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Agriculture Cyber-Physical System (A-CPS) is becoming increasingly important in enhancing crop quality and productivity by utilizing minimum cropland. This paper introduces the innovative idea of the Internet-of-Agro-Things (IoAT) with an explanation of the automatic detection of plant disease for the development of ACPS. Majority of the crops were infected by microbial diseases in conventional agriculture. Also, the constantly mutating pathogens cannot be known to the knowledge of the farmer, due to which, there arises a demand to develop a disease prediction system. To prevent this, we use a trained Convolutional Neural Network (CNN) model to perform an analysis of the crop image captured by a health maintenance system. The image capturing along with continuous sensing and intelligent automation is performed by the solar sensor node. The sensor node houses a developed soil moisture sensor which has a high longevity compared to its peers. A real time implementation of the proposed system is demonstrated using a solar sensor node with a camera module, a microcontroller and a smartphone application using which a farmer can monitor the field. The prototype was deployed for three months and has achieved a robust performance by remaining rust-free and sustaining the varied weather conditions. An accuracy of 99.24% is achieved by the proposed plant disease prediction framework.

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The concept of Artificial Intelligence has gained a lot of attention over the last decade. In particular, AI-based tools have been employed in several scenarios and are, by now, pervading our everyday life. Nonetheless, most of these systems lack many capabilities that we would naturally consider to be included in a notion of "intelligence". In this work, we present an architecture that, inspired by the cognitive theory known as Thinking Fast and Slow by D. Kahneman, is tasked with solving planning problems in different settings, specifically: classical and multi-agent epistemic. The system proposed is an instance of a more general AI paradigm, referred to as SOFAI (for Slow and Fast AI). SOFAI exploits multiple solving approaches, with different capabilities that characterize them as either fast or slow, and a metacognitive module to regulate them. This combination of components, which roughly reflects the human reasoning process according to D. Kahneman, allowed us to enhance the reasoning process that, in this case, is concerned with planning in two different settings. The behavior of this system is then compared to state-of-the-art solvers, showing that the newly introduced system presents better results in terms of generality, solving a wider set of problems with an acceptable trade-off between solving times and solution accuracy.