Selective protection of cereals using artificial neural networks

Global agricultural losses due to pests and pathogens are substantial, particularly for wheat, maize, and potatoes. Addressing these challenges necessitates innovative approaches in plant protection, particularly through early detection methods. This article outlines research areas concerning the application of spectral imaging technologies in selective crop protection processes. Recent technological advancements, driven by the development of high-resolution optical sensors and data analysis methods (Pena et al. 2013), have enabled early detection of weeds, plant diseases, and pests in the field. Spectral imaging technologies, particularly hyperspectral imaging, play a pivotal role in early disease detection by capturing detailed spectral data across a wide range of wavelengths. This technology enables the detection of subtle physiological changes in plants long before visible symptoms occur. Hyperspectral imaging has proven effective in identifying diseases such as Fusarium head blight in wheat, allowing for timely interventions and potentially reducing yield losses. The integration of hyperspectral imaging with remote sensing technologies, including unmanned aerial vehicles and ground-based sensors, as well as artificial intelligence represents a significant advancement in precision agriculture. This multidisciplinary approach aims to enhance crop protection while minimizing environmental impacts. The article also explores the advantages and limitations of these technologies and strategies for reducing the reliance on chemical plant protection methods in agricultural production. It is underlined, that future research should focus on optimizing these technologies, addressing cost barriers, and exploring UAV-based applications for precision spraying and monitoring.