Abstract: The productivity of tomato crops is negatively impacted by a number of leaf diseases, including Yellow Leaf Curl Virus, Septoria Leaf Spot, Early Blight, and Late Blight. In rural areas, manual disease detection is frequently unreliable and unavailable. This study presents LeafLens, a lightweight hybrid tomato leaf disease detection framework integrating CNN-based learning with statistical texture descriptors for practical agricultural deployment with 93.5%. The model uses deep learn-ing and lightweight classifiers to classify leaf images after extracting spatial, colour, and texture features. The scalable, affordable, and user-friendly design of LeafLens helps to increase agricultural sustainability and productivity.