# A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder Agriculture
## 3 Results and Discussion  
### Current State of IoT for Agriculture  
For this overview of IoT for agriculture, sensor and communication technology applications are classified into five categories of (a) climate, (b) livestock, (c) plant, (d) soil, and (e) water. Within each category, there are many common, measurable parameters that can influence the performance of the agricultural system (Figure 4). This categorization focuses on \"ground-based\" measurements, while other methods exist including aerial- and space-based earth observation or remote sensing, for example in [26; 28]. In some cases, remote sensing and ground-based measurements are combined to provide temporally (ground-based sensors) and spatially (remote sensing) dense crop measurements [29]. Table 1 summarizes common electronic sensors, their applications in agriculture, and articles that describe those in more detail. Wherever available, articles that report on applications in LMICs are included, although relevant precision agriculture research from across the globe is also included. Some parameters can use different technologies to estimate the same output. Methods for measuring basic agricultural parameters, including soil and atmospheric conditions, are well-established, and commercial products are available for IoT applications. New applications for optical sensors, in particular, are evolving as the cost of semiconductor technology and data storage and transmission decreases. A particular challenge with using low-cost sensors in agriculture is the need to calibrate the sensor for the specific implementation conditions. For applications where artificial intelligence (AI) is applied to classify events based on measurement patterns, complex training datasets are necessary to teach the AI algorithm. Figure 4: Measurement categories and related measurable parameters. In addition to the measurement device, careful consideration should be given to the path and process by which measurements are transformed into insights. Data transmission can represent a major challenge in smallholder agriculture applications. Edge computing is becoming more common in commercial IoT products, in which onboard device memory and processing capacity is utilized to carry out some data reduction and analysis [80]. However, for insights that require a large quantity of measurements or computational power, transmitting data to the cloud for computing will be required. Table 2 summarizes common IoT data transmission protocols and their associated advantages and disadvantages. The IoT for smallholder agriculture represents a challenge for data transmission due to remote locations, with devices distributed over large areas or multiple farms that have potentially limited access to electricity and cellular networks. Therefore, the range, data rate, and power consumption are important design considerations and are compared for the common communication protocols. In Figure 5, communication protocols are grouped based on data rate and range of transmission, which is divided into \"Long range\", including low-power wide area network (LPWAN), which includes LoRa and SigFox, \"Short range\", including Bluetooth Low Energy (BLE), Zigbee, and Z-Wave, and cellular communication including GSM 2G, 3G, 4G, and 5G. With respect to power consumption, solutions that are tailored to IoT applications offer superior performance to more general protocols (Figure 6). While the range and power consumption of protocols like LoRa and SigFox are well suited for IoT applications, their device compatibility is more limited compared to generic wireless protocols like Bluetooth and WiFi. Additionally, cellular and satellite communication offer the advantage of providing a direct link to a web server instead of passing through an intermediate gateway.