Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies.

Conversational systems are now applicable to almost every business domain. Evaluation is an important step in the creation of dialog systems so that they may be readily tested and prototyped. There is no universally agreed upon metric for evaluating all dialog systems. Human evaluation, which is not computerized, is now the most effective and complete evaluation approach. Data gathering and analysis are evaluation activities that need human intervention. In this work, we address the many types of dialog systems and the assessment methods that may be used with them. The benefits and drawbacks of each sort of evaluation approach are also explored, which could better help us understand the expectations associated with developing an automated evaluation system. The objective of this study is to investigate conversational agents, their design approaches and evaluation metrics. This approach can help us to better understand the overall process of dialog system development, and future possibilities to enhance user experience. Because human assessment is costly and time consuming, we emphasize the need of having a generally recognized and automated evaluation model for conversational systems, which may significantly minimize the amount of time required for analysis.

The fourth agricultural revolution has popularized the usage of information and communication technologies (ICTs) in farms, which has been shown to improve productivity [1], reduce labor, and help mitigate unforeseen circumstances through predictive modeling [2]. This practice is referred to as smart farming and has seen several successful applications worldwide in recent years [3]. Smart farming projects are conducted over a long period of time and can have a noteworthy impact [4], [5], [6], [7], [8]. Some major concerns regarding the new smart farming technologies are data and privacy concerns [9], lack of reliable infrastructure and equipment [10], laws and regulations in a particular location, training, and skill acquisition [11], and apprehension associated with the adoption of new technology. Farmers need to be provided with proper training and support to help them adopt new sustainable practices [12], [13]. It is also important to monitor the sentiments of farmers and the public toward innovations in the field of smart farming to allow for easier adaptation to changes and enhance future projects. Sentiment analysis can highlight the aspects of smart farming, which are positively and negatively perceived by the public, which can uncover areas for improvement. Insights derived through sentiment analysis can be invaluable for various shareholders, as it can signal market demand for smart farming tools.