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KaraAgro-AI/
CADI-AI

Name: CADI-AI
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Cashew Disease Identification with AI (CADI-AI) Model

Model Description

Object detection model trained using YOLO v5x, a SOTA object detection algorithm.
The model was pre-trained on the Cashew Disease Identification with AI (CADI-AI) train set (3788 images) at a resolution of 640x640 pixels. The CADI-AI dataset is available via Kaggle and HuggingFace.

Intended uses

You can use the raw model for object detection on cashew images.

The model was initially developed to inform users whether cashew trees suffer from:

pest infection, i.e. damage to crops by insects or pests
disease, i.e. attacks on crops by microorganisms
abiotic stress caused by non-living factors (e.g. environmental factors like weather or soil conditions or the lack of mineral nutrients to the crop).

KaraAgro AI developed the model for the initiatives Market-Oriented Value Chains for Jobs & Growth in the ECOWAS Region (MOVE) and FAIR Forward - Artificial Intelligence for All. Both initiatives are implemented by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ).

How to use

Load model and perform prediction:

pip install -U ultralytics

python

from ultralytics import YOLO

# load model
model = YOLO("CADI-AI/yolov12x.pt")

# set confidence threshold
model.conf = 0.25

# perform inference
results = model("/path/to/your/image.jpg", size=640)

# parse results
for result in results:
    boxes = result.boxes.xyxy       # x1, y1, x2, y2
    scores = result.boxes.conf      # confidence scores
    categories = result.boxes.cls   # class indices
    print(result.names)             # {0: 'abiotic', 1: 'disease', 2: 'insect'}

# save results into "results/" folder
results[0].save(save_dir="results/")

Finetune the model on your custom dataset:

yolo train data=data.yaml imgsz=640 batch=16 model=KaraAgroAI/CADI-AI epochs=10

Model performance

Class	Precision	Recall	mAP@50	mAP@50-95
all	0.663	0.632	0.648	0.291
insect	0.794	0.811	0.815	0.39
abiotic	0.682	0.514	0.542	0.237
disease	0.594	0.571	0.588	0.248

Demo

CADI-AI Spaces demonstration

Project Repo

If you want to know how the model and dataset has been used further for the GIZ-funded activity, please have a look at:

The GitHub repository for the CADI AI desktop application

Example prediction

KaraAgro-AI/CADI-AI