Leveraging deep learning for plant disease identification: a bibliometric analysis in SCOPUS from 2018 to 2024

Deep learning has emerged as a transformative technology in agricultural science, particularly for the identification of plant diseases. This approach leverages advanced algorithms, primarily Convolutional Neural Networks (CNNs), to analyze images of plants and accurately diagnose diseases that threaten crop health and yield (Mohanty et al., 2016; Guo et al., 2020; Saleem et al., 2020; Ahmed & Y adav, 2023; Jung et al., 2023; Shoaib et al., 2023; Pacal et al., 2024). Plant diseases pose a significant threat to global food security, leading to substantial yield losses and economic impacts on agriculture. T raditional methods of disease identification often rely on visual assessments by trained professionals, which can be time-consuming, subjective, and prone to errors (Jafar et al., 2024). As a result, there is a pressing need for automated systems that can provide rapid and accurate disease detection to support farmers and agricultural experts in managing crop health effectively . Deep learning models, especially CNNs, have been shown to outperform traditional methods in terms of accuracy and efficiency . These models can learn hierarchical representations from raw image data, enabling them to identify complex patterns associated with various plant diseases. Recent studies have demonstrated that CNNs can achieve accuracy rates as high as 99.35% when classifying images of diseased and healthy plants. The architecture of CNNs typically includes layers for feature extraction and classification, allowing them to process visual information effectively .