The integration of AI and IoT in agriculture has sparked a transformative revolution in conventional farming practices. This abstract underscores the profound impact of AI and IoT technologies in the agricultural domain, emphasizing their pivotal role in enhancing productivity, optimizing resources, and driving sustainability. The symbiosis between AI and IoT has unlocked a multitude of opportunities in agriculture. By deploying IoT sensors, drones, and smart devices across agricultural landscapes, real-time data on soil conditions, weather patterns, and crop health can be meticulously collected and seamlessly transmitted. AI algorithms then process this data, empowering farmers to make well-informed decisions and fine-tune agricultural operations with unparalleled precision. Precision agriculture emerges as a transformative application of AI and IoT in farming. Through AI-powered analytics, farmers can optimize resource allocation, precisely administer water, fertilizers, and pesticides, thereby minimizing wastage.
Top1. Introduction
The meaning of artificial intelligence has varied over time, and even now, there is no single definition. However, definitions can generally be grouped into four categories. Artificial intelligence (AI) refers to a type of machine that can think and behave rationally (Kok et al., 2002). The Turing Test, proposed by Alan Turing in a paper he authored in the 1950s, aims to address the question “Can a machine think?” (Kevin et al., 2011).
To pass the Turing test, a computer must be capable of four things: natural language processing, knowledge representation, automated reasoning, and machine learning (Dobrev et al., 2012). While Turing's definition of artificial intelligence is the most commonly accepted, it does have a flaw. John McCarthy initially proposed a study based on the idea that “every aspect of learning or any other feature of intelligence can, in principle, be so precisely described that a machine can be made to simulate it” (McCarthy et al., 1955) during the 1955 Dartmouth Conference, where the term “Artificial Intelligence” was coined. Recently, AI, a key discipline in computer science, has made significant progress in various industries such as manufacturing, healthcare, finance, and education, as it can effectively address challenges that humans often struggle with (Banerjee et al., 2018).
Currently, agriculture remains one of the most critical issues worldwide, as approximately 820 million people suffer from hunger. The introduction of technology is urgently needed as our current farming methods alone will not be sufficient to meet the demand for food. Agriculture automation holds great potential for the Internet of Things (IoT), which is already revolutionizing the industry. The IoT refers to a network of physical objects or “things” that are connected to each other and can gather, share, and interact with data from their environment (Matta and Pant, 2019). By implementing an effective intelligent decision-making system and leveraging the IoT, the need for human intervention in various agricultural tasks can be significantly reduced. The success or failure of this system relies on its intelligent decision-making, which acts as the system's brainMachine learning in Agriculture (Khang & Vugar et al., 2024).
When equipped with GPS units and monitoring mechanisms, various types of machinery can be utilized in precision agriculture. Examples of such machinery include tractors, plows/tillers, seeders, sprayers, pruners, harvesters, fruit and vegetable pickers, threshers, cranes, trailers/trolleys, loaders, dumpers, and haulers. These instruments can be enhanced with additional technologies such as global positioning systems (GPS), electrical conductivity sensors, greenhouse gas monitoring devices, soil temperature sensors, plant-canopy PAR sensors, and plant leaf sensors for measuring chlorophyll content, leaf moisture, and leaf indices like NDVI (normalized difference vegetation index). Moreover, smart phones equipped with sensors and cameras can detect plant temperature, plant-water levels, and discoloration. Additionally, drones with mounted cameras can be utilized for thermal imaging in agricultural fields.
For trenching, harvesting, weeding, hoeing, and other field and gardening operations, the tools include sickles, hoes, machetes, and various other gardening implements. However, tools such as pumps, motors, pressure irrigation systems (such as drip or sprinkler systems), water storage tanks, main and lateral water lines, sprinkler heads, drip systems, and equipment used in weather stations, such as rain gauges, humidity and solar radiation sensors, and wind vanes with sensors to measure wind direction, are not included in the list of precision agriculture tools. Implementing sound animal husbandry practices enables livestock farmers to maximize animal production. Animals that are in optimal health and comfort experience greater happiness and well-being. As livestock farming is an important pillar for ensuring food security; however, the use of precision agriculture technologies has potential to further enhance the economic returns from animal husbandry/livestock farming.