Crop Disease Detection AI Training
Developed and deployed proprietary CNN models for detecting crop diseases and identifying plants from agricultural images. Led the AI training process, optimizing model inference and expanding the model's ability to accurately classify disease types across varied datasets. Oversaw the preparation, labeling, and augmentation of large-scale crop images for supervised machine learning training and validation. • Labeled and annotated agricultural image datasets for use in deep learning pipelines. • Utilized TensorFlow and Keras for Object Detection model development and fine-tuning. • Implemented ONNX optimization for deployment and real-time inference on edge devices. • Ensured accurate disease classification by reviewing and augmenting labeling instructions and practices.