# A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder Agriculture
## 1 Introduction  
In the 1930s, one farmer in the United States could grow enough to feed four people. Today, one farmer can feed 155 people [1]. Approximately 1 billion people worldwide are involved in agriculture, and although the total number of farmers are declining, the demand for agricultural crops is expected to double as the world population reaches 9 billion by 2050 [2]. This will require an increase in agricultural productivity, especially from low- and middle-income countries (LMICs). Recent technological advances have contributed to the rise of precision agriculture, enabling farmers to make better decisions with more information about their soil, water, crop, and local climate [3; 4]. However, uptake of these advances has been limited to commercial-scale and cash-crop cultivation [1]. There is a growing body of evidence, which indicates that the implementation of precision agriculture tools and practices across different types of farming offers benefits. For example, a digital decision support system (DSS) that utilized crop information provided by growers, in combination with weather data, effectively alerted growers when to apply fungicide to their potatoes, resulting in the effective mitigation of late blight disease and more efficient fungicide use, saving them up to 500 USD per acre cultivated [5; 6; 7]. Another DSS platform integrated weather data and electrical capacitance sensors for real-time monitoring of soil water content along with soil water balance and irrigation scheduling models to provide recommendations to durum wheat farmers on timing and intensity of irrigation, resulting in water savings of at least 25% compared to traditional schedulingpractices [8]. In another common application, optical sensors attached to fertilizer equipment measure the light reflectance at specific wavebands, which are related to chlorophyll content, an indicator of plant nitrogen (N) content, and fertilizer is applied in that location based on the estimated N deficiency. Compared to the grower selected constant-rate, sensor-enabled variable-rate fertilization has been shown to increase corn N uptake by more than 20% [9] and save 10-15 kg N per acre of corn and cotton, especially when soil N content varies year-to-year [10]. Smallholder farms account for a large share of agricultural production in LMICs and help maintain the genetic diversity of food supply, and mitigate the risks of nutritional deficiencies and ecosystem degradation [11]. However, they face challenges including pressures on farmland from population growth and urbanization, and an inability to rely on generational knowledge and practices due to climate change. Technology advances made through precision agriculture could address some of these challenges [3; 12; 13; 14]. The internet-of-things (IoT) and the services that it enables has contributed to the rise of precision agriculture, and it is being applied in smallholder agriculture [4; 15; 16]. However, limited research exists that reports outcomes and challenges for implementation of the IoT in smallholder agriculture [3; 17]. Thus, this article aims to illustrate the current landscape of the IoT in smallholder farming, summarize specific implementation cases, and describe some of the challenges and recommendations in order to direct this sector. A typical IoT for agriculture system consists of the following: (i) measurement device, (ii) data transmission, (iii) data storage and analytics, (iv) feedback and implementation, and (v) project structure and support (Figure 1). The device layer consists of a sensor to measure the parameter of interest (e.g., soil moisture) and the electronics necessary to support its functions. Devices are arranged in a topology and connected to a gateway using a communication protocol in the data transmission layer. Broad coverage of cellular networks allows for frequent, and in some cases near real-time, data transmission. Individual or aggregated measurements are received by a server where they can be queried, cleaned, and analyzed.