Crop Disease Detection Using AI: A Comprehensive Survey

Abstract: Crop disease detection is critical for ensuring food security and improving agricultural productivity, especially in India where a large population depends on farming. Traditional disease identification relies on manual inspection by farmers, which is often inaccurate, time-consuming, and leads to delayed treatment and excessive pesticide use. In recent years, Artificial Intelligence (AI) and deep learning, particularly Convolutional Neural Networks (CNN), have emerged as effective solutions for automated plant disease diagnosis.

This project presents an AI-based crop disease detection system that uses CNN for identifying diseases in tomato and potato leaves from uploaded images. The system provides instant classification of Healthy or Diseased leaves along with treatment recommendations and preventive measures. A web-based interface is developed using Flask for easy access by farmers with basic digital skills. The system also maintains a history of past detections to help users track disease patterns over time.

The study evaluates the system’s performance in terms of accuracy, usability, cost-effectiveness, and scalability. Results show that the system delivers fast and reliable predictions, reduces dependency on agricultural experts, and minimizes crop loss. The project highlights the potential of AI in making agriculture more efficient and sustainable, while also identifying scope for future enhancements like multi-crop support and mobile integration.

Keywords: Crop Disease Detection; Artificial Intelligence; Convolutional Neural Network; CNN; VGG-16; Flask; TensorFlow; PlantVillage Dataset; Smart Agriculture; Image Processing.