枯萎病
鉴定(生物学)
计算机科学
Rust(编程语言)
农业
植物病害
限制
图像处理
小粒咖啡
叶斑病
机器视觉
人工智能
模式识别(心理学)
生物技术
生物
园艺
工程类
植物
图像(数学)
机械工程
生态学
程序设计语言
作者
Divyashri. P,Lishma Anisha Pinto,Ligina Mary,Manasa. P,Sandhya Dass
出处
期刊:2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC)
日期:2021-08-04
被引量:1
标识
DOI:10.1109/icesc51422.2021.9532662
摘要
Coffee plants are extremely susceptible to a wide range of pests and diseases. Usage of reckless pesticides can lead to increased pathogen resistance in the long run, severely limiting coffee plants' ability to fight. Plant health monitoring and disease detection in plants are very critical for sustainable agriculture. As there is a rapid rise in the number of diseases and low awareness of these conditions, disease detection and prevention remain a major concern. Coffee leaves have certain textures and visually striking similarities that can be used to identify the disease type. The proposed solution is used to detect coffee leaf diseases and classify them into five categories: healthy, diseased leaves with Brown eye spots, Coffee Leaf Blight, Coffee Leaf Rust, and Coffee Leaf Miner. The paper focuses on building a CNN model which processes the leaf images that can be used for the detection of plant diseases with digital image processing techniques for disease diagnosis using visual image processing techniques. This solution serves its purpose by identifying and classifying the diseases based on the features derived from the Coffee leaf picture samples with an accuracy of 88.35%. With the aid of the above technique, coffee plant farmers may be able to identify diseases more quickly, increasing India's coffee production output. The proposed system was designed to benefit farmers and the agricultural sector, especially in the Karnataka region.
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