Abstract: The project "Smart IoT-Based Fruit Chemical and Disease Detection Using Machine Learning" aims to enhance the efficiency of fruit quality assessment and disease detection through a multidimensional approach. Leveraging image-based detection and gas sensor technology, the system employs machine learning algorithms to analyse visual data and chemical emissions. The image-based detection utilizes computer vision techniques to identify visual cues associated with fruit diseases, while the gas sensor component focuses on chemical signatures emitted by fruits. By integrating these features into an IoT framework, the system provides real- time monitoring and analysis, allowing for early detection of diseases and chemical anomalies. Apart from just ensuring the accuracy of fruit quality assessment but also facilitates prompt intervention and decision-making in agricultural practices, contributing to improved crop yield and overall sustainability.
Keywords: Disease, Chemical detection, IoT, Machine learning
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DOI:
10.17148/IJARCCE.2024.134165
