Pushing the boundaries of aphid detection: An investigation into mmWaveRadar and machine learning synergy

Abstract

Agriculture, essential for global sustenance and economic vitality, faces significant threats from pest-induced damages, resulting in substantial crop losses and affecting food supply if not detected on time. Traditional pest control methods, primarily reliant on pesticides. However, blindly applying pesticide may cause environmental issue. Therefore detecting the infested crops at early stage is crucial for application of sustainable pest management solutions. This study innovatively employs the IWR1443BOOST FMCW Millimeter Wave Radar (mmWaveRadar) in conjunction with machine learning algorithms such as SVM, Random Forest, Adaboost, Lightgbm, Catboost, and edRVFL for enhanced pest detection in crops. Our novel framework encompasses the collection and pre-processing of mmWaveRadar data from both healthy and infested crops, followed by comprehensive feature extraction. Decision tree-based methods exhibited a remarkable detection accuracy of 98%. EdRVFL demonstrated a 95% detection accuracy. SVM, post-feature selection, achieved a 90% accuracy. The research reveals the efficacy of the mmWaveRadar as a robust tool, overcoming the environmental and concealment limitations of conventional image-based pest detection methods. The integration of curated features with machine learning algorithms has shown promising empirical results, establishing a connection between the discerned features and the real-world attributes of healthy and infested crops. This study underscores the potential of mmWaveRadar, coupled with specific machine learning algorithms, as a significant stride towards sustainable and effective pest management strategies in agricultural technology.