Tomato Leaf Disease Classification Dataset in Pakistan

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Authors: Mohammad Naeem Malik, Syed Hashir Ali, Hamid Ali
Cited by: 0

Abstract

This dataset comprises 7,200 images of tomato leaves collected from real-time agricultural field conditions in Pakistan. The images were captured using a smartphone camera under natural lighting, without controlled backgrounds, to reflect realistic field environments. The dataset includes six classes: Early Blight, Late Blight, Septoria Leaf Spot, Leaf Mold, Yellow Leaf Curl Virus, and Healthy leaves. All images are stored in JPG format with consistent resolution and were manually labeled based on visible disease symptoms. The dataset is intended to support research and development in machine learning, deep learning, and computer vision, particularly for tomato leaf disease detection and precision agriculture applications under real-world conditions.

Fields of Study & Subjects

Smart Agriculture and AI Advanced Neural Network Applications Industrial Vision Systems and Defect Detection

Publication details

Journal / source
Mendeley Data
Publication date
30 January 2026
Document type
Dataset