Comparative analysis of tiny machine learning models for maize crop disease identification

Authors

  • Fortunatus Aabangbio Wulnye Author
  • Ewura Abena Essanoah Arthur Author
  • Dennis Agyemanh Nana Gookyi Author

Keywords:

CNN, deep neural network, disease identification, EfficientNet, maize crop, MobileNet, SqueezeNet, ShuffleNet, Tiny Machine Learning

Abstract

This paper presents a comprehensive analysis of tiny machine-learning models for identifying maize crop diseases. The study evaluates custom deep neural network (DNN) models alongside popular deep learning architectures in terms of their effectiveness and efficiency in disease identification. The traditional crop disease detection methods such as manual checking of crop leaves for defects have become obsolete hence the need for modern and machine learning methods that are accurate and precise. This paper uses a dataset with maize crops as an example. This paper also compares four types of deep learning architectures used with custom deep learning architectures namely MobileNet, EfficientNet, ShuffleNet, and SqueezeNet. MobileNet is a lightweight CNN for mobile devices, it uses depth-wise separable convolutions for efficiency. EfficientNet is a scalable CNN architecture balancing depth, width, and resolution for optimal performance across resource-constrained devices. ShuffleNet is an optimized CNN with channel shuffle operations for cross-group information flow, ideal for resource-constrained environments. SqueezeNet is a compact CNN-based deep learning architecture utilizing fire modules to minimize parameters while preserving accuracy, it is suitable for IoT and embedded systems. The comparison of these architectures was done using the accuracy, loss, train time, and validations as the model selection criteria for comparison. The results show that MobileNet and SqueezeNet outperform both the Custom Model and ShuffleNet in terms of both test loss and test accuracy. EfficientNet, however, shows significantly poorer performance compared to the other models, particularly in terms of test accuracy. The Custom Model achieved a test loss of 0.15 with a test accuracy of .96.38%, while MobileNet attained a lower test loss of 0.098 and a higher test accuracy of 97.32%. In contrast, EfficientNet exhibited the highest test loss of 2.79 with the lowest test accuracy of 28.43%. ShuffleNet showed a test loss of 0.12 and a test accuracy of 96.18%, whereas SqueezeNet achieved a slightly lower test loss of 0.09 but a similar test accuracy of 96.84. Contribute to advancing the development of precision agriculture technologies tailored to address crop health challenges.

Author Biographies

  • Fortunatus Aabangbio Wulnye

    Fortunatus Aabangbio Wulnye is a dedicated scholar and aspiring expert in the field of Telecommunication Engineering, currently pursuing a Master of Philosophy (MPhil) in Telecommunication Engineering at Kwame Nkrumah University of Science and Technology (KNUST), Ghana. His academic journey began with a Bachelor of Science in Telecommunication Engineering, also attained at KNUST. Fortunatus holds certifications in Cybersecurity, Azure Development, SOC Analyst, and Huawei ICT Routing and Switching. He has showcased his commitment to academia through various roles at KNUST. He served as a Graduate Assistant from 2022 to 2023, aiding in academic endeavors while pursuing his graduate studies. Before this, he worked as a Research and Teaching Assistant from 2020 to 2021, contributing significantly to the university's research and educational initiatives. As Tratech Chairman for the Telecommunication Engineering Students Association and Electronics Arduino Tutor for the Ghana Engineering Students Association, Fortunatus displayed exceptional leadership in fostering learning environments and mentoring peers. His research interests revolve around the intersection of Machine Learning with Cyber-Physical Systems, Internet of Things (IoT), Data Networks, and Embedded Systems. Throughout his academic tenure, Fortunatus has actively participated in several noteworthy projects, including the design of an Event Management System in collaboration with Azubi Africa in 2020, a Facial Recognition-Based Home Security System at KNUST in the same year, and the development of a Waste Sorting Machine during the Korean Exchange Program in 2019.

  • Ewura Abena Essanoah Arthur

    Ewura Abena Essanoah Arthur is a graduate student currently pursuing a Master’s degree in Telecommunication Engineering at Kwame Nkrumah University of Science and Technology. She has gained working experience in the fields of Renewables and Telecommunications. She has a Bachelor of Science degree in Telecommunications Engineering from Kwame Nkrumah University of Science and Technology, Kumasi. Her research interests lie in the Internet of Things (IoTs), Wireless Sensor Networks (WSNs), and Artificial Intelligence. She has experience as a Teaching and Research Assistant at The Brew-Hammond Energy Centre, KNUST, where she helped fellows with their research and projects, summarized documents for applications, assisted the Director and the Administrative Assistant with office management, and composed and drafted correspondence and reports. She is currently involved in a research project funded by the UNESCO-TWAS program "Seed Grant for African Principal Investigators" financed by the German Federal Ministry of Education and Research (BMBF). The project aims to enable deep learning inference on low-cost edge devices for crop pest and disease identification.

  • Dennis Agyemanh Nana Gookyi

    Dennis Agyemanh Nana Gookyi received a B.Sc. degree in Computer Engineering from Kwame Nkrumah University of Science and Technology (KNUST), Ghana, in 2013 and M.Eng. and Ph.D. degrees in Information and Communication Engineering from Hanbat National University (HNU), South Korea in 2017 and 2021 respectively. He worked as a Researcher at the Intelligent Image Processing Research Center division of the Korea Electronics Technology Institute (KETI) from 2021 to 2022. He currently works as a Research Scientist at the Electronics Division of the Council for Scientific and Industrial Research, Institute for Scientific and Technological Information (CSIR–INSTI), Ghana. His research interests include Deep Learning Hardware Primitives Design, Tiny Machine Learning (TinyML) Hardware Implementation, System-on-Chip (SoC) Design, and Lightweight Cryptography.

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Published

06/13/2024

How to Cite

Wulnye, F. A., Arthur, E. A. E. ., & Gookyi , D. A. N. (2024). Comparative analysis of tiny machine learning models for maize crop disease identification. Journal of Applied Science and Information Technology, 1(1). https://jasit.csirgh.com/index.php/journal/article/view/1