ABSTRACT
In the realm of modern agriculture, the advent of smart farming has revolutionized traditional practices, ushering in an era of enhanced efficiency and sustainability. As farmers harness the power of cutting-edge technologies, they face new challenges, particularly in the identification and management of diseases and weeds. This chapter delves into the critical aspects of disease and weed identification in the context of smart farming. The chapter begins by exploring the fundamental concepts of smart farming and its transformative impact on the agricultural landscape. It highlights the integration of advanced sensors, Internet of Things devices, and artificial intelligence, all of which play pivotal roles in disease and weed identification processes. The utilization of data analytics and machine learning algorithms enables real-time monitoring, early detection, and precise diagnosis, minimizing crop losses and optimizing resource utilization. Subsequently, the chapter delves into the intricate realm of plant diseases and their manifestations in smart farming. It discusses the challenges posed by rapidly evolving pathogens and the need for accurate and rapid detection to prevent widespread outbreaks. Various state-of-the-art methods such as spectral imaging, hyperspectral analysis, and drone-based surveillance are elucidated, each showcasing their unique capabilities in identifying and monitoring diseases in crops. After that, the chapter addresses the persistent issue of weeds and their detrimental impact on crop yield. Through advanced imaging techniques and computer vision algorithms, smart farming offers innovative solutions to distinguish between crops and weeds, facilitating targeted and precise weed control measures. At the culmination of the chapter, we present a comprehensive case study focusing on coffee leaf disease detection using the powerful Vision Transformer (ViT) model. The ViT model has shown exceptional promise in various computer vision tasks, and our case study demonstrates its effectiveness in accurately identifying coffee leaf diseases.
