Abstract
In agriculture, plant disease detection is an important concern to achieve high crop production and yield sustainably. Automated detection and analysis could be beneficial for early action to prevent spreading, cure the plant in earlier stages, reduce the damage, and protect crop or forest health. This study proposes a new deep-learning model that correctly classifies plant leaf diseases for the agriculture and food sectors. It focuses on the detection of plant diseases for potato leaves from images by designing a new convolutional neural network architecture. The experimental results conducted on a real-world dataset showed that a significant improvement (8.6%) was achieved on average by the proposed model (98.28%) compared to the state-of-the-art models (89.67%) in terms of classification accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
