The global demand for food is increasing due to complex factors such as population growth and changing dietary preferences. These factors influence agricultural practices and supply chains. While technological advancements like precision farming contribute to increased productivity, they also raise sustainability concerns. Agriculture, as an intensive sector, is significantly impacted by external factors, particularly weather conditions that can disrupt global supply chains, leading to substantial economic losses. Crop devastation from pests, diseases, and extreme weather events such as droughts and floods further complicates the situation. This paper introduces a modified optimizer based on the particle swarm optimization (PSO) algorithm, specially adapted for optimizing convolutional neural networks for detecting plant diseases. Simulations are conducted on real-world data in order to evaluate the viability of the proposed approach with the best-performing models yielding an accuracy of 89.05%. Key contributions include enhancing accuracy and efficiency through the modified PSO optimizer and developing an AI-based system for precise plant disease detection.