气候变化领域集成服务门户(CCPortal): The integration of machine learning and IoT for the early detection of tomato leaf disease in real-time

CCPortal

DOI	10.47974/JIOS-1548
	The integration of machine learning and IoT for the early detection of tomato leaf disease in real-time
	Kumar, H. N. Naveen; Prasad, M. S. Guru; Gujjar, J. Praveen; Sharath, K. R.
发表日期	2024
ISSN	0252-2667
EISSN	2169-0103
起始页码	45
结束页码	2
卷号	45 期号:2
英文摘要	The impact of climate change, pests, and inadequate agricultural practices on crop health is becoming a growing concern. It is estimated that 20-40% of global crop yield is adversely affected by pests and diseases. This has a direct negative impact on food security and nutritional well-being, as staple cereal crops (such as rice, maize, and wheat) and tuber crops (like potato, onion, and tomato) are affected. Advancements in Artificial Intelligence (AI), Computer Vision (CV), and IoT have a significant influence on reducing crop losses by detecting crop diseases at early stages. The primary focus of this research is to develop a realtime system that can detect tomato crop diseases at an early stage by integrating Machine Learning (ML) algorithms and IoT. The most common tomato leaf diseases, such as Tomato Mosaic Virus (TMV), Tomato Bacterial Leaf Spot (TBLS), Tomato Early Blight (TEB), and Tomato Late Blight (TLB), are considered in this work. The hybrid discriminative feature space is derived from the integration of low- and high-level features via Convolution Neural Network (CNN) Layers. The 3-stage Stacked Deep Convolutional Autoencoder is used to optimize the CNN performance by reducing computation complexity. The proposed model is implemented on the Plantvillage benchmark dataset and achieves the highest recognition accuracy of 95.6% for the 5-class problem using 5-fold cross-validation.
英文关键词	Convolutional neural network (CNN); Stacked deep convolutional autoencoder (SDCA); Optimization in CNN model; Low; and high-level features; Plantvillage dataset
语种	英语
WOS研究方向	Information Science & Library Science
WOS类目	Information Science & Library Science
WOS记录号	WOS:001202153600009
来源期刊	JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/296643
作者单位	Vidyavardhaka College of Engineering; Graphic Era University; NITTE (Deemed to be University); NMAM Institute of Technology
推荐引用方式 GB/T 7714	Kumar, H. N. Naveen,Prasad, M. S. Guru,Gujjar, J. Praveen,et al. The integration of machine learning and IoT for the early detection of tomato leaf disease in real-time[J],2024,45(2).
APA	Kumar, H. N. Naveen,Prasad, M. S. Guru,Gujjar, J. Praveen,&Sharath, K. R..(2024).The integration of machine learning and IoT for the early detection of tomato leaf disease in real-time.JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES,45(2).
MLA	Kumar, H. N. Naveen,et al."The integration of machine learning and IoT for the early detection of tomato leaf disease in real-time".JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 45.2(2024).