A Hybrid YOLOv5-SSD IoT-Based Animal Detection System for Durian Plantation Protection

• URL: http://arxiv.org/abs/2511.00777v1
• Date: Sun, 02 Nov 2025 03:03:26 GMT
• Title: A Hybrid YOLOv5-SSD IoT-Based Animal Detection System for Durian Plantation Protection
• Abstract summary: Durian plantation suffers from animal intrusions that cause crop damage and financial loss.<n>This research suggests an IoT-enabled animal detection system for durian crops.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Durian plantation suffers from animal intrusions that cause crop damage and financial loss. The traditional farming practices prove ineffective due to the unavailability of monitoring without human intervention. The fast growth of machine learning and Internet of Things (IoT) technology has led to new ways to detect animals. However, current systems are limited by dependence on single object detection algorithms, less accessible notification platforms, and limited deterrent mechanisms. This research suggests an IoT-enabled animal detection system for durian crops. The system integrates YOLOv5 and SSD object detection algorithms to improve detection accuracy. The system provides real-time monitoring, with detected intrusions automatically reported to farmers via Telegram notifications for rapid response. An automated sound mechanism (e.g., tiger roar) is triggered once the animal is detected. The YOLO+SSD model achieved accuracy rates of elephant, boar, and monkey at 90%, 85% and 70%, respectively. The system shows the highest accuracy in daytime and decreases at night, regardless of whether the image is still or a video. Overall, this study contributes a comprehensive and practical framework that combines detection, notification, and deterrence, paving the way for future innovations in automated farming solutions.

