Application of hyperspectral imaging to identify spectral signatures of grass weeds in rice at early stages: a review

Abstract

The productivity and yield of rice crops are continually threatened by various biotic and abiotic stressors, with weed infestations being a primary concern. Among the many types of weeds that challenge rice cultivation, grass weeds are particularly troublesome due to their competitive nature and fast growth, which can lead to significant yield losses if not managed effectively. Normally, the detection and control of grass weeds in rice fields have relied on labor-intensive visual methods, such as visual inspections and hand-weeding. These approaches are not only time-consuming but also prone to human error, making them inefficient and costly. In recent years, remote sensing, particularly hyperspectral imaging, has emerged as a promising technology for addressing this challenge. Hyperspectral imaging systems capture a vast amount of spectral information across numerous narrow wavelength bands, enabling the differentiation of various objects and materials based on their unique spectral signatures. The objective of this review was to examine the principles of hyperspectral imaging, its advantages over current methods, and the various techniques and approaches used in weed detection and classification. Also, this paper examines the challenges and limitations associated with this technology and identify potential areas for future research and development.