CROPINTEL 2.0: AI BASED SMART GRAPE DISEASE DETECTION AND SOIL HEALTH ANALYSIS SYSTEM

CropIntel 2.0 presents an AI-based smart agriculture framework for grape disease detection and soil health analysis. The proposed system combines CNN-based image classification with soil parameter analysis to improve farming productivity and reduce crop losses. The integration of FastAPI and MySQL technologies enables low-latency prediction, scalable deployment, and secure data management. Experimental analysis demonstrates improved prediction accuracy and faster response time compared to traditional agricultural systems. The proposed framework supports precision agriculture and sustainable farming practices by enabling data-driven agricultural decision-making.

  S. Sladojevic et al., “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification,” Computational Intelligence and Neuroscience, 2016.   K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Computers and Electronics in Agriculture, 2018.   A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, 2018.   M. Too et al., “A comparative study of fine-tuning deep learning models for plant disease identification,” Computers and Electronics in Agriculture, 2019.   A. Krizhevsky et al., “ImageNet Classification with Deep Convolutional Neural Networks,” NIPS, 2012.   Sandler et al., “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” CVPR, 2018.   Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” CVPR, 2017.   FastAPI Documentation.   TensorFlow Documentation.   MySQL Documentation.