The advancement of digital technologies has significantly impacted plant pest and disease management, yet gaps remain, especially in developing regions. This paper introduces the TOM2024 dataset, a comprehensive collection of high-resolution images designed to enhance pest and disease identification of maize, tomato, and onion crops. The dataset encompasses 25,844 raw images and over 12,000 labeled images, categorized into 30 classes (healthy crop, infested crop, and pest) across the three cropping systems. Acquired through meticulous fieldwork in Burkina Faso using high-resolution cameras, the dataset includes diverse environmental conditions and crop stages, ensuring a robust resource for AI model training and validation. The dataset is segmented into three categories: processed images (Category A), selected images with augmentation (Category B), and an online repository with over 25,000 raw images (Category C). Category A and B features images of crops affected by 21 distinct pests and diseases. This dataset addresses critical gaps in existing collections by offering extensive coverage and high-resolution imagery that can be used to developed AI models for automatic identification and classification of pests and diseases that affects crops. TOM2024’s versatility extends to research, educational purposes, and the practical application of digital tools in agriculture thereby contributes to the advancement of precision agriculture, sustainable agricultural practices, and food security globally.