36][37][38][39]50] indicates a lack of sufficient attention towards the establishment of reliable analytical techniques for prevention and control [40][41][42][43][44][45] and accurately identifying pests through the analysis of acoustic signals produced by these pests [46][47][48][49]. The summarized findings of these reviews can be found in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

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Multi-Features and Multi-Deep Learning Networks to identify, prevent and control pests in tremendous farm fields combining IoT and pests sound analysis

Ali,

Sharma,

Dhanaraj

2024

Preprint

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The agriculture sectors, which account for approximately 50% of the worldwide economic production, are the fundamental cornerstone of each nation. The significance of precision agriculture cannot be understated in assessing crop conditions and identifying suitable treatments in response to diverse pest infestations. The conventional method of pest identification exhibits instability and yields subpar levels of forecast accuracy. Nevertheless, the monitoring techniques frequently exhibit invasiveness, require significant time and resources, and are susceptible to various biases. Numerous insect species can emit distinct sounds, which can be readily identified and recorded with minimal expense or exertion. Applying deep learning techniques enables the automated detection and classification of insect sounds derived from field recordings, hence facilitating the monitoring of biodiversity and the assessment of species distribution ranges. The current research introduces an innovative method for identifying and detecting pests through IoT-based computerized modules that employ an integrated deep-learning methodology using the dataset comprising audio recordings of insect sounds. This included techniques, the DTCDWT method, Blackman-Nuttall window, Savitzky-Golay filter, FFT, DFT, STFT, MFCC, BFCC, LFCC, acoustic detectors, and PID sensors. The proposed research integrated the MF-MDLNet to train, test, and validate data. 9,600 pest auditory sounds were examined to identify their unique characteristics and numerical properties. The recommended system designed and implemented the ultrasound generator, with a programmable frequency and control panel for preventing and controlling pests and a solar-charging system for supplying power to connected devices in the networks spanning large farming areas. The suggested approach attains an accuracy (99.82%), a sensitivity (99.94%), a specificity (99.86%), a recall (99.94%), an F1 score (99.89%), and a precision (99.96%). The findings of this study demonstrate a significant enhancement compared to previous scholarly investigations, including VGG 16, VOLOv5s, TSCNNA, YOLOv3, TrunkNet, DenseNet, and DCNN.

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Section: Literature Reviewmentioning

confidence: 99%

“…VGG 16 [31] 82.50 2023 YOLOv5s [33] 93.90 2023 TSCNNA [34] 93.16 2023 YOLOv3 [36] 96.00 2023 TrunkNet [46] 96.89 2023 DenseNet [65] 98 VGG 16 [31] YOLOv5s [33] TSCNNA [34] YOLOv3 [36] TrunkNet [46] DenseNet [65] DCNN [66]…”

Section: Accuracy (%) Yearmentioning

confidence: 99%

Multi-Features and Multi-Deep Learning Networks to identify, prevent and control pests in tremendous farm fields combining IoT and pests sound analysis

Ali,

Sharma,

Dhanaraj

2024

Preprint

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“…The presented method is examined by employing the NBAIR dataset. Prasath and Akila [14] introduced an approach called Yolov3 for recognizing pests in plants and the conceded neurons are maximized by the method called Adaptive Energy-based Harris Hawks Optimizer (AE-HHO). A deep featureextracting process is employed with VGG16 and ResNet50 methods.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Deep Learning and Farmland Fertility Algorithm for Automated Rice Pest Detection and Classification Model

2024

KSII TIIS

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Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.

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“…Overall, the research contributes to advancing the field of IoT-enabled WSNs by introducing a hybrid optimization approach that can be applied to various practical applications, leading to the improved IoT infrastructure and their services [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Enhancing IoT Applications with Hybrid Artificial Immune System (HAIS) and Particle Swarm Optimization (PSO) in Heterogeneous Wireless Sensor Networks (HWSN)

2024

IJSREM

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The research proposes an innovative integration of a Hybrid Artificial Immune System (HAIS) and Particle Swarm Optimization (PSO) to optimize a HWSN tailored for Internet of Things (IoT) applications. The combined HAIS-PSO framework is designed to enhance the network's performance metrics, such as energy efficiency, coverage, and data accuracy. Through rigorous simulations and practical experiments, to demonstrate the efficacy and superiority of this hybrid system in overcoming key challenges encountered in IoT deployments. This study not only contributes to advancing the optimization techniques for HWSNs but also lays a foundation for improved IoT infrastructure and services, ultimately benefiting a wide range of IoT applications. Key Words: Hybrid Artificial Immune System, Particle Swarm Optimization, HWSN, IoT, Utilize statistical analysis.

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IoT-based pest detection and classification using deep features with enhanced deep learning strategies

Cited by 10 publications

References 34 publications

Multi-Features and Multi-Deep Learning Networks to identify, prevent and control pests in tremendous farm fields combining IoT and pests sound analysis

Multi-Features and Multi-Deep Learning Networks to identify, prevent and control pests in tremendous farm fields combining IoT and pests sound analysis

Leveraging Deep Learning and Farmland Fertility Algorithm for Automated Rice Pest Detection and Classification Model

Enhancing IoT Applications with Hybrid Artificial Immune System (HAIS) and Particle Swarm Optimization (PSO) in Heterogeneous Wireless Sensor Networks (HWSN)

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