Weed Species Identification Using Hyperspectral Imaging and Machine Learning

Reliable identification of weed species is essential for effective and sustainable weed management. In this study, we explored the use of hyperspectral imaging to distinguish nine weed species based on their spectral signatures. Although the species showed similarities in their spectral curves due to comparable growing conditions, clear differences emerged related to morphological traits and pigment composition. We analysed the spectral data using five classification algorithms: Random Forest, Support Vector Machine, Artificial Neural Network, Maximum Entropy, and SIMCA. Model performance was assessed using per-class and overall accuracy. Random Forest outperformed the other methods, achieving 93.5% accuracy despite limited and imbalanced training data. This work contributes to the development of a spectral library for weed species and demonstrates the value of machine learning for species identification across different crops and environmental conditions. Expanding such spectral databases can enhance the speed and accuracy of weed monitoring, reduce herbicide reliance, and reduce environmental impact. The proposed approach shows strong potential for integration into precision agriculture and agroecological monitoring systems, supporting more efficient and environmentally responsible farmland management.