## 1 Introduction  
The agricultural sector in developing countries has multiple challenges, i.e., if we look at the production side, the first challenge which the farmer faces is the productivity challenge, as it becomes evident that developing countries are very low in productivity, i.e., for any crop, the particular developing country could be the highest producer but at the same time its productivity could be very low. Therefore, crop productivity is a significant factor which has to be looked at. The second challenge which the farmer faces is with respect to climate change, industrial pollution, and pest attacks, as they can damage crops substantially. It is deemed necessary for the farmer to mitigate those challenges by adapting to the latest technologies and insurance schemes. The third challenge is related to market connectivity, whereby the farmer produces crops and thus needs to be connected to distant markets as per the crop production analysis and its subsequent data insights. It is essential for the farmers to have all the information on a digital platform and there should be seamless trade between different markets and different places. However, this connection between farmers and the distant market is nowhere to be seen in developing countries. On the other hand, the industry is looking up for exports, which in turn need to be streamlined substantially. From the economic perspective of the developing country, it is a boon if more facility in nature and regulatory support has to be given for people who are looking to export outside their country or who are looking to have a value-added product across the globe. Now, this mismanagement in the agricultural sector can potentially lead to food security risk. Precision Agriculture (PA) is intended to help and maximize the development of the farming sector and will also help to ensure food security [1]. It is to be highlighted that PA is a high-tech farming technology that observes, measures, and analyzes farming fields and crops. With the advent of PA, on-field sensors can provide detailed levels of data for problems of soil and weather conditions pertaining to heavy metal toxics and climate change. Big data obtained from sensor networks and farm inputs tracking have a significant role to play to increase farm productivity, reduce environmental impacts, and improve human welfare [2]. By combining artificial intelligence-based big data analytics with sensor and image data, an integrated system could be developed for the agricultural domain. Implementing intensive, high-value, personalized management of crops would increase both production and economic performance. The aim of this paper is to highlight the importance of smart sensors and high-performance computing in protecting stakeholders in the agrifood value chain and providing them with unlimited access to a large dataset of various categories in order to track their farms. The challenges and consideration for the farming sector in developing countries are also highlighted. Figure 1 shows the number of articles reviewed in various domains in the proposed manuscript. The structure of the paper is arranged as follows: The first section of the paper explains the paper's scope and the reasoning for the proposed study. Section 2 gives an overview of food security by taking into consideration the pollution caused by persistent toxics and heavy metal. In addition to this, Section 2 also highlights the importance of smart farming based on IoT. Section 3 gives a detailed overview of the wireless communication protocols and sensors used in the agricultural domain. Furthermore, Section 3 also emphasizes the importance of edge computing and also highlights the model of High Performance Computing (HPC) on Edge. In addition, Section 3 also details the various edge computing  
Figure 1: Distribution of reviewed articles. methodologies used by previous researchers in precision agriculture. Section 4 highlights the current challenges and considerations for the agricultural sector in developing countries.