SURINOVA
Website:
surinova.com
Job details:
Key Responsibilities:
- Develop and implement image processing algorithms
- Train, fine-tune, and evaluate YOLO-based object detection models (e.g., YOLOv5/YOLOv8)
- Collect, clean, and annotate agricultural image datasets (leaves, fruits, crops, diseases, etc.)
- Perform data augmentation and preprocessing to improve model performance
- Optimize models for accuracy, speed, and deployment efficiency
- Collaborate with the team to integrate models into real-world agricultural workflows
- Document experiments, results, and model performance metrics
- Stay updated with the latest advancements in computer vision and precision agriculture
Required Skills & Qualifications:
- Currently pursuing or recently completed a degree in Computer Science, AI, Data Science, or related field
- Strong understanding of Python programming
- Familiarity with Computer Vision concepts and Deep Learning
- Hands-on experience with frameworks such as TensorFlow or PyTorch
- Basic experience with object detection models (YOLO preferred)
- Knowledge of OpenCV, image preprocessing, and augmentation techniques
- Understanding of model evaluation metrics (precision, recall, mAP)
Preferred Qualifications:
- Experience working with YOLO (v5/v7/v8) on custom datasets
- Familiarity with agricultural datasets or plant disease detection
- Experience with annotation tools (e.g., LabelImg, CVAT)
- Basic understanding of deploying models on edge devices or cloud
What You Will Gain:
- Hands-on experience building real-world AI solutions in agriculture
- Exposure to end-to-end ML pipeline development
- Opportunity to work on impactful projects in AgriTech and food systems
- Mentorship from experienced professionals
- Potential for full-time opportunity based on performance
Click on Apply to know more.