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Multispectral image classification is a field of static learning with non-stationary input data assumptions. The evolution of Industry 4.0 has resulted in the development of multispectral images in several application areas. The classification of multispectral images is a tedious process due to its complex characteristics of including spectral as w...
Citations
... Spectral imaging captures light across the electromagnetic spectrum, while multi-spectral imaging captures a small number of spectral bands. Hyperspectral imaging collects the complete spectrum at each pixel [37,67,[116][117][118]. ...
With the continuous growth of the global population and the increasing demand for crop yield, enhancing crop productivity has emerged as a crucial research objective on a global scale. Weeds, being one of the primary abiotic factors impacting crop yield, contribute to approximately 13.2% of annual food loss. In recent years, Unmanned Aerial Vehicle (UAV) technology has developed rapidly and its maturity has led to widespread utilization in improving crop productivity and reducing management costs. Concurrently, deep learning technology has become a prominent tool in image recognition. Convolutional Neural Networks (CNNs) has achieved remarkable outcomes in various domains, including agriculture, such as weed detection, pest identification, plant/fruit counting, maturity grading, etc. This study provides an overview of the development of UAV platforms, the classification of UAV platforms and their advantages and disadvantages, as well as the types and characteristics of data collected by common vision sensors used in agriculture, and discusses the application of deep learning technology in weed detection. The manuscript presents current advancements in UAV technology and CNNs in weed management tasks while emphasizing the existing limitations and future trends in its development process to assist researchers working on applying deep learning techniques to weed management.
