CocoVision AI
Production-ready Open Source Deep Learning Model for classifying coconut tree diseases with 96.5% accuracy.
Architecture
EfficientNetB0 • Transfer Learning
Core Technology
Built on a robust, industry-standard machine learning stack designed for performance and extensibility.
Python
Core language powering the inference API and training scripts.
TensorFlow
Deep learning framework used for model building and execution.
Keras
High-level neural networks API running on top of TensorFlow.
NumPy
Fundamental package for scientific computing and matrix operations.
EfficientNetB0
State-of-the-art CNN architecture optimized for accuracy and efficiency.
Transfer Learning
Leveraging ImageNet weights for rapid convergence and high accuracy.
Model Architecture
A lightweight, efficient pipeline designed for high accuracy and rapid inference on edge devices or cloud servers.
Input Image
Raw coconut leaf image
Image Preprocessing
Resize to 224x224, normalization
EfficientNetB0
Pre-trained ImageNet Base
Feature Extraction
Global Average Pooling
Classification Layer
Dense layer with Softmax
Disease Prediction
6 Classes + Confidence Score
Key Features
Engineered to be practical, reliable, and immediately useful for developers and researchers.
High Accuracy
Consistently achieves ~96.5% validation accuracy across complex datasets with overlapping disease symptoms.
Python API
Clean, well-documented Python Developer API allows instant integration into existing pipelines.
CLI Interface
Zero-code Command Line Interface for quick inference and batch processing.
Standalone
Lightweight architecture requiring minimal dependencies beyond TensorFlow.
Cross Platform
Runs seamlessly on Windows, macOS, and Linux environments.
MIT License
Fully open source. Free for commercial, academic, and personal use.
Production Ready
Tested thoroughly with error handling, logging, and robust input validation.
Developer Friendly
Clear repository structure, unit tests, and comprehensive Markdown documentation.
Performance Metrics
Validated on a rigorous test dataset ensuring balanced performance across all disease classes.
Developer Experience
Designed to be immediately usable. Whether you prefer the terminal or Python scripts, integration takes minutes.
Zero-Code CLI
Run inference directly from your terminal without writing a single line of Python.
Clean Python API
Import the model as a module and integrate it into your Flask, FastAPI, or Django backend instantly.
from cocovision import Model
# Initialize the model
model = Model(weights='best_model.h5')
# Predict
result = model.predict('image.jpg')
print(result.class_name, result.confidence)Why Open Source?
CocoVision AI was extracted from the proprietary CocoGuard engine. Recognizing the significant gap in accessible agricultural AI tools, I decided to open-source the core vision pipeline.
This project empowers researchers, students, and agricultural technologists to build upon a high-accuracy baseline without starting from scratch.
Development Roadmap
Continuous improvements planned for better edge performance and explainability.
EfficientNetB0 Baseline
Achieve 96%+ accuracy on 6 disease classes.
Python & CLI API
Release developer-friendly integration tools.
TensorFlow Lite Export
Optimize model for mobile and edge deployment.
Grad-CAM Visualization
Add visual explainability to model predictions.