Authors :
Sri M.Chilaka Rao; Sharun Kumar; Kishore; Rakesh Kumar; SravanKumar
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/hu4emryt
Scribd :
https://tinyurl.com/yme6cp95
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR444
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Identification of plant diseases is crucial for
preserving crops and ensuring food security. Analysis of
detectable chemicals in plants is essential to understand
transmission mechanisms and develop effective
strategies for disease control measures to conserve
agricultural products and prevent losses. However,
manual monitoring of plant health is labor-intensive and
time-consuming, requiring specialized skills and
knowledge. To overcome these challenges, random forest
systems are emerging as a powerful tool for disease
detection and classification in plants. The process
involves several steps, including image acquisition,
preprocessing, and segmentation, followed by feature
extraction, model training, and testing. Leveraging
machine learning techniques, the random forest
algorithm enables accurate classification of healthy and
diseased leaves based on selected features. Image
classification techniques are utilized to extract color
information, while global features such as size and
texture are captured through annotation. The dataset
used for model training and testing comprises diverse
samples, encompassing healthy and diseased plants. The
random forest model is trained on 70% of the data to
ensure robust learning, while the remaining 30% is
reserved for testing, facilitating the exploration of model
performance and overall feasibility
Identification of plant diseases is crucial for
preserving crops and ensuring food security. Analysis of
detectable chemicals in plants is essential to understand
transmission mechanisms and develop effective
strategies for disease control measures to conserve
agricultural products and prevent losses. However,
manual monitoring of plant health is labor-intensive and
time-consuming, requiring specialized skills and
knowledge. To overcome these challenges, random forest
systems are emerging as a powerful tool for disease
detection and classification in plants. The process
involves several steps, including image acquisition,
preprocessing, and segmentation, followed by feature
extraction, model training, and testing. Leveraging
machine learning techniques, the random forest
algorithm enables accurate classification of healthy and
diseased leaves based on selected features. Image
classification techniques are utilized to extract color
information, while global features such as size and
texture are captured through annotation. The dataset
used for model training and testing comprises diverse
samples, encompassing healthy and diseased plants. The
random forest model is trained on 70% of the data to
ensure robust learning, while the remaining 30% is
reserved for testing, facilitating the exploration of model
performance and overall feasibility
