Data-Driven Smart Farming and Personalized Diet Advisory System
DOI:
https://doi.org/10.15662/IJEETR.2026.0802121Abstract
Smart agriculture systems leverage machine learning techniques to enhance crop productivity and resource efficiency. This system integrates sensor data such as soil moisture, temperature, humidity, and nutrient levels to monitor real-time field conditions. Machine learning models analyze historical and real-time data to predict crop yield, detect plant diseases, and recommend optimal irrigation and fertilization schedules. By automating decision-making, the system minimizes water usage, reduces fertilizer waste, and improves overall farm management
Weather forecasting data is also incorporated to support proactive farming decisions and reduce climate-related risks. The proposed approach enables early detection of crop stress and pest infestation, allowing timely intervention. In addition, the system includes an image-based analysis module where farmers can upload images of fruits or vegetables to identify the produce and estimate its nutritional values such as vitamins, minerals, and caloric content using computer vision and deep learning techniques.
Furthermore, the system personalizes nutritional recommendations based on the user’s health conditions, including diabetes, obesity, or nutrient deficiencies. By linking dietary advice to specific health needs and the quantity of produce consumed, it supports better health management alongside sustainable farming. Farmers and consumers receive actionable insights through a user-friendly interface, supporting both production and post-harvest decision-making
This intelligent system promotes sustainable farming practices, enhances food quality awareness, improves health outcomes, and increases overall profitability. Overall, the machine learning-based smart agriculture system provides a comprehensive and reliable solution to address modern agricultural and dietary challenges.
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