Effectiveness of IoT and Deep Learning for Detection and Severity Assessment of Postelectrotermes militaris in Tea Plantations

• URL: http://arxiv.org/abs/2608.27480v1
• Date: Sat, 22 Aug 2026 10:42:47 GMT
• Title: Effectiveness of IoT and Deep Learning for Detection and Severity Assessment of Postelectrotermes militaris in Tea Plantations
• Abstract summary: Tea plantations are vulnerable to Postelectrotermes militaris, commonly known as the Upcountry Live Wood Termite (ULWT)<n>This study proposes an IoT-enabled acoustic monitoring framework integrated with deep learning for early detection and severity assessment of ULWT infestations.
• Score: 0.20972270756982536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tea plantations are vulnerable to Postelectrotermes militaris, commonly known as the Upcountry Live Wood Termite (ULWT), which can cause substantial damage when infestations remain undetected. This study proposes an IoT-enabled acoustic monitoring framework integrated with deep learning for early detection and severity assessment of ULWT infestations in tea plantations. Research Method: Audio signals were captured non-invasively from tea trunks using a high-sensitivity microphone connected to a Raspberry Pi-based IoT device, with geographic coordinates recorded for spatial tracking. After trimming, resampling, and segmentation, 2,000 ten-second samples were obtained, comprising 1,000 healthy and 1,000 infested samples, and divided into 1,600 training, 200 validation, and 200 test samples. The dataset used in this study is publicly available on Kaggle (Senevirathna et al. 2026). Fourier-derived spectrograms trained a CNN for infestation classification and probability estimation. A weighted severity model combined CNN probability, mean acoustic amplitude, and nearby infested plants within 5 m, with geospatial mapping used to visualize infestation distribution. Findings and Values: Field trials in a ULWT-affected tea plantation in Pundaluoya demonstrated feasibility under realistic environmental noise. On the held-out test set, the CNN achieved 81.5% accuracy, 80.6% precision, 83.0% recall, 81.8% F1-score, and 0.819 ROC-AUC. Beyond binary infestation detection, the framework introduced quantitative severity assessment using infestation probability, acoustic amplitude, and nearby infested plants. The resulting severity and geospatial outputs can support plantation managers in identifying high-risk areas, prioritizing field inspections, and implementing more timely and targeted control measures.

Related papers

• Embedding NDRE Trajectories into Contrastive Learning for Label-Free, Physiology-Aware Crop-Stress Staging and DSS Outputs [0.0]
  EigenCL is a physiology-guided contrastive learning framework that stages crop stress from Sentinel-2 NDRE trajectories.<n>It was trained on 10,000 maize NDRE patches from drought-affected Iowa fields in 2020 and tested on Nebraska fields in 2023 without retraining.<n>It produced four physiologically coherent stress clusters (Healthy, Mild, Moderate, Severe), significantly outperforming baselines.
  arXiv  Detail & Related papers  (2026-08-26T15:01:56Z)
• Calibrated Tree-Neural Fusion for Fine-Grained Vegetation Community Classification [5.365444289459681]
  Calibrated EcoTreeFuseNet-Plus is a tree-neural probability-fusion framework.<n>The proposed model achieved an accuracy of 0.8000, a macro F1-score of 0.7768, a balanced accuracy of 0.7903, and an MCC of 0.7903.<n>Results demonstrate a reliable discrimination-calibration trade-off for small-sample, fine-grained ecological classification.
  arXiv  Detail & Related papers  (2026-07-27T08:37:49Z)
• FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation [63.7829089874007]
  This paper introduces the Fetal Ultrasound Grand Challenge (FUGC), the first benchmark for semi-supervised learning in cervical segmentation.<n>FUGC provides a dataset of 890 TVS images, including 500 training images, 90 validation images, and 300 test images.<n> Methods were evaluated using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and runtime (RT), with a weighted combination of 0.4/0.4/0.2.
  arXiv  Detail & Related papers  (2026-01-22T01:34:39Z)
• Multispectral Remote Sensing for Weed Detection in West Australian Agricultural Lands [3.6284577335311563]
  The Kondinin region in Western Australia faces significant agricultural challenges due to pervasive weed infestations, causing economic losses and ecological impacts.<n>This study constructs a tailored multispectral remote sensing framework for weed detection to advance precision agriculture practices.<n>Unmanned aerial vehicles were used to collect raw multispectral data from two experimental areas over four years, covering 0.6046 km2 and ground truth annotations were created with GPS-enabled vehicles to manually label weeds and crops.
  arXiv  Detail & Related papers  (2025-02-12T07:01:42Z)
• GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding [46.46135774964818]
  Estimating causal effects from data is essential in public health, environmental science, and policy evaluation.<n>We introduce GST-UNet, a neural framework that combines a U-Net-basedtemporal encoder with regression-based iterative G-mputation.<n>We validate its effectiveness in synthetic experiments and in a real-world analysis of wildfire smoke exposure and respiratory hospitalizations during California Camp Fire.
  arXiv  Detail & Related papers  (2025-02-07T19:56:01Z)
• A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields [9.735484847744416]
  Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields. Farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. We have developed a large multi-scale dataset for aphid cluster detection and segmentation.
  arXiv  Detail & Related papers  (2024-05-07T13:27:58Z)
• Interpretable cancer cell detection with phonon microscopy using multi-task conditional neural networks for inter-batch calibration [39.759100498329275]
  We present a conditional neural network framework to simultaneously achieve inter-batch calibration. We validate our approach by training and validating on different experimental batches. We extend our model to reconstruct denoised signals, enabling physical interpretation of salient features indicating disease state.
  arXiv  Detail & Related papers  (2024-03-26T12:20:10Z)
• A multiscale spatiotemporal approach for smallholder irrigation detection [0.0]
  This paper presents an irrigation detection methodology that leverages multiscale satellite imagery of vegetation abundance. The methodology is applied to detect smallholder irrigation in two states in the Ethiopian highlands, Tigray and Amhara.
  arXiv  Detail & Related papers  (2022-02-09T02:50:42Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)
• A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery [56.10033255997329]
  We propose a novel deep learning method based on a Convolutional Neural Network (CNN) It simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops.
  arXiv  Detail & Related papers  (2020-12-31T18:51:17Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.