Why Tomato Leaf Detection?
Tomato is a high-demand crop used fresh and in processed products. Early disease identification helps reduce crop loss and supports better farm decisions.
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A complete AI-enabled platform that helps identify key tomato leaf diseases from images and keeps a prediction history for farm records and learning.
Registered Users
4
Total Predictions
8
Local API Setup
This website uses the in-project ai_api folder and Flask endpoint at http://127.0.0.1:5000.
Tomato is a high-demand crop used fresh and in processed products. Early disease identification helps reduce crop loss and supports better farm decisions.
Provide a practical web system where users upload leaf images, receive AI diagnosis with confidence, and track predictions over time.
The model classifies 10 tomato categories based on your trained dataset.
Water-soaked lesions that can become necrotic and cause defoliation.
Often shows dark concentric “bullseye” spots, starting on older leaves.
Rapidly expanding water-soaked to brown lesions in humid conditions.
Yellow upper-leaf patches with velvety fungal growth underneath.
Small gray-white spots with dark margins and tiny black specks.
Leaf stippling/yellowing and fine webbing under mite pressure.
Necrotic lesions with visible concentric zones on infected tissue.
Mosaic mottling, distortion, and potential fruit quality reduction.
Upward curling and yellowing leaves, with plant stunting.
No visible disease pattern among learned classes.
Step 1
User or admin logs in.
Step 2
Leaf image saved in uploads/.
Step 3
PHP sends file to Flask /predict.
Step 4
Result saved in MySQL and shown in dashboard.