machine learning

Systematic Review of Hyperspectral Imaging in Precision Agriculture: Analysis of its Current State and Future Prospects

The paper conducted a systematic review of 97 scientific articles published between 2003 and 2023 to analyze current practices and identify future research directions. Key areas explored include data preprocessing techniques, hyperspectral data acquisition, data compression methods, segmentation, and the role of hardware accelerators like FPGAs and GPUs in speeding up data processing.

Palmer amaranth Identification using Hyperspectral Imaging and Machine Learning Technologies in Soybean Field.

Hyperspectral imaging combined with machine learning accurately differentiates Palmer amaranth from soybeans in field conditions. Quadratic discriminant analysis was the best model, achieving high accuracy, precision, recall, F1 score, and MCC. This research paves the way for real-time weed detection and autonomous weed management systems.