Leaf Disease Detection | Part 3 – Training & Evaluation | Deep learning full project

Leaf Disease Detection Project – Part 3 (final) In this final part of our Leaf Disease Detection series, we train and evaluate our EfficientNetB4 model! Learn how to: ✅ Train the model on leaf images ✅ Visualize accuracy & loss curves ✅ Evaluate performance with confusion matrix and classification report ✅ Make predictions on new leaf images for real-time disease detection This video completes the workflow: Data → Model → Training → Prediction. Perfect for students, researchers, and AI enthusiasts looking to apply deep learning in agriculture. Resources & Notebook: https://www.aionlinecourse.com/ai-projects/playground/leaf-disease-detection-using-deep-learning If you missed previous parts: Part 1 – Data Preparation: Mount Drive, load dataset, process & visualize images → https://youtu.be/Bu2wQJ-9CB8?si=Y6Z6T1MOVCyCpFOz Part 2 – Model Building: Build VGG16, VGG19 & EfficientNetB4 models with Dense layers, BatchNormalization & Dropout → https://youtu.be/vyC_iEEguQE?si=BPhOggaz78G3U74f Like, subscribe, and hit the bell to explore more real-world AI projects! #leafdiseasedetection #deeplearningprojects #efficientnet #tensorflow #plantdiseasedetection #machinelearning #computervision #aiproject #aionlinecourse #aiproject