<p>Convolutional neural networks (CNNs) are a powerful tool for image-related applications due to their ability to learn features of images hierarchically. However, even after more than a decade, CNNs still present many challenges. Among these challenges, there is the arbitrary choice of parameters that makes the design of CNNs a difficult task. This work presents a new CNN model -SevenNet- for classifying tomato leaf diseases from the PlantVillage dataset. SevenNet’s Architecture has been built from scratch using a formulation extracted through extensive experimentation. SevenNet’s main advantages are the large number of extracted feature maps, fast convergence, and an overall reduced number of learnable parameters. A detailed study explored training the network on different data partitions, ranging from standard partition to cross-validation split in addition to other non-standard partition. Validation of SevenNet has been conducted against several state-of-the-art models, with all networks being trained from scratch. Obtained results were not only found to be outstanding and comparable to leading models, but SevenNet’s architecture demonstrated distinctive advantages, matching the performance of these established models. Notably, SevenNet’s convergence has been achieved more rapidly in terms of accuracy and loss. Additionally, the highest overall accuracy has been achieved when tested with an unusual partition (10% training, 10% validation, 80% test). The proposed CNNs were also found to be superior in terms of execution speed and convergence, solidifying SevenNet’s advantages over existing approaches.</p>