Smart spraying technologies for precision weed management: A review

In this study, the previous work, present status, benefits, and limitations of the state-of-the-art technologies used in smart spraying technologies in precision weed management are reviewed. A total of 116 articles were identified from Google Scholar and Scopus to study the research work in the field of smart sprayers and precision weed management. The articles were examined based on the relevance, research focus, novelties, measured parameters, used technologies, and field of applications. Smart sprayers based on machine vision (MV) and artificial intelligence (AI) are keys to improving crop productivity and meeting the food demands of the future by reducing the yield losses due to weeds and working towards a sustainable future in agriculture. Many papers published in recent years have focused more on the machine vision, weed detection, and classification aspects of the weeding robot. Very few studies have attempted to discuss the components of a smart weeding machine, non-chemical-based weeders, the components of spraying systems, their controls, underlying fluid mechanics, and the field trials of these weeding robots. This article reviews conventional weeding techniques, machine-vision-based weeding robots, and spraying systems proposed or constructed in the last twenty-five years. Key technologies such as non-chemical-based weeding machines, image preprocessing, feature extraction, and weed detection based on machine learning (ML) and deep learning (DL) for smart sprayers are discussed. The fundamental components of a smart spraying system are also discussed, and previous works are compared to highlight the key components, the spraying accuracy, and the major advantages and disadvantages. The fluid mechanics of the spraying system and its associated controls involved are also presented. There are still many bottlenecks in weed detection systems and smart spraying systems. The results of the systematic review provide an understanding of the progress made in the field of robotic weed detection, herbicide and non-herbicide-based weed management, the use of machine vision, and the limitations of the current spraying systems.