Green By Digital (GYD) Projects

Recently, food security has become a significant concern worldwide as a serious risk to sustainable societies. One of the key challenges facing food security is ensuring that plants are grown in optimal conditions. Therefore, it is important to implement effective monitoring systems in plant agriculture. In this paper, we present an Internet of Things (IoT) application for plant agriculture monitoring systems to help farmers monitor and track plant environmental conditions. The system consists of a remote camera, power source, a sensor module, and an AWS cloud platform. To reduce power consumption, it periodically measures various environmental parameters such as temperature, humidity, and soil moisture with power management configured. Then, it sends them to AWS securely with token-based authentication. It also captures the plant using a remote camera and sends it to a cloud. Finally, users can monitor and track the collected plant information through our system’s user interface from Android application on their mobile phones. We implement and verify the functionality of the proposed system through applications in a potted garlic plant on the rooftop at Okayama University.

Related publication:

1. Samsul Huda, Muhammad Bisri Musthafa, S. M. Shamim, and Yasuyuki Nogami, "A Study on Zeek IDS Effectiveness for Cybersecurity in Agricultural IoT Networks," Journal of Computational and Cognitive Engineering (JCCE), 2025. (Online First)

2. Samsul Huda, Md. Biplob Hossain, Maya Rahayu, Andri Santoso and Yasuyuki Nogami, "Design of Blockchain-Based Secure Device Authentication for IoT Plant Monitoring Systems," in 2025 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan 2025), July 16-18, 2025. (Presented)

3. Samsul Huda, Muhammad Bisri Musthafa and Yasuyuki Nogami, "A Performance Evaluation of Zeek-Based Intrusion Detection in Agricultural IoT Security," in IEEE International Conference on Consumer Electronics (ICCE 2025), pp. 1-6, January 2025. Best presentation award link

4. Samsul Huda, Yasuyuki Nogami, Maya Rahayu, Takuma Akada, Md. Biplop Hossain, Muhammad Bisri Musthafa, Yang Jie, and Le Hoang Anh, "IoT-Enabled Plant Monitoring System with Power Optimization and Secure Authentication," Computers, Materials & Continua, Vol. 81, No. 2, pp. 3165-3187, 2024. link

5. Samsul Huda, Muhammad Bisri Musthafa and Yasuyuki Nogami, "Zeek Intrusion Detection on Raspberry Pi for IoT-Based Agriculture Monitoring Systems: Preliminary Investigation," in IEEE International Symposium on Consumer Technology (ISCT 2024), pp. 372-377, August  2024. link

6. Samsul Huda, Yasuyuki Nogami, Yang Jie, Md. Biplop Hossain, Le Hoang Anh, Takuma Akada, Muhammad Bisri Musthafa and Maya Rahayu, "A Secure Authentication for Plant Monitoring System Sensor Data Access," in IEEE International Conference on Consumer Electronics (ICCE 2024), pp. 1-2, January 2024. link

7. Samsul Huda, Yasuyuki Nogami, Takuma Akada, Maya Rahayu, Md. Biplop Hossain, Muhammad Bisri Musthafa, Le Hoang Anh and Yang Jie, "A Proposal of IoT Application for Plant Monitoring System with AWS Cloud Service," in 2023 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1–5, July 2023. link

This project is adopting drones in AgriDX (Agricultural Digital Transformation). Drones have shown tremendous potential in revolutionizing the agricultural sector by providing valuable data and insights to farmers. Drones equipped with high-resolution cameras and sensors can capture images of crops from above. This aerial view allows farmers to monitor crop health, identify diseases, pests, nutrient deficiencies, and other issues that might not be visible from the ground. Early detection of problems can help optimize crop treatment and increase overall yield. The adoption of drones in AgriDX has the potential to significantly improve farming practices and contribute to more sustainable agriculture. As with any technology implementation, it's essential to consider factors like the initial investment cost, proper training for drone pilots, and data privacy issues. Additionally, adhering to local regulations and safety guidelines is crucial to ensure the responsible and safe use of drone technology in agriculture.

This project, in collaboration with AIDA Engineering (英田エンジニアリング ), aims to develop a smart pest detection system using YOLO (You Only Look Once), a cutting-edge deep learning model for object detection. As part of the development, we also applied detailed labelling and annotation to a curated dataset featuring 11 distinct types of agricultural pests, enabling the model to accurately classify and localize each pest in real-world crop images. This system supports precision agriculture by enabling early pest detection, minimizing crop damage, and promoting efficient, targeted pest management with minimal human intervention.