Plant disease poses a significant threat to global food security each year, highlighting the urgent need for accurate detection methods. Fortunately, new advancements in AI technology offer promising solutions. Deep learning, powered by neural networks, has emerged as a top choice for classifying various problems, particularly those involving digital images. With an active and supportive community, comprehensive documentation, and user-friendly tutorials, deep learning techniques have become accessible even to those with basic programming knowledge. As a result, an explosion of articles showcasing deep learning's application to a wide range of problems has occurred.
Despite the impressive results achieved by deep learning, its application requires precautions to avoid unrealistic or biased outcomes. Often, basic references fail to address critical subtleties, leading to oversight in experimental design. Moreover, deep learning models' "black box" nature makes it challenging to identify issues with model fitting. To mitigate these challenges, one effective approach involves training models with comprehensive datasets that capture the full variability of the problem at hand. However, creating such datasets presents a significant hurdle for plant pathology researchers, as the problem's variability demands a large number of diverse samples.
This work aims to provide a detailed workflow for training deep learning models in the context of plant disease classification. Special emphasis is placed on highlighting the numerous pitfalls that can render generated models impractical and ineffective. Additionally, practical strategies to overcome challenges in developing plant pathology technologies are explored. By following this comprehensive guide, researchers can optimize their use of AI technologies, ensuring reliable and robust plant disease detection systems for real-world applications.
(No code is publicly available for this chapter yet)