A plant disease detection model was trained using TensorFlow, addressing issues like overshooting loss and underfitting. Adjustments included lowering the learning rate, increasing neuron count, and adding convolution layers and dropout layers to improve accuracy. The final model achieved a validation accuracy of 95.52% after implementing these changes.