Efficient Plant Disease Detection on RISC Devices: A comparison of Basic CNN, AlexNet, ResNet-50, and MobileNet Models using MiniTensorFlow

Authors

  • Rushikesh S. Tanksale Department of Computer Engineering & IT, COEP Technological University, Pune, Maharashtra, India
  • Sunil B. Mane Department of Computer Engineering & IT, COEP Technological University, Pune, Maharashtra, India

Keywords:

Precision Agriculture, Edge Computing, Embedded Systems, IoT, Deep Learning, Model Comparison, Real-time Detection, Disease Classification, Edge AI

Abstract

This study presents a meticulous comparison of plant disease detection models on the Raspberry Pi 5 platform, employing Basic CNN, AlexNet, ResNet-50, and MobileNet architectures through MiniTensorflow. Our investigation scrutinizes response time latency, individual plant image performance, and overall model efficiency and accuracy. The assessment includes a diverse dataset, the New Plant Diseases Dataset from Kaggle, encompassing various plant species and diseases. Response time latency is measured to gauge the processing speed of each model, while individual plant image analysis identifies potential efficiency variations across different plant types. A user-friendly web application, developed using Python Flask, facilitates model accessibility and real-time testing. The study transcends traditional accuracy metrics, offering insights into each model's nuanced strengths and limitations. This research contributes a valuable perspective on the suitability of these models for real-world deployment on the widely used Raspberry Pi 5, essential for practitioners and researchers in the field of plant disease detection.

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Published

23.02.2024

How to Cite

Tanksale, R. S. ., & Mane, S. B. . (2024). Efficient Plant Disease Detection on RISC Devices: A comparison of Basic CNN, AlexNet, ResNet-50, and MobileNet Models using MiniTensorFlow. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 374–383. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4849

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

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