Mohd Nazrin Md Isa
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Preferred name
Mohd Nazrin Md Isa
Official Name
Mohd Nazrin , Md Isa
Alternative Name
Md Isa, Mohd N.
Isa, M. Nazrin Md
Md Isa, M. N.
Isa, Nazrin
Isa, M. Nazrin M.
Main Affiliation
Scopus Author ID
56426995200
Researcher ID
N-1250-2017

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IoT Based Soil Nutrient Sensing System for Agriculture Application

2021-12-01 , Othaman N.N.C. , Mohd Nazrin Md Isa , Razaidi Hussin , Zakaria S.M.M.S. , Isa M.M.

Rice is the primary food source for millions of Asians and satisfies the most fundamental requirement for human survival. The paddy scarcity has heightened public awareness of the global food problem. Rice yield and quality are affected by various factors, including soil nutrients, irrigation, types of soil, and pests. This work proposed developing an Internet of Things (IoT) based mobile device for measuring soil nutrients in real-time. The proposed system consists of electrical conductivity (EC) and temperature sensors with TTGO T-Beam microcontroller and IoT connectivity. During experimental work, the results showed that the observed EC data near the calibration solution conductivity of 12.88mS/cm and 150mS/cm, which are less than 2% from the calibration solution's stated value. Furthermore, it is found that the measured EC value increases with temperature (linearly proportional). The study showed that the soil's EC of sensor node 1 at 5 cm depth without fertiliser is 1.04375mS/cm and with fertiliser is 3.86mS/cm, while at 10 cm depth without fertiliser is 0.65625mS/cm and with fertiliser is 420mS/cm. These investigations show that soil EC is directly linked to nutrient availability and soil depth.

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Image Processing for Paddy Disease Detection Using K-Means Clustering and GLCM Algorithm

2021-12-01 , Ahmad Effendi A.F.A. , Mohd Nazrin Md Isa , Ahmad M.I. , Che Husin M.F. , Siti Zarina Md Naziri

The traditional human-based visual quality inspection approach in agriculture is unreliable and uneven due to various variables, including human errors. In addition to the lengthy processing durations, the traditional method necessitates plant disease diagnostic experts. On the other hand, existing image processing approaches in agriculture produce low-quality output images despite having a faster computation time. As a result, a more comprehensive set of image processing algorithms was used to improve plant disease detection. This research aims to develop an efficient method for detecting leaf diseases using image processing techniques. In this work, identifying paddy diseases based on their leaves involved a number of image-processing stages, including image pre-processing, image segmentation, feature extraction, and eventually paddy leaf disease classification. The proposed work targeted the segmentation step, whereby an input image is segmented using the K-Means clustering with image scaling and colour conversion technique in the pre-processing stage. In addition, the Gray Level Co-occurrence Matrix technique (GLCM) is used to extract the features of the segmented images, which are used to compare the images for classification. The experiment is implemented in MATLAB software and PC hardware to process infected paddy leaf images. Results have shown that K-Means Clustering and GLCM are capable without using the hybrid algorithm on each image processing phase and are suitable for paddy disease detection.

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A Novel Double Co-Transformation for a Simple and Memory Efficient Logarithmic Number System

2020-07-01 , Basir M.S.S.M. , Rizalafande Che Ismail , Mohd Nazrin Md Isa

To date, co-transformation architecture is typically used in resolving the singularity issue in the logarithmic number system (LNS). The co-transformation was first introduced by Coleman, by using a rule of sign(r1) ≠ sign(r2) which translate the singularity into an argument that can be stored in two identical look-up tables (LUTs) with size of 2k. Recently, a portable 32-bit chipset preferred a small LUT, hitherto a co-transformation architecture is rearranged. This paper presents a novel double co-transformation, by means of first-order co-transformation architecture that covers-0.5 < r < 0 region is extended to r >-1 to replace the triumvirate F, D and E tables occupy by the interpolator. The accuracy settings at the co-transformation is compromised with the worst case error of 0.5 ulp. The outcome revealed a double co-transformation with Lagrange interpolator shows a decline in the total bit by 13% compared to European Logarithmic Microprocessor (ELM). With a simple architecture, the proposed double co-transformation is a promise for a fast LNS system.

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