Red Palm Weevil (RPW) infestation poses a significant threat to coconut trees leading to severe damage and economic losses. However, existing RPW detection techniques have low sensitivity to early infestations as internal damage by larvae is not externally visible, hindering timely intervention. In this work, a novel deep learning-based RED-BIYO is proposed for detecting coconut tree damages using multimodal data namely stem image and audio signal. The stem images are gathered by an image capture device and audio signals are captured by a coconut microphone sensor. The stem image is pre-processed by Savitzky-Golay (SavG) filter to remove the noise from the images. The YOLOv9 model leverages a ResNeSt-based split-attention backbone and a Feature Pyramid Network (FPN) to effectively extract detailed multi-scale features from pre-processed images for enabling precise detection of Damaged Tree (DT) and No Damaged Tree (NDT). Simultaneously, the acquired audio signals are pre-processed using short-time framing with Hamming windowing and STFT-based spectrogram extraction. The resulting spectrograms are analyzed using the Spiking Convolutional Neural Network based Bidirectional Gated Recurrent Unit (SCNN-BiGRU) for RPW detection. SCNN extracts spatial and frequency features, while BiGRU captures temporal patterns in both forward and backward directions. The multi model data are fused in fuzzy rules to assess coconut tree health and notify farmers via an IoT-enabled Blynk app. The proposed RED-BIYO achieves the detection accuracy (AC) of 99.54% and Matthews Correlation Coefficient (MCC) of 97.93%. The proposed RED-BIYO model increases the overall accuracy by 2.75%, 9.38%, 2.61%, 22.16%, 8.54% and 4.39% of YOLOv5, ResNet50, Mask R-CNN, InceptionResNetV2, YOLOv3 and MIN-SVM model respectively. In future, RED-BIYO will focus on optimizing the model for low-power edge deployment to enable large-scale, real-time RPW monitoring.
T et al. (Fri,) studied this question.