deep 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.

Predicting Gypsum Tofu Quality from Soybean Seeds Using Hyperspectral Imaging and Machine Learning.

Hyperspectral imaging (HSI) and machine learning accurately predict gypsum tofu quality from soybean seeds. A new Yield and Texture Trade-off Theory was proposed. XGBoost outperformed other models, achieving 96-99% accuracy in classifying soybean seeds based on tofu quality.

Applications of deep learning in precision weed management: A review

The review analyzed 60 research papers on weed detection and discussed the potential of deep learning techniques for improving weed detection.