| _version_ | 1866901621461483520 |
|---|---|
| author | ARUNAPRIYA. R , Dr.S.P.VALLI |
| author_facet | ARUNAPRIYA. R , Dr.S.P.VALLI |
| contents | <p>Citrus crops are often damaged by pest a infestation, which is a persistent problem for growers and often leads to significant declines in output and profitability. Traditional pest detection techniques rely on direct observation by field workers, which is labour-intensive, time consuming and sometimes unreliable. In order to detect and categories citrus pests in real time, this study proposes an internet of things (IoT) based automated bug detection system that makes use of convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks. Images from Iot-enabled field cameras are processed using a lightweight hybrid deep learning techniques to extract features and make fast conclusions on devices with limited processing power. Six important citrus pest species are represented by the 1,200 images in this experimental dataset which was collected from a citrus research station in Tamil Nadu, India. With an accuracy of 95.4%, the evaluation results demonstrate that our hybrid CNN-LSTM architecture performs better than both traditional CNN and conventional machine learning models. By facilitating automated field monitoring early insect detection and timely decision making the proposal method reduces crop losses and the need for pesticides.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18666661 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A NOVEL IOT BASED PEST IDENTIFICATION AND CLASSIFICATION USING CNN AND LSTM ARUNAPRIYA. R , Dr.S.P.VALLI Artificial Intelligence (AI), Internet of Things (IoT), Citrus, CNN, LSTM, Pests <p>Citrus crops are often damaged by pest a infestation, which is a persistent problem for growers and often leads to significant declines in output and profitability. Traditional pest detection techniques rely on direct observation by field workers, which is labour-intensive, time consuming and sometimes unreliable. In order to detect and categories citrus pests in real time, this study proposes an internet of things (IoT) based automated bug detection system that makes use of convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks. Images from Iot-enabled field cameras are processed using a lightweight hybrid deep learning techniques to extract features and make fast conclusions on devices with limited processing power. Six important citrus pest species are represented by the 1,200 images in this experimental dataset which was collected from a citrus research station in Tamil Nadu, India. With an accuracy of 95.4%, the evaluation results demonstrate that our hybrid CNN-LSTM architecture performs better than both traditional CNN and conventional machine learning models. By facilitating automated field monitoring early insect detection and timely decision making the proposal method reduces crop losses and the need for pesticides.</p> |
| title | A NOVEL IOT BASED PEST IDENTIFICATION AND CLASSIFICATION USING CNN AND LSTM |
| topic | Artificial Intelligence (AI), Internet of Things (IoT), Citrus, CNN, LSTM, Pests |
| url | https://doi.org/10.5281/zenodo.18666661 |