AI-Based Crop Rotation & Smart Irrigation System with Intrusion Detection & IoT Monitoring

Electronics and Communication

Artificial intelligence (AI) and sustainability—two words not commonly used in the context of crop rotation management. However, simple AI-based expert systems supporting farms’ decision-making for optimizing crop rotation might be the key to solving most of the UN sustainable development goals worldwide. The essence of AI based crop rotation and farm management is that it works with nature—not against it. Thus, the AI-based expert system needs to solve the multidimensional optimization tasks of maximizing the diversity of crops that match the local soil, local climate conditions, the needs of the livestock, and any available machinery. Next to the optimization task, the expert system has to argue its decision-making basis since the goal is to achieve the most sustainable farming with the most profitable crop yield, not necessarily the largest yield. The expert system’s user interface (UI) should broaden thinking habits, offering new sustainable farming perspectives. This paper introduces both the technical architecture and the user-in-the-loop (UIL) principle of such an expert system. Since our system is still in the conceptual phase, we argue design decisions and address open research questions needed to implement such an expert system. Next to the option task, the expert system had to argue its decision-making basis since the goal is to get the most sustainable farming with the most profitable crop yield, not to get the largest yield. The expert systems’ user interface (UI) should broaden thinking habits, opting for new sustainable farming perspectives. This paper will introduce both the technical architecture and the user-in-the-loop (UIL) principle of such an expert system. Since our system is still in the conceptional phase, we argue design decisions and address open research questions needed to implement such an expert system

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