# Weed Detection in Rice Fields Using Remote Sensing Technique: A Review
## 5 Weed Detection Using Remote Sensing Technique
### The Application of Remote Sensing and Machine Learning Technique into Weed Detection
#### 5.6.1 Machine Learning (ML)
However, for long term continuous monitoring, Landsat 8 OLI provides valuable information compared to the WorldView-2 commercial sensor. A few researchers chose object-based image analysis (OBIA) to classify weeds from crops. OBIA is an automatic hierarchal image classification algorithm. It allows numerous image objects to be created and further categorized into user-defined classes [98]. For example, [PERSON] et al. [87] used an RGB (red, green, blue) UAV to monitor early-season weeds in a sunflower field using object-based image analysis (OBIA). Their experiment was tested at two different flying heights, 30 m and 60 m, above the surface. They found that both flying heights give satisfactory outputs, with 2.5% to 5% thresholds and an accuracy higher than 85%. The same result was archived by [PERSON] et al. [88], [PERSON] and [PERSON] [93], and [PERSON] et al. [97] when they classified weeds from maize, wheat, and cotton, respectively. Their research helped farmers with rationalization of the herbicide application. Some of the researchers integrated object-based image analysis (OBIA) with other machine learning algorithms. OBIA's final output can be converted into another GIS format [99], making it flexible to integrate with other algorithms. For example, [PERSON] et al. [96] successfully classified _Cynodon dactylon_ (bermudagrass) in a vineyard by combining OBIA with the decision tree (DT) algorithm. [PERSON] et al. [46] also managed to produce a weed map of _Convolulus arvenis_ L. (bindweed) in a soybean field by combining OBIA with the RF algorithm. Meanwhile, [PERSON], [PERSON], and [PERSON] [62] successfully generated various types of weed maps in the sorghum's field by integrating OBIA with the artificial nearest neighbor (ANN) algorithm. [PERSON] et al. [47] experimented with the OBIA classification method using the simple linear iterative clustering algorithm-random forest (SLIC-RF). SLIC is a super-pixel method for extracting input feature details for each subject. They used three-color spaces (RGB, hue-saturation-brightness (HSV) and transformation function of RGB images (CIE-L*a*b*b*)) as the primary input feature and a spatial texture, four VIs (excess green (ExG), excess red (ExR), green-red vegetation index (GRVI), and color index of vegetation extraction (CIVE)), and DSM as the secondary data. The HSV-based SLIC-RF outperformed the other color spaces tested, with an accuracy of 90.4%. Instead of using an RGB UAV, [PERSON] et al. [50] used UAV multispectral images for early season weed mapping in rice using ISODATA classification. Their input data are spectral indices (normalized different vegetation index (NDVI), soil adjusted vegetation index (SAVI), GSAVI, a simple ratio index related to leaf pigments content and greenness (RGRI), normalized difference red edge (NDRE), and chlorophyll vegetation index (CVI)) and textural metrics. Weed mapping performance was validated by measuring overall accuracy (OA), while for weed class, omission errors (OE) and commission errors (CE) were calculated. The result shows that SAVI and GSAVI gave the best output compared to other indices, with 96.5% and 94.5% overall accuracy. The final production, classification map, weed proportion in the percentage map, weed canopy height measured in meters (m) map, and rice fraction cover map, were successfully produced from SAVI and GSAVI. [PERSON] et al. [49] also chose multispectral UAVs to map weeds in cereals.