Accurate recognition of greenhouse tomato leaf diseases is crucial for crop monitoring, timely intervention, and yield protection. In greenhouse environments, disease symptoms are often affected by complex illumination, background clutter, overlapping leaves, mixed patterns, and subtle inter-class differences, making reliable image-based diagnosis challenging. To evaluate compact convolutional neural networks for this task, this study presents a controlled comparison of five CNN models—MobileNetV3-Large, ShuffleNetV2\_x1\_0, MobileNetV2, EfficientNet-B0, and ResNet18—using the public Tomato Leaf Image Dataset (TLID). A curated split of 15,254 images covering seven conditions was used, wi... More >