A brief study on paddy applications with image processing and proposed architecture

Mutalib, Sofianita; Abdullah, Mohd Hafiz Bin; Abdul-Rahman, Shuzlina; Aziz, Zalilah Abd

doi:10.1109/spc.2016.7920716

Cited by 15 publications

(7 citation statements)

References 19 publications

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“…Based on one of the research on image processing for paddy in [22], pictures of paddy leaves taken from a UAV and with lens correction technique contained RGB images, successfully produced weighted accuracy of about 95.04%, which shows that detection using RGB image can generate accurate and detailed pictures as applied in this paper. This image will be processed by using image processing algorithm to enable its features to be extracted.…”

Section: Real-time Appearance-based Algorithmmentioning

confidence: 84%

“…This image will be processed by using image processing algorithm to enable its features to be extracted. According to Mutalib et al [22], image pre-processing that is frequently used is the conversion from RGB to Grayscale image. In addition, Kahar et al [23], in their research on detection for paddy diseases, also included reading and extracting the RGB colour from images of paddy leaves to be converted into grayscale.…”

Section: Real-time Appearance-based Algorithmmentioning

confidence: 99%

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Visual analytics of 3D LiDAR point clouds in robotics operating systems

Azri¹,

Abdul-Rahman²,

Hamzah³

et al. 2020

Bulletin EEI

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This paper presents visual analytics of 3D LiDAR point clouds in robotics operating system. In this study, experiment on Simultaneous Localization and Mapping (SLAM) using point cloud data derived from the Light Detection and Ranging (LiDAR) technology is conducted. We argue that one of the weaknesses of the SLAM algorithm is in the localization process of the landmarks. Existing algorithms such as Grid Mapping and Monte Carlo have limitations in dealing with 3D environment data that have led to less accurate estimation. Therefore, this research proposes the SLAM algorithm based on Real-Time Appearance-Based (RTAB) and makes use of the Red Green Blue (RGB) camera for visualisation. The algorithm was tested by using the map data that was collected and simulated on the Robot Operating System (ROS) in Linux environment. We present the results and demonstrates that the map produced by RTAB is better compared to its counterparts. In addition, the probability for the estimated location is improved which allows for better vehicle maneuverability.

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Section: Real-time Appearance-based Algorithmmentioning

confidence: 84%

Section: Real-time Appearance-based Algorithmmentioning

confidence: 99%

Visual analytics of 3D LiDAR point clouds in robotics operating systems

Azri¹,

Abdul-Rahman²,

Hamzah³

et al. 2020

Bulletin EEI

Self Cite

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“…In this chapter we reviewed importance of ICT in data collection and some traditional image processing in order to understand input and output applications with image processing [4]for making formers decision.…”

Section: Literature Riviewmentioning

confidence: 99%

Classification and Detection Rice leaf Diseases Using Information and Communication Technology (ICT) Tools

fan¹,

Roy²,

Roy³

2020

IJAERS

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Despite sustainable development, increase in use smartphones, drones, satellite and other information communication technologies for data collection and analyzing for decision marking. Crop losses due to insect pests and diseases are a major threat to farming communities globally. In the case of rice, up to 37% of economic losses are caused by insect pests and disease infestation. Timely and accurate disease and insect pest diagnosis and management can not only reduce crop losses. In the last decade, Information and communication technologies (ICTs) have been increasingly used for information sharing. With mobile internet services becoming available in even the furthest locations, ICT-based agricultural solutions are finding a foothold on the farms of poor smallholders. ICT-based tool that supports diagnosis of insect pests and diseases and enables farmers to make timely decisions for better pest management. To identify the rice diseases at any untimely phases is not yet explored. Early classify and detection for estimation of severity effect or incidence of diseases can save the production from quantitative and qualitative losses, reduce the use of pesticide, and increase country's economic growth. The main challenges is to minimize the impacts of attacks. Detection of plant disease through some automatic technique is beneficial as it requires a large amount of work of monitoring in big farm of crops, and at very early stage itself it detects symptoms of diseases means where they appear on plant leaves. In this paper we review different disease classification techniques that can be used for plant leaf disease detection. Also we describe the data collection by information communications technology for rice leaf, different disease classification approaches that can be used for rice diseases detection. Thirdly we suggested the framework for convolutional neural network in Agricultural sector for detection and identification for innovation technology in agriculture.

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“…The recognition of flower species requires significant knowledge from the field of botanical science and experiences. With the growth of machine learning, recognition algorithms have indeed aided scientists to identify plants including flowers for further scientific research [1,2]. Recognition of numerous plant species will support the development of the pharmaceutical industry especially now as medical plants are considered to having lesser adverse reactions and being less expensive than contemporary medication [3].…”

Section: Introductionmentioning

confidence: 99%

Flower Recognition using Deep Convolutional Neural Networks

Hanafiah

Adnan

Abdul-Rahman

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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This study investigates the suitable model for flower recognition based on deep Convolutional Neural Networks (CNN) with transfer learning approach. The dataset used in the study is a benchmark dataset from Kaggle. The performance of CNN for plant identification using images of flower are investigated using two popular image classification models: AlexNet and VGG16. Results show that CNN is proven to produce outstanding results for object recognition, but its achievement can still be influenced by the type of images and the number of layers of the CNN architecture. The models produced adequate performance rates, with the VGG16 model achieving the best results. AlexNet and VGG16 models achieved the accuracy of 85.69% and 95.02% respectively. This model can be replicated for flower recognition in other areas, especially in our national heritage, Taman Negara which is among the richest flora ecosystem in the world. The significant feature extraction processes were discussed as well, and this is useful for other types of flowers than the trained dataset.

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A brief study on paddy applications with image processing and proposed architecture

Cited by 15 publications

References 19 publications

Visual analytics of 3D LiDAR point clouds in robotics operating systems

Visual analytics of 3D LiDAR point clouds in robotics operating systems

Classification and Detection Rice leaf Diseases Using Information and Communication Technology (ICT) Tools

Flower Recognition using Deep Convolutional Neural Networks

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