Recognition of Marijuana Plant Leaf Diseases Based on Deep Learning

dc.contributor.authorKong, Qingye
dc.contributor.authorTooprakai, Siraphop
dc.date.accessioned2026-08-06T10:49:25Z
dc.date.available2026-08-06T10:49:25Z
dc.date.issued2025-01-01
dc.description.abstractPlant pests and diseases are a common problem in agricultural production, and if not handled properly, they can seriously affect crop yield and quality. The era of the Internet of Things has arrived, and modern monitoring methods have also been applied to the growth monitoring of some crops, achieving good results. However, the cultivation of marijuana still relies mainly on traditional manual monitoring and modern methods have not yet been widely used. At the same time, the prevention of diseases and pests in marijuana is the focus, and if problems are not detected early, the losses can often be severe. This article proposes using machine learning to screen for abnormal leaves and confirm whether the leaves are healthy. After repeated training, the system can compare and classify different images of marijuana leaves and identify abnormal parts. The system is designed based on Python. The test results indicate that this technology can be applied to distinguish marijuana leaves to ensure early detection of diseases and pests, and to minimize agricultural losses caused by untimely remedial measures.
dc.identifier.citation2025 11th International Conference on Engineering Applied Sciences and Technology Iceast 2025 Proceeding, 99-102, 2025
dc.identifier.doi10.1109/ICEAST64767.2025.11088195
dc.identifier.other2-s2.0-105013472121
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16516
dc.source2025 11th International Conference on Engineering Applied Sciences and Technology Iceast 2025 Proceeding
dc.subjectDeep Learning
dc.subjectMachine Learning
dc.subjectMarijuana
dc.subjectPlant diseases
dc.subjectPython
dc.titleRecognition of Marijuana Plant Leaf Diseases Based on Deep Learning
dc.typeConference Paper

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