Back-End Development of an Interactive Dashboard with Real-Time API Integration for Chili Plant Monitoring in Precision Agriculture

Azwar Farrel Wirasena; Hanif Fakhrurroja; Dita Pramesti

doi:10.15408/jti.v18i2.46450

Authors

Azwar Farrel Wirasena Information System Study Program, School of Industrial Engineering, Telkom University
Hanif Fakhrurroja Information System Study Program, School of Industrial Engineering, Telkom University https://orcid.org/0000-0002-2483-251X
Dita Pramesti Information System Study Program, School of Industrial Engineering, Telkom University https://orcid.org/0000-0002-8784-5377

DOI:

https://doi.org/10.15408/jti.v18i2.46450

Keywords:

AI-powered chatbot, back-end development, disease classification, growth prediction , interactive dashboard, precision agriculture

Abstract

This research focuses on the development of an interactive web-based dashboard to support a precision agriculture system for chili plants. The primary focus of this research is on the back-end development of the system. The system integrates several internal and external APIs, including the Flask API (internal) for plant disease classification and growth prediction, and the Google Gemini API for the AI-powered chatbot that provides consultation to farmers (external). These features allow farmers to receive automatic disease diagnosis and growth predictions, improving decision-making and crop management. The dashboard also presents weather information, environmental data, and nanobubble data, along with Echarts gauge charts for seven essential metrics: Electrical Conductivity (EC), temperature, humidity, pH, nitrogen, phosphorus, and potassium. Data for the environmental and nanobubble data is retrieved from the ThingSpeak API (external), while weather information is fetched from the OpenWeatherMap API (external). The system was thoroughly tested using Postman to ensure all API endpoints function correctly. The results confirmed that all endpoints responded with status code 200 OK, indicating stable back-end performance. Performance testing showed response times stabilizing at 2000 ms after initial 4500 ms peaks, confirming efficient handling, reliable endpoints, and deployment readiness.

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