Original scientific paper
https://doi.org/10.1080/00051144.2024.2317093
CoDet: A novel deep learning pipeline for cotton plant detection and disease identification
R.T. Thivya Lakshmi
; Research Scholar, Anna University, Chennai, Tamil Nadu, India
*
Jeevaa Katiravan
; Department of IT, Velammal Engineering College, Chennai, Tamil Nadu, India
P. Visu
; Department of AI & DS, Velammal Engineering College, Chennai, Tamil Nadu, India
* Corresponding author.
Abstract
Cotton detection is a crucial component of the agricultural sector because it enables farmers to
correctly identify and keep track of the development of cotton crops. Systems for automatically
detecting cotton could boost output and efficiency while decreasing costs and waste in cotton
growing operations. New cotton detection systems have been developed as a result of recent
developments in machine learning and computer vision. These devices can precisely identify
and monitor cotton plants using images and sensor data. These systems assess and categorize
cotton plants according to their many spectral signatures using convolutional neural networks
(CNNs), deep learning algorithms, and hyperspectral imaging, among other methods. The use
of cotton detection technologies can help with problems related to crop diseases, pests, and
environmental factors in addition to enhancing crop management and production optimization.
Farmers and researchers may spot possible issues early and take corrective action to decrease
risks and promote healthy crop growth by offering real-time monitoring and data analytics. As
cotton detecting technologies have the potential to alter the cotton farming sector and improve
environmentally friendly farming techniques, they represent a promising area for research and
development. The proposed pipeline demonstrates how cotton may be recognized quickly and
reliably.
Keywords
Computer vision; catmull rom; convolutional networks; feature maps
Hrčak ID:
323055
URI
Publication date:
13.2.2024.
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