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Comparative Analysis of Detection of Diseases in Apple Tree Leaves Using ResNet and CNN
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Author(s): S. Aditi Apurva (Indian Institute of Information Technology, Ranchi, India)
Copyright: 2025
Pages: 18
Source title:
Scalable Modeling and Efficient Management of IoT Applications
Source Author(s)/Editor(s): Dharmendra Singh Rajput (Vellore Institute of Technology, India), Gotam Singh Lalotra (Government Degree College, Basohli, India), Vinod Kumar (Koneru Lakshmaiah Education Foundation, Guntur, India), Pushpendra Kumar (Central University of Jharkhand, India)and Harshita Patel (Vellore Institute of Technology, India)
DOI: 10.4018/979-8-3693-1686-3.ch006
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Abstract
The modern era has become an era of machine learning as an essential tool for developing IoT (internet of things) application. Machine learning for IoT can be used to depict future trends, detect anomalies, and argument intelligence by ingesting images, videos, and audios. Introducing IoT and machine learning in agricultural has empowered farmers to make and take well informed decision in optimal resource utilization as well as mitigation of pest and disease control. IoT and machine learning has aided in revolutionizing the farming sector. IoT sensors placed in the soil measure parameters like moisture content, pH levels, and nutrient levels. This chapter delves into a comparative analysis of two deep learning architectures, the residual neural network (ResNet), and convolutional neural network (CNN), for detecting diseases in apple tree leaves. By employing these models, the study aims to determine their performance in accurately identifying and classifying diseased apple tree leaves against healthy ones.
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