Evaluation
The project is evaluated against its objectives, technical effectiveness, usability and contribution to sustainable gardening.
Technical Performance
Evaluation considers irrigation prediction error, plant-image classification measures, system response time and correct transfer of data between modules.
Practical Value
The system should provide understandable outputs, reduce avoidable watering and support earlier recognition of plant-health problems.
Usability
Clear navigation, consistent page structure, readable text and responsive layouts help users interpret results without specialist knowledge.
Limitations
Performance depends on sensor quality, dataset diversity, image clarity and the amount of real-world field testing completed.
Ethics and Sustainability
Privacy
The core system does not require sensitive personal data. Environmental readings and plant images should be stored securely and used only for the project purpose.
Transparency
Users should understand that AI recommendations can be incorrect. Confidence information and clear explanations should accompany important outputs.
Human Control
The platform supports decisions rather than replacing the gardener. Final watering and treatment choices remain with the user.
Sustainability
More accurate irrigation can reduce water waste. Earlier disease recognition may support targeted organic treatment and reduce unnecessary intervention.