## 1 Introduction  
The need to increase farm production has become crucial due to diminishing agricultural fields and the loss of scarce natural resources [1; 2]. The lack of natural resources such as fresh water and arable land, as well as decreased yield rates in many staple commodities, have made the issue worse [3; 4]. Another looming issue for the farming sector is the shifting demographics of the agricultural workforce. Internet of Things strategies are targeted at assisting farmers in bridging the supply-demand gap by maintaining high yields, sustainability, and environmental security [5]. Increasing crop yields while reducing operating costs can be accomplished through precision agriculture, which uses IoT technologies [6; 7]. Agricultural IoT innovations provide advanced devices and wireless networking [8; 9]. Based on IoT technology, smart farming assists growers and producers in reducing pollution and increasing efficiency in a variety of ways, including the quantity of fertilizer used, the number of trips made by farm cars, and the efficient use of resources such as water and power [10; 11; 12]. IoT smart farming solutions is a device that uses sensors to track the crop field and automate the irrigation system (light, humidity, temperature, soil moisture, crop protection, etc.) [13; 14; 15]. The agriculture sector is in its infancy when technological aspects are considered. In traditional farming, farmers have to bear financial losses due to various reasons such as incorrect weather prediction, amount of pesticides and fertilizers to be sprayed, incorrect irrigation, plant diseases and pests, and low yield of crops [16; 17]. The younger generation of farmers reining in the agricultural business now demands a smart system for improving the production of crops [18; 19]. The issues of traditional farming and the demands of younger farmers will be addressed by introducing IoT in the agricultural sector with cost-effective solutions. Crop disease forecasting is crucial because it helps farmers plan their crop production and minimise risks. Using information gathered from farm sensors, artificial networks are utilized to forecast crop illnesses and pests [20; 21]. This information includes variables including soil, temperature, pressure, rainfall, and humidity [22; 23; 24; 25]. This information also helps in estimating the severity of the crop diseases [26]. Bibliometric Analysis of the role of IoT in Smart Agriculture: The base word, bibliometric, is made up of two words that come from Greek and Latin words, where 'biblio' means book or paper and'metrics' means to measure. [PERSON] introduced the concept of bibliometrics in 1969. In accordance with his definition, bibliometrics refers to the application of mathematical and statistical approaches and models to books and some other forms of written communication, including journals, e-books, theses, reports, etc. Once the article is published, it is necessary to understand its influence and its impact [27; 28]. The impact can be understood by the number of citations received to a particular research paper. Thus, to understand the influence and impact of the research document, it is necessary to conduct a bibliometric analysis. Lotka's Law, Bradford's Law, and Zipf's Law are the three bibliometrics laws [29]. 1. Lotka's law: It was introduced in 1934 and is also known as the author's productivity. It measures the productivity of an author based on the number of published articles. 2. Bradford's law: It is known as the law of scattering. It measures the scattering of articles on a particular subject in various periodicals. 3. Zipf's Law: It is known as the law of word frequency, and is used to predict the occurrence of the words in the article. Thus, a bibliometric analysis is useful for researchers who are beginners. This analysis provides insights into what kind of research work is already conducted by the researcher in the field. Additionally, it can be used to identify the areas of research that need further study.