Pesticide Residue Detection in Crops and Fruits using Machine Learning

The detection of pesticide residues in agricultural products has become a critical concern for food safety and public health worldwide. Traditional analytical methods, while accurate, are time-consuming, expensive, and require specialized laboratory infrastructure, making them unsuitable for real-time monitoring. This paper presents a comprehensive review and analysis of machine learning approaches for pesticide residue detection in crops and fruits. We systematically examine various detection methodologies including spectroscopic techniques (Raman, NIR, hyperspectral imaging), chromatographic methods (GC-MS, HPLC), electrochemical sensors, and IoT-based systems integrated with machine learning algorithms. Our analysis reveals that machine learning models, particularly Support Vector Machines, Convolutional Neural Networks, and Random Forest algorithms, significantly enhance detection accuracy and reduce analysis time. Hyperspectral imaging combined with deep learning achieved the highest accuracy rates (>97%) for multi-pesticide detection, while IoT-sensor networks demonstrated excellent potential for real-time field monitoring with 96% accuracy. The integration of artificial intelligence with traditional detection methods offers promising solutions for rapid, cost-effective, and accessible pesticide monitoring systems. This comprehensive analysis provides insights into current technological gaps and future research directions for developing intelligent pesticide detection systems suitable for modern agricultural practices.