The application of Hyperspectral Remote Sensing Imagery (HRSI) for weed detection analysis in rice fields: a review

• Main Authors: Sulaiman, Nursyazyla, Che’Ya, Nik Norasma, Mohd Roslim, Muhammad Huzaifah, Juraimi, Abdul Shukor, Mohd Noor, Nisfariza Maris, Fazlil Ilahi, Wan Fazilah
• Format: Article
• Published: Multidisciplinary Digital Publishing Institute 2022
• Online Access: http://psasir.upm.edu.my/id/eprint/103463/; https://www.mdpi.com/2076-3417/12/5/2570
• Institution: Universiti Putra Malaysia

Weeds are found on every cropland across the world. Weeds compete for light, water, and nutrients with attractive plants, introduce illnesses or viruses, and attract harmful insects and pests, resulting in yield loss. New weed detection technologies have been developed in recent years to increase weed detection speed and accuracy, resolving the contradiction between the goals of enhancing soil health and achieving sufficient weed control for profitable farming. In recent years, a variety of platforms, such as satellites, airplanes, unmanned aerial vehicles (UAVs), and close-range platforms, have become more commonly available for gathering hyperspectral images with varying spatial, temporal, and spectral resolutions. Plants must be divided into crops and weeds based on their species for successful weed detection. Therefore, hyperspectral image categorization also has become popular since the development of hyperspectral image technology. Unmanned aerial vehicle (UAV) hyperspectral imaging techniques have recently emerged as a valuable tool in agricultural remote sensing, with tremendous promise for weed detection and species separation. Hence, this paper will review the weeds problem in rice fields in Malaysia and focus on the application of hyperspectral remote sensing imagery (HRSI) for weed detection with algorithms and modelling employed for weeds discrimination analysis.