Shallow and Deep Learning Architecture for Pests Identification on Pomelo Leaf

Truong, Quoc Bao; Thanh, Tan Kiet Nguyen; Nguyen, Minh Hoang; Truong, Quoc Dinh; Huynh, Hiep Xuan

doi:10.1109/kse.2018.8573422

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…The previously mentioned methodology is also utilized through cell phones to assist in managing tomato pests. A study by Liu and colleagues [23] examined the difficulties of monitoring various pests. The global activation technique uses many hierarchical pyramid tiers to identify small-scale pests.…”

Section: Literature Reviewmentioning

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 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%

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 DL tool, CNN architecture, is used in models to recognize pests, diseases, and weeds, achieving greater accuracy in results than other tools Gao;Hang, 2019;Li et al 2020;Ren et al 2018;Tetila et al 2020b;Truong et al 2018;Wu, 2019). Developed applications with CNN to detect pests and inform the farmer which procedures should be adopted (Liu et 14…”

Section: Qp1 -What Are Computational Technologies Used To Combat and ...mentioning

confidence: 99%

“…Vivek A. (2019) andTruong et al, (2018) used SVM to perform classification and identify pests. Ma, Liang, Lyu (2019) used Neural network (NN) and D-S Evidence Theory to predict pest outbreaks based on climatic and pest outbreak factors.…”

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confidence: 99%

Artificial Intelligence and Its Tools in Pest Control for Agricultural Production: A Review

Mignoni,

Soares Monteiro,

Zagonel

et al. 2024

RECIMA21

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Artificial Intelligence (AI) and its tools are being widely used worldwide. In the area of agriculture, AI is being widely studied and expanding. The use of AI in agriculture is being widely studied and expanding from pre-harvest to post-harvest. The increase in world population has triggered the need to increase food production. This need has triggered a search for solutions that promote increased food production and quality. One way to increase food production and quality is pest control. AI and its tools have proven to be a growing and rising solution in controlling and combating pests. This research focuses on reviewing and demonstrating the advances in combating and controlling pests using AI tools and images. It stands out: the classification of pests; insect identification; use and capture of Unmanned aerial vehicle (UAV) footage; using Deep Learning (DL) and Convolutional Neural Network (CNN). A search engine was applied to 5 databases. Cutting criteria were applied in 3 stages, and there were 71 papers at the end. The 71 went through 3 quality assessment questions, leaving 47 works for final analysis. This study demonstrated that the DL and the CNN tool using real images have the potential for insect control and combat solutions. Another tool in recent studies associated with CNN is the attention mechanism, improving pest identification results. Identification of insects through leaf images using CNN requires.

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Deep feature-based plant disease identification using machine learning classifier

Hassan

Maji

2022

Innovations Syst Softw Eng

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Shallow and Deep Learning Architecture for Pests Identification on Pomelo Leaf

Cited by 10 publications

References 7 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

Artificial Intelligence and Its Tools in Pest Control for Agricultural Production: A Review

Deep feature-based plant disease identification using machine learning classifier

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