Real-Time Recognition of Fruits, Pests, and Weeds Using Integrated Deep Learning Methods

Under the umbrella of modernised agriculture, rapid and precise identification of fruits, weeds, and pests has become a fundamental aspect towards increasing crop productivity and securing food supplies and environmental conservation. Manual inspection is tedious, time-consuming, and prone to human error. Hence it becomes impractical as the farm grows too big to manage without some intervention. Computer vision based on deep learning, in principle, would be a suitable alternative, its ability being dulled by the impediments posed by the real agricultural environment. There is significant hindrance to detection because of varying lighting, in-the-wild types of situations with dense foliage, and the numerous differences that the object undergoes in appearance. Besides, developing areas that demand very high-accuracy models, including robotic harvesting and precision spraying, also require fairly fast models that can perform in real-time. This chapter discusses state-of-the-art hybrid deep learning models that can be applied to these challenges. It will focus on how hybrid models, known widely for their powerful local feature extraction ability, can be coupled with other neural architectures to mitigate some of their weaknesses. The fine-grained feature extraction from CNNs has now been merged with the Transformer&s;s capability to cross-link information throughout an image and attention-based networks-that would be able to automatically direct attention to a scene&s;s critical pathways for close-up and wide-field understanding, respectively-thus reducing attention from background clutter. Lastly, the chapter leverages recent studies to compare these approaches in detection precision, rate, and strength of being real-time deployable in agriculture.