Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Rahma-Shaban/Zar3y-Explainable-Crop-Disease-Detection-for-Smallholder-Farmers

Domaine:

agriculture

Type de record:

modelsoftware
Créateur:
Rah
Hôte:
Zar3y ("My Crop") is an end-to-end AI solution designed to empower Egyptian smallholder farmers in the Nile Delta and Upper Egypt. By leveraging computer vision, the system provides immediate diagnosis of crop diseases—such as those affecting tomatoes, potatoes, peppers, and corn—mitigating the heavy losses caused by delayed expert intervention. # 🌱 Zar3y — Crop Disease Detection from Phone Photos > **"Hassan, a smallholder farmer in Beheira, sees yellow spots on his tomato leaves. He takes a photo with his phone."** Zar3y ("my crop") identifies crop leaf diseases from a single photo — returning a **plain-language diagnosis**, **confidence score**, and **Grad-CAM overlay**. --- ## Team (5 Members) | Role | Member | Responsibilities | |------|--------|-----------------| | Data Engineer | Nourhan | Data prep, augmentation, OOD test set | | ML Engineer (Train) | shahd | Transfer learning, training pipeline | | ML Engineer (Eval) | menna | Evaluation, metrics, benchmarks | | Backend Developer & team leader | Rahma | FastAPI, TFLite inference, quantization | | Frontend Developer | tasneem | Streamlit demo, Grad-CAM UI, demo recording | ## Dataset - **Source**: PlantVillage on Kaggle - **Subset**: 10 locked classes (Tomato, Potato, Pepper, Corn) - **Split**: Stratified 70/15/15, **seed=42** ### 10 Locked Classes | # | Class | Crop | |---|-------|------| | 0 | Tomato___healthy | Tomato | | 1 | Tomato___Early_blight | Tomato | | 2 | Tomato___Late_blight | Tomato | | 3 | Tomato___Leaf_Mold | Tomato | | 4 | Potato___healthy | Potato | | 5 | Potato___Early_blight | Potato | | 6 | Potato___Late_blight | Potato | | 7 | Pepper_bell___healthy | Pepper | | 8 | Pepper_bell___Bacterial_spot | Pepper | | 9 | Corn___Common_rust | Corn | ### Per-Class Distribution | Class | Train | Validation | Test | |---|---|---|---| | Corn_(maize)___Common_rust_ | 834 | 179 | 179 | | Pepper,_bell___Bacterial_spot | 697 | 150 | 150 | | Pepper,_bell___healthy | 1034 | 222 | 222 | | Potato___Early_blight | 700 | 150 | 150 | | Potato___Late_blight | 700 | 150 | 150 | | Potato___healthy | 106 | 23 | 23 | | Tomato___Early_blight | 700 | 150 | 150 | | Tomato___Late_blight | 1336 | 286 | 287 | | Tomato___Leaf_Mold | 666 | 143 | 143 | | Tomato___healthy | 1113 | 239 | 239 | > Note: `Potato___healthy` contains fewer than 500 images. Class …

Visit

github.com

Similaires

AI-Driven Mobile Crop Disease Detection for Smallholder Farmers: A Literature Review and Implications for ZimbabweAI-Based Cassava Leaf Disease Detection to Support Smallholder Farmers in GhanaDataset for Crop Pest and Disease Detectionmichaelnkema1/crop-disease-detectionCrop Biotechnology and Smallholder Farmers in AfricaSghaier04Mouhanned/ghana-crop-disease-detection

AI-Driven Mobile Crop Disease Detection for Smallholder Farmers: A Literature Review and Implications for Zimbabwe

Crop diseases pose a significant threat to agricultural productivity, particularly for smal

AI-Based Cassava Leaf Disease Detection to Support Smallholder Farmers in Ghana

Late detection of cassava leaf disease is a major cause of crop loss among smallholder farmers in Gh

Dataset for Crop Pest and Disease Detection

The application of Artificial Intelligence (AI) has been evident in the agricultural sector recently

michaelnkema1/crop-disease-detection

My internship project with the Council for Scientific and Industrial Research in Accra --- title: C

Crop Biotechnology and Smallholder Farmers in Africa

The tools of genetic engineering and modern biotechnology offer great potential to enhance agricultu

Sghaier04Mouhanned/ghana-crop-disease-detection

# Ghana Crop Disease Detection This project trains a YOLO model to detect crop diseases using objec