Related papers

• Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lens [47.03825808787752]
  We present empirical findings from a human-subject study in which users classified news fragments by origin (human vs. machine) and veracity (real vs. fake)<n>We organize results using an adapted cybersecurity kill chain as a taxonomy for intervention, mapping perception data onto stages of a cognitive attack lifecycle.<n>Three key findings emerge: (1) a perception-accuracy gap where heightened suspicion does not improve detection; (2) modern LLMs frequently produce human-indistinguishable text; and (3) an asymmetric cognitive fatigue effect where fake-news detection degrades by 10.2 percentage points under sustained exposure while AI-origin detection remains stable.
  arXiv  Detail & Related papers  (2026-07-26T15:40:41Z)
• Real-Time Source-Free Object Detection [40.10658212485104]
  Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints.<n>We show that building on YOLOv10, an NMS-free dual-head detector, we achieve state-of-the-art adaptation accuracy while being faster and more compact.
  arXiv  Detail & Related papers  (2026-06-30T15:38:56Z)
• From Visual to Multimodal: Systematic Ablation of Encoders and Fusion Strategies in Animal Identification [35.71275089934349]
  This study introduces a multimodal verification framework that enhances visual features with semantic identity priors derived from synthetic textual descriptions.<n>We constructed a massive training corpus of 1.9 million photographs covering 695,091unique animals to support this investigation.
  arXiv  Detail & Related papers  (2026-02-28T21:27:38Z)
• Investigating Target Class Influence on Neural Network Compressibility for Energy-Autonomous Avian Monitoring [36.85680419418593]
  We propose running machine learning models on inexpensive microcontroller units (MCUs) directly in the field.<n>We trained and compressed models for various numbers of target classes to assess the detection of multiple bird species.<n>We also provide benchmarking results for different hardware platforms and evaluate the feasibility of deploying energy-autonomous devices.
  arXiv  Detail & Related papers  (2026-02-19T16:24:33Z)
• MultiCore+TPU Accelerated Multi-Modal TinyML for Livestock Behaviour Recognition [0.5524804393257919]
  In recent years, more intelligent livestock monitoring solutions have been proposed to enhance farming efficiency and productivity.<n>This work presents a novel approach to animal activity recognition and movement tracking, leveraging tiny machine learning (TinyML) techniques.<n>It collects and fuses accelerometer data and vision inputs to build a multi-modal network for three tasks: image classification, object detection, and behaviour recognition.
  arXiv  Detail & Related papers  (2025-04-10T12:55:08Z)
• Endangered Alert: A Field-Validated Self-Training Scheme for Detecting and Protecting Threatened Wildlife on Roads and Roadsides [10.412505957288406]
  This paper presents an innovative self-training methodology aimed at detecting rare animals, such as the cassowary in Australia.<n>The proposed method addresses critical real-world challenges, including acquiring and labelling sensor data for rare animal species.<n>It achieves this by leveraging cloud and edge computing, and automatic data labelling to improve the detection performance of the field-deployed model.
  arXiv  Detail & Related papers  (2024-12-16T07:44:27Z)
• Scarecrow monitoring system:employing mobilenet ssd for enhanced animal supervision [0.0]
  The project employs advanced object detection, the system utilizes the Mobile Net SSD model for real-time animal classification. Real-time detection is achieved through a webcam and the OpenCV library, enabling prompt identification and categorization of approaching animals.
  arXiv  Detail & Related papers  (2024-07-01T16:26:57Z)
• Multisensor Data Fusion for Automatized Insect Monitoring (KInsecta) [32.57872751877726]
  This paper presents a multisensor approach that uses AI-based data fusion for insect classification. The system is designed as low-cost setup and consists of a camera module and an optical wing beat sensor. First tests on a small very unbalanced data set with 7 species show promising results for species classification.
  arXiv  Detail & Related papers  (2024-04-29T08:46:43Z)
• Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems [30.16714420093091]
  Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks. This article proposes a novel deep neural network/architecture called Constrained Twin Variational Auto-Encoder (CTVAE) CTVAE can boost around 1% in terms of accuracy and Fscore in detection attack compared to the state-of-the-art machine learning and representation learning methods.
  arXiv  Detail & Related papers  (2023-12-05T04:42:04Z)
• CSI-Based Efficient Self-Quarantine Monitoring System Using Branchy Convolution Neural Network [2.609279398946235]
  We propose a Wi-Fi-based device-free self-quarantine monitoring system. We exploit channel state information (CSI) derived from Wi-Fi signals as human activity features. Our experimental results indicate that the proposed model can achieve an average accuracy of 98.19% for classifying five different human activities.
  arXiv  Detail & Related papers  (2023-05-24T04:02:49Z)
• Agile gesture recognition for capacitive sensing devices: adapting on-the-job [55.40855017016652]
  We demonstrate a hand gesture recognition system that uses signals from capacitive sensors embedded into the etee hand controller. The controller generates real-time signals from each of the wearer five fingers. We use a machine learning technique to analyse the time series signals and identify three features that can represent 5 fingers within 500 ms.
  arXiv  Detail & Related papers  (2023-05-12T17:24:02Z)
• Real-Time Driver Monitoring Systems through Modality and View Analysis [28.18784311981388]
  Driver distractions are known to be the dominant cause of road accidents. State-of-the-art methods prioritize accuracy while ignoring latency. We propose time-effective detection models by neglecting the temporal relation between video frames.
  arXiv  Detail & Related papers  (2022-10-17T21:22:41Z)
• Automated Pest Detection with DNN on the Edge for Precision Agriculture [0.0]
  This paper presents an embedded system enhanced with machine learning (ML) functionalities, ensuring continuous detection of pest infestation inside fruit orchards. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer's intervention.
  arXiv  Detail & Related papers  (2021-08-01T10:17:48Z)
• Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks [26.09388179354751]
  We present the first study focused on poisoning attacks on online-trained autoencoder-based attack detectors. We show that the proposed algorithms can generate poison samples that cause the target attack to go undetected by the autoencoder detector. This finding suggests that neural network-based attack detectors used in the cyber-physical domain are more robust to poisoning than in other problem domains.
  arXiv  Detail & Related papers  (2020-02-07T12:41:28Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.