Detection of sedge weeds infestation in wetland rice cultivation using hyperspectral images and artificial intelligence: a review

Muhamad Noor Hazwan Abd Manaf, . and Abdul Shukor Juraimi, . and ., Mst. Motmainna and Nik Norasma Che’Ya, . and Ahmad Suhaizi Mat Su, . and Muhammad Huzaifah Mohd Roslim, . and Anuar Ahmad, . and Nisfariza Mohd Noor, . (2024) Detection of sedge weeds infestation in wetland rice cultivation using hyperspectral images and artificial intelligence: a review. Pertanika Journal of Science & Technology (Malaysia), 32 (3). 1317 -1334. ISSN 2231-8526

Sedge is one type of weed that can infest the rice field, as well as broadleaf and grasses. If sedges are not appropriately controlled, severe yield loss will occur due to increased competition with cultivated rice for light, space, nutrients, and water. Both sedges and grasses are monocots and have similar narrowed leaf characteristics, but most sedge stems have triangular prismatic shapes in cross sections, which differ them from grasses. Event sedges and grasses differ in morphology, but differentiating them in rice fields is challenging due to the large rice field area and high green color similarity. In addition, climate change makes it more challenging as the distribution of sedge weed infestation is influenced by surrounding abiotic factors, which lead to changes in weed control management. With advanced drone technology, agriculture officers or scientists can save time and labor in distributing weed surveys in rice fields. Using hyperspectral sensors on drones can increase classification accuracy and differentiation between weed species. The spectral signature of sedge weed species captured by the hyperspectral drone can generate weed maps in rice fields to give the sedge percentage distribution and location of sedge patch growth. Researchers can propose proper countermeasures to control the sedge weed problem with this information. This review summarizes the advances in our understanding of the hyperspectral reflectance of weedy sedges in rice fields. It also discusses how they interact with climate change and phenological stages to predict sedge invasions.

Full text available from: Official URL: http://www.pertanika.upm.edu.my/resources/files/Pe...

Abstract

Sedge is one type of weed that can infest the rice field, as well as broadleaf and grasses. If sedges are not appropriately controlled, severe yield loss will occur due to increased competition with cultivated rice for light, space, nutrients, and water. Both sedges and grasses are monocots and have similar narrowed leaf characteristics, but most sedge stems have triangular prismatic shapes in cross sections, which differ them from grasses. Event sedges and grasses differ in morphology, but differentiating them in rice fields is challenging due to the large rice field area and high green color similarity. In addition, climate change makes it more challenging as the distribution of sedge weed infestation is influenced by surrounding abiotic factors, which lead to changes in weed control management. With advanced drone technology, agriculture officers or scientists can save time and labor in distributing weed surveys in rice fields. Using hyperspectral sensors on drones can increase classification accuracy and differentiation between weed species. The spectral signature of sedge weed species captured by the hyperspectral drone can generate weed maps in rice fields to give the sedge percentage distribution and location of sedge patch growth. Researchers can propose proper countermeasures to control the sedge weed problem with this information. This review summarizes the advances in our understanding of the hyperspectral reflectance of weedy sedges in rice fields. It also discusses how they interact with climate change and phenological stages to predict sedge invasions.

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Item Type:	Article
AGROVOC Term:	rice
AGROVOC Term:	weeds
AGROVOC Term:	monocotyledons
AGROVOC Term:	weed control
AGROVOC Term:	data analysis
AGROVOC Term:	artificial intelligence
AGROVOC Term:	drones (insect)
AGROVOC Term:	climate change
AGROVOC Term:	yield losses
Geographical Term:	Malaysia
Uncontrolled Keywords:	Climate change, drone, internet of things (IoT), rice, smart farming, weed
Depositing User:	Ms. Azariah Hashim
Date Deposited:	22 Apr 2026 01:55
Last Modified:	22 Apr 2026 01:55
URI:	http://webagris.upm.edu.my/id/eprint/2981

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