https://matjournals.net/engineering/index.php/JoAESP <p>Journal of Advancement in Electronics Signal Processing is a peer-reviewed journal in the field of Electronics published by the MAT Journals Pvt. Ltd. JoAESP is a print e-journal focused on the rapid publication of fundamental research papers on all areas of Advancement in Electronics Signal Processing. This Journal involves the basic principles of Signal Processing Algorithms: signal filtering, Signal compression, Signal enhancement, Signal analysis, Digital Signal Processing (DSP): advanced DSP architectures, high-speed processors, audio and video compression, communication systems, biomedical signal analysis, Biomedical Signal Processing: medical imaging, electroencephalography (EEG), electrocardiography (ECG), and other physiological signal analysis. Multimedia Signal Processing: multimedia compression, content analysis, and multimedia communication. The Journal aims to promote high-quality Research, Review articles, and case studies mainly focussed on Analog signal processing, Digital signal processing, Non-linear signal processing, Statistical signal processing, Detection and estimation, Multichannel or multidimensional signal processing, Array signal processing, Spectral analysis and filtering, New theories or methods applied in signal processing, Speech processing, audio processing, speech recognition, Image processing, Video processing, Remote sensing and photogrammetry, filters, signal compressors, digital signal processors, Machine learning and artificial intelligence in signal/image analysis. This Journal involves the comprehensive coverage of all the aspects of Advancement in Electronics Signal Processing.</p> MAT Journals Pvt. Ltd. en-US Journal of Advancement in Electronics Signal Processing https://matjournals.net/engineering/index.php/JoAESP/article/view/3681 <p><em>Modern agriculture faces significant challenges in early disease detection and efficient resource management. This study presents an autonomous mobile platform integrating computer vision and deep learning for real-time coconut tree health assessment. The proposed system employs the YOLOv8 architecture for disease identification and growth stage classification, achieving detection rates exceeding 90% in field trials. A Raspberry Pi-based processing unit coordinates with Arduino microcontrollers to enable remote operation through a web-based interface. The platform captures live imagery, performs on-device inference, and provides fertilizer recommendations based on detected conditions. Field validation demonstrates the system’s capability to reduce manual inspection time by 75% while maintaining detection accuracy comparable to expert assessment. The integration of autonomous navigation with precision agriculture techniques offers a scalable solution for plantation monitoring and targeted intervention. The rover system identifies four critical disease classes, including Bud Rot, Stem Bleeding, Grey Leaf Spot, and Bud Dropping, with an overall accuracy of 93.1%. Real-time processing operates at 26 frames per second with minimal latency, enabling smooth video streaming to a Flutter-based mobile application. The system’s distributed architecture separates high-level AI processing on Raspberry Pi from time-critical motor control on Arduino, ensuring reliable operation. Battery-powered operation provides 4.5 hours of continuous monitoring with WiFi connectivity extending up to 50 meters. The automated fertilizer recommendation engine achieves 100% alignment with expert agricultural protocols, supporting sustainable farming practices through optimized chemical application. This cost-effective implementation using commercially available components makes precision agriculture technology accessible to small and medium-scale farmers, addressing critical labor shortages while improving crop health management. </em></p> Midhun M. Pillai Sooraj Anil Tintu Mary John Bejoy Antony Thushara Tulasi Copyright (c) 2026 Journal of Advancement in Electronics Signal Processing 2026-06-06 2026-06-06 1 11