Date of Award
Spring 2022
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2022.
Department
Electrical and Computer Engineering
First Advisor
Tokunbo Ogunfunmi
Abstract
Agriculture is such a vital part of our society, and according to the United Nations’ Food and Agricultural Organization (FAO), plant diseases are considered one of the two main causes of decreasing food availability. This paper explores not only the methods and findings of building a CNN-based disease detection model, but that of building a deployable remote crop disease detection system incorporating IoT technology. By using transfer learning with AlexNet, we were able to predict with 89.8% accuracy tomato plant images into one of the ten pre-defined disease classes. Our proposed system tracks plant health throughout the day by using a microprocessor and a camera to automatically capture images, diagnose the plant, and report results. The system is a proof of concept of a technology that can significantly help increase crop yield, reduce food waste, and automate the tasks of detecting and caring for diseased crops.
Recommended Citation
Chung, Ivy and Gupta, Anoushka, "Remote Crop Disease Detection using Deep Learning with IoT" (2022). Electrical and Computer Engineering Senior Theses. 72.
https://scholarcommons.scu.edu/elec_senior/72