In the heart of Ukraine, at the Dnipro University of Technology, a groundbreaking study is revolutionizing the way we think about agriculture and disease prediction. Led by Grygorii Diachenko from the Department of Electric Drive, this research is not just about monitoring crops; it’s about creating a smarter, more efficient agricultural ecosystem. The study, published in the journal ‘Internet of Things’ (IoT), introduces a computer model that leverages the Internet of Things (IoT) and edge computing to predict crop diseases with unprecedented accuracy.
Imagine a world where farmers can anticipate and mitigate crop diseases before they even appear. This is the vision that Diachenko and his team are bringing to life. Their model combines wireless sensor networks and edge-computing technologies to monitor soil and climatic conditions in real-time. “The key is to aggregate and intelligently process agricultural monitoring data,” explains Diachenko. “By doing so, we can predict crop diseases and support decision-making processes in agriculture.”
The implications for the agricultural sector are immense. According to the Food and Agriculture Organization, plant diseases account for 20–40% of global crop production losses annually. This not only impacts farmers but also disrupts food supply chains and contributes to economic instability. Diachenko’s research aims to change this narrative by providing farmers with the tools they need to protect their crops and increase productivity.
The model developed by Diachenko and his team is based on an adaptive neuro-fuzzy inference system (ANFIS), integrated into the microcontroller unit of IoT systems for agricultural applications. This approach enables the optimization of the IoT system’s algorithmic and structural organization, making it reliable for agricultural monitoring in open fields. “We are essentially creating a networked agricultural ecosystem,” says Diachenko. “This connectivity provides farmers with invaluable information that allows them to monitor, analyze, and respond to dynamic environmental conditions and crop-specific needs.”
The study also highlights the importance of modelling in the development of IoT systems. Modelling allows for the simulation and analysis of proposed solutions before deployment, providing insight into system behavior, potential problems, and areas for improvement. In the case of crop disease prediction, modelling is becoming a critical step in enhancing algorithms and optimizing the performance of wireless sensor networks.
The research conducted by Diachenko and his team is a significant step forward in the field of agritech. It not only addresses the pressing need for sustainable and efficient agriculture but also contributes to the broader goals of digitalization and intellectualization of industrial ecosystems within the agricultural sector. As the world’s population continues to grow, the demand for food is increasing, making it necessary to optimize agricultural processes.
The study’s findings serve as a foundation for advancing the digitization and intellectualization of agricultural monitoring systems. By using embedded computer-oriented tools employing edge architecture, farmers can achieve more precise and timely interventions, reducing the need for chemical treatments and minimizing yield losses caused by crop diseases.
As we look to the future, the research conducted by Diachenko and his team at the Dnipro University of Technology offers a glimpse into a world where technology and agriculture converge to create a more sustainable and efficient food system. The integration of IoT, wireless sensor networks, and edge computing technologies is paving the way for a new era in agriculture, one where farmers are empowered with the tools they need to protect their crops and ensure a stable food supply. This research, published in the journal ‘Internet of Things’ (IoT), is a testament to the power of innovation and the potential it holds for transforming the agricultural landscape.