Suitability analysis of rice varieties using learning vector quantization and remote sensing images

Apriliani, Annisa; Kusumaningrum, Retno; Endah, Sukmawati Nur; Prasetyo, Yudo

doi:10.12928/telkomnika.v17i3.12234

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…The competitive layer automatically learns to classify the input vectors based on distance calculation. LVQ network architecture consists of only input and output layers, which makes it relatively faster [28] and less time consuming than other ANN, such as multilayer perceptron [29].…”

Section: Learning Vector Quantizationmentioning

confidence: 99%

Improving learning vector quantization using data reduction

Semadi¹,

Pulungan²

2019

Int. J. Adv. Intell. Informatics

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Learning Vector Quantization (LVQ) is a supervised learning algorithm commonly used for statistical classification and pattern recognition. The competitive layer in LVQ studies the input vectors and classifies them into the correct classes. The amount of data involved in the learning process can be reduced by using data reduction methods. In this paper, we propose a data reduction method that uses geometrical proximity of the data. The basic idea is to drop sets of data that have many similarities and keep one representation for each set. By certain adjustments, the data reduction methods can decrease the amount of data involved in the learning process while still maintain the existing accuracy. The amount of data involved in the learning process can be reduced down to 33.22% for the abalone dataset and 55.02% for the bank marketing dataset, respectively.

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Section: Learning Vector Quantizationmentioning

confidence: 99%

Improving learning vector quantization using data reduction

Semadi¹,

Pulungan²

2019

Int. J. Adv. Intell. Informatics

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“…These methods are sometimes known as optical and thermal infrared of remote sensing. Previous studies were conducted based on passive or active microwave data to estimate the soil water substance in the surface soil layer within 0 to 10 cm [18,19].…”

Section: Introductionmentioning

confidence: 99%

Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

Tajudin

Ya’acob

Ali

et al. 2021

IJECE

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Soil moisture is one of the contributing factors that accelerates soil erosion and landslide events due to the increase in pore pressure which eventually reduces the soil strength. For landslide prediction and monitoring purposes, large-scale measurement involves estimating the soil moisture. However, estimation of soil moisture usually involves point-based measurements at a particular site and time, which is difficult to capture the spatial and temporal soil moisture dynamics. This paper presents the estimation of the SMI using Landsat 8 images for prediction and monitoring of landslide events in Ulu Kelang, Selangor. The selected SMI map for dry, moist, and wet seasons are obtained from climatology rainfall analysis over 20-year periods (1998-2017). SMI is assessed based on remote sensing data which are land surface temperature (LST) and normalized difference vegetation index (NDVI) using GIS software. Overall results indicated that rainfall distribution is high during inter-monsoon (IM), followed by northeast monsoon (NEM) and southwest monsoon (SWM) season. High rainfall distribution is a direct contributor towards SMI condition. Results from simulation show that April 2017 is known to have the highest SMI estimation season and selected to be the best SMI mapping parameter to be applied for prediction and monitoring of landslide events.

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Assessing Soil and Land Suitability of an Olive–Maize Agroforestry System Using Machine Learning Algorithms

Hayat,

Iqbal,

Ashworth

et al. 2024

Crops

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Exponential population increases are threatening food security, particularly in mountainous areas. One potential solution is dual-use intercropped agroforestry systems such as olive (Olea europaea)–maize (Zea mays), which may mitigate risk by providing multiple market sources (oil and grain) for smallholder producers. Several studies have conducted integrated agroforestry land suitability analyses; however, few studies have used machine learning (ML) algorithms to evaluate multiple variables (i.e., soil physicochemical properties and climatic and topographic data) for the selection of suitable rainfed sites in mountainous terrain systems. The goal of this study is therefore to identify suitable land classes for an integrated olive–maize agroforestry system based on the Food and Agriculture Organization (FAO) land suitability assessment framework for 1757 km2 in Khyber Pakhtunkhwa province, Pakistan. Information on soil physical and chemical properties was obtained from 701 soil samples, along with climatic and topographic data. After determination of land suitability classes for an integrated olive–maize-crop agroforestry system, the region was then mapped through ML algorithms using random forest (RF) and support vector machine (SVM), as well as using traditional techniques of weighted overlay (WOL). Land suitability classes predicted by ML techniques varied greatly. For example, the S1 area (highly suitable) classified through RF was 9%↑ than that of SVM, and 8%↓ than that through WOL. The area of S2 (moderately suitable) classified through RF was 18%↑ than that of SWM and was 17%↓ than the area classified through WOL; similarly, the S3 (marginally suitable) class area via RF was 27%↓ than that of SVM, and 45%↓ than the area classified through WOL. Conversely, the area of N2 (permanently not suitable class) classified through RF and SVM was 6%↑ than the area classified through WOL. Model performance was assessed through overall accuracy and Kappa Index and indicated that RF performed better than SVM and WOL. Crop suitability limitations of the study area included high elevation, slope, pH, and large gravel content. Results can be used for sustainable intensification in mountainous rainfed regions by expanding intercrop agroforestry systems in developing nations to close yield gaps.

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Suitability analysis of rice varieties using learning vector quantization and remote sensing images

Cited by 3 publications

References 19 publications

Improving learning vector quantization using data reduction

Improving learning vector quantization using data reduction

Soil moisture index estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

Assessing Soil and Land Suitability of an Olive–Maize Agroforestry System Using Machine Learning Algorithms

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