The advent of cutting-edge techniques such as Computer Vision (CV) and Artificial Intelligence (AI) have sparked a revolution in the agricultural industry, with applications ranging from crop and livestock monitoring to yield optimization, crop grading and sorting, pest and disease identification, and pesticide spraying among others. By leveraging these innovative techniques, sustainable farming practices are being adopted to ensure future food security. With the help of CV, AI, and related methods, such as Machine Learning (ML) together with Deep Learning (DL), key stakeholders can gain invaluable insights into the performance of agricultural and farm initiatives, enabling them to make data-driven decisions without the need for direct interaction. This chapter presents a comprehensive overview of the requirements, techniques, applications, and future directions for smart farming and agriculture. Different vital stakeholders, researchers, and students who have a keen interest in this field would find the discussions in this chapter insightful.
TopIntroduction
Agriculture, aside from technology and oil, remains a leading contributor to many nations' GDPs and is the world's most established and vital industry. It has consistently been the primary provider of food and livestock for billions of people (A. Kumar et al., 2019; Thilakarathne et al., 2022). As the global populace continues to soar, projections estimate that by 2050, it will have reached a staggering 9.8 billion individuals (United Nations, 2017). This consequently means that arable lands are expected to decrease due to increased urbanization which will raise concerns as to whether the anticipated increase in the demand for food and agricultural production will be met.
To meet such massive demand due to the increased population, a 70% increase in the current production of agricultural foods will have to be realized by the year 2050 (Kakani et al., 2020). Achieving that status will, however, prove to be a tough challenge due to reasons such as decreased cultivable lands, rising food demands, labor requirements, and limited financial capital (Thilakarathne et al., 2022). Hence, multi-stakeholder initiatives from the industry, academia, along with other research and development entities are needed to fill up this gap by developing and implementing innovative solutions. These solutions are aimed at increasing crop yields with minimal labor and limited cultivable lands, thereby providing much-needed food security for the increased population.
Currently, the realm of information and communication technology, along with its related technologies like fifth generation and beyond, CV, internet of things (IoT), Big Data, AI, Edge/Fog/cloud Computing, and image processing (Kakani et al., 2020; Shafik et al., 2021; Yang et al., 2021), is transforming the agricultural sector, fostering the development of innovative solutions to boost crop yields and satisfy the needs of a burgeoning population (Uddin and Bansal, 2021). The integration of these technologies has brought forth the emergence of “smart farming” and “precision agriculture,” novel concepts revolutionizing the way we cultivate and harvest, which involve the use of advanced technologies to optimize decision-making in farm management and enhance the efficiency and effectiveness of agricultural tasks (Pathan et al., 2020). So, by harnessing the capabilities of these technologies, farmers can implement sustainable practices that increase crop productivity and combat the effects of food insecurity caused by poor planning, uneven harvesting, inadequate irrigation, low crop yields, and unpredictable weather events such as droughts.
Recently, the field of CV has been garnering substantial interest in the realm of agriculture, owing to its capacity to lower the expenses associated with food production through adaptable and intelligent automation mechanisms, thereby helping farmers and other key stakeholders increase crop productivity. By allowing machines to perceive and understand the environment in a way similar to humans, CV, combined with image acquisition through remotely configured camera sensors, holds enormous potential for enhancing the overall performance of the farming and agricultural sector through contactless and scalable solutions (Uddin and Bansal, 2021).
As a critical technology, AI and its related technologies, including ML and DL, are gaining traction in the realm of smart farming and agriculture. Their adaptability, speed, automation, precision, and predictive capabilities enable them to emulate human thinking attributes, allowing farmers and other key stakeholders to make precise and timely decisions that lead to increased crop yields in both quality and quantity (Ayoub Shaikh et al., 2022) In contrast to traditional methods, these technologies offer several key advantages, including decreased equipment expenses, amplified computational capabilities, and the ability to collect and process large amounts of agricultural and farm-related data, thus enabling efficient monitoring and timely decision-making (Raval et al., 2022).