CoDet: A novel deep learning pipeline for cotton plant detection and disease identification

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