Cover Image

Assessment of crops healthiness via deep learning approach: Python / Mohamad Amirul Asyraf Mohd Ramli

Detecting healthy crops using Python in the context of an analysis project has emerged as an approach that speeds up a process to find out the current state of crops. This study focuses on using Python for remote sensing data analysis to identify and classify healthy crops. By leveraging image proce...

Full description

Saved in:
Bibliographic Details
Main Author: Mohd Ramli, Mohamad Amirul Asyraf
Format: Student Project
Language:English
Published: 2023
Subjects:
Remote Sensing
Online Access:https://ir.uitm.edu.my/id/eprint/87930/1/87930.pdf
https://ir.uitm.edu.my/id/eprint/87930/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uitm.ir.87930
record_format eprints
spelling my.uitm.ir.879302023-12-10T09:29:14Z https://ir.uitm.edu.my/id/eprint/87930/ Assessment of crops healthiness via deep learning approach: Python / Mohamad Amirul Asyraf Mohd Ramli Mohd Ramli, Mohamad Amirul Asyraf Remote Sensing Detecting healthy crops using Python in the context of an analysis project has emerged as an approach that speeds up a process to find out the current state of crops. This study focuses on using Python for remote sensing data analysis to identify and classify healthy crops. By leveraging image processing techniques, statistical analysis and machine learning algorithms, Python enables the extraction of relevant features and patterns from data. This feature includes spectral information, vegetation indices and other quantitative metrics that indicate plant health. This study addresses challenges related to data acquisition, preprocessing, feature extraction, and results. The importance of this study lies in its potential to provide an accurate and efficient algorithm for plant health assessment, in making informed decisions. By using Python in analytics projects, farmers can identify areas of concern, monitor crop health trends, and implement targeted interventions to optimize resource use and maximize yields. This research utilizing the Python programming language and the PyCharm integrated development environment (IDE) to integrate coding into the processing. This research utilized several libraries in PyCharm, including NumPy, Rasterio, and Matplotlib. Furthermore, these libraries provide an essential functionalities for data processing and visualization tasks .These findings emphasize the importance of a data-driven approach and the integration of Python in analytical projects helping to better crop management practices, increased sustainability and increased productivity in the agricultural sector. 2023-08 Student Project NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/87930/1/87930.pdf Assessment of crops healthiness via deep learning approach: Python / Mohamad Amirul Asyraf Mohd Ramli. (2023) [Student Project] (Submitted)
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Remote Sensing
spellingShingle Remote Sensing
Mohd Ramli, Mohamad Amirul Asyraf
Assessment of crops healthiness via deep learning approach: Python / Mohamad Amirul Asyraf Mohd Ramli
description Detecting healthy crops using Python in the context of an analysis project has emerged as an approach that speeds up a process to find out the current state of crops. This study focuses on using Python for remote sensing data analysis to identify and classify healthy crops. By leveraging image processing techniques, statistical analysis and machine learning algorithms, Python enables the extraction of relevant features and patterns from data. This feature includes spectral information, vegetation indices and other quantitative metrics that indicate plant health. This study addresses challenges related to data acquisition, preprocessing, feature extraction, and results. The importance of this study lies in its potential to provide an accurate and efficient algorithm for plant health assessment, in making informed decisions. By using Python in analytics projects, farmers can identify areas of concern, monitor crop health trends, and implement targeted interventions to optimize resource use and maximize yields. This research utilizing the Python programming language and the PyCharm integrated development environment (IDE) to integrate coding into the processing. This research utilized several libraries in PyCharm, including NumPy, Rasterio, and Matplotlib. Furthermore, these libraries provide an essential functionalities for data processing and visualization tasks .These findings emphasize the importance of a data-driven approach and the integration of Python in analytical projects helping to better crop management practices, increased sustainability and increased productivity in the agricultural sector.
format Student Project
author Mohd Ramli, Mohamad Amirul Asyraf
author_facet Mohd Ramli, Mohamad Amirul Asyraf
author_sort Mohd Ramli, Mohamad Amirul Asyraf
title Assessment of crops healthiness via deep learning approach: Python / Mohamad Amirul Asyraf Mohd Ramli
title_short Assessment of crops healthiness via deep learning approach: Python / Mohamad Amirul Asyraf Mohd Ramli
title_full Assessment of crops healthiness via deep learning approach: Python / Mohamad Amirul Asyraf Mohd Ramli
title_fullStr Assessment of crops healthiness via deep learning approach: Python / Mohamad Amirul Asyraf Mohd Ramli
title_full_unstemmed Assessment of crops healthiness via deep learning approach: Python / Mohamad Amirul Asyraf Mohd Ramli
title_sort assessment of crops healthiness via deep learning approach: python / mohamad amirul asyraf mohd ramli
publishDate 2023
url https://ir.uitm.edu.my/id/eprint/87930/1/87930.pdf
https://ir.uitm.edu.my/id/eprint/87930/
_version_ 1787139561665593344
score 13.211869

Similar Items