Detecting and segmenting destructive anomalies in farmland from satellite images, improving time, efficiency, and crop yield.
Winning solution of Capital One Launchpad Hackathon 2025
A research-grade framework for exhaustive evaluation of meteorological variables in reference evapotranspiration (ETo) estimation using linear regression.
基于YOLOv11+Flask+Vue+SQLite3的杂草检测系统
Solution IA dédiée à la surveillance des cultures tropicales, permettant la détection automatique de la mosaïque du manioc et des dégâts causés par la chenille légionnaire d'automne sur le maïs grâce à la vision par ordinateur et à YOLOv11. — https://huggingface.co/kjd-dktech/agbledo01
🍅 AI-powered tomato classification system using ResNet-50 and color analysis to sort tomatoes into ripe, unripe, and damaged categories. Includes video frame extraction, batch processing, and pre-trained model with 95%+ accuracy.
PyTorch MLP that forecasts crop yield (kg/ha) one year ahead for 165 countries & 102 crop types using multi-source climate, soil, and land-cover data. R²=0.9452 | Pearson r=0.9681 | 52K+ training samples.
A multi-task deep learning pipeline leveraging a pretrained MobileNetV2 backbone to jointly classify disease type, progression stage, and days since infection from leaf imagery. Outputs feed an urgency scoring function for actionable treatment recommendations..
Folder with code related to object detection in the CCTV cameras placed in the agricultural field and also down streaming for agricultural use-case
Reproducible benchmark for Black Soldier Fly larvae detection and image-level counting with classical computer vision and lightweight YOLO models.
Deep learning feasibility study for automated clove quality classification using CNN architectures on a novel Zanzibar dataset. AI for East Africa Conference (AI4EAC) 2026, Kigali, Rwanda.
Streamlit ML app predicting optimal crop types from N/P/K ratios, soil pH, temperature and rainfall using RandomForest + SHAP explainability. Batch CSV inference supported.
Hybrid deep learning pipeline: MobileNetV2 embeddings + GLCM/HSV features → XGBoost classifier. 92.36% accuracy across 12 sugarcane disease classes on 12,000 images.
Research in combinatorial optimization, graph algorithms, discrete structures, network analysis, and computational methods for emerging economies.
Plant health AI platform — leaf disease classification (38 classes) from photos, soil/climate care recommendations, growth stage tracking, and LLM-powered plant Q&A.
Open-source AI agriculture platform — TensorFlow crop yield prediction, OpenCV plant disease detection, JWT-authenticated Flask REST API, SQLite crop marketplace, and a React 18 + Vite dashboard. Self-hosted, fully documented, and live in under 2 minutes.
Official benchmark dataset and code for clove quality grading using classical texture features and fine-tuned deep models. CVPR 2026, Vision for Agriculture (V4A) Workshop.
Multi-LLM, multi-tier prompt engineering benchmark for CNN-generated training pipelines on coffee leaf disease recognition (RoCoLe dataset)
End-to-end crop prediction ML deployment — joblib-serialised RandomForest/XGBoost served via FastAPI or Streamlit, with Docker containerisation and health-check endpoint.
Curated agricultural and plant AI models relevant to grass AI-genomics.
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