Deep Learning Combined Water Quality is a B.Tech project topic for Food Technology. It gives students a clear starting point for research, implementation planning, and documentation.
Deep Learning Combined Water Quality Project Details
| Abstract |
This project builds a clear framework for optimizing farm resources by using deep‑learning models to check irrigation water quality and suggest the best crops. Farm output depends a lot on the quality of two main inputs: soil and water. The system looks at important soil factors such as sunlight exposure, humidity, pH, and moisture. It also measures key water‑quality indicators, including pH, electrical conductivity, and the amounts of chloride, calcium, and magnesium. With deep‑learning architectures, the model classifies how safe the water is for irrigation and predicts which crops will give the highest yields. By doing both, it reduces the chance of soil damage and crop loss that can happen
with poor irrigation practices. Implementation guidance covers data cleaning, feature extraction, and training multi‑layer neural networks. This technical help lets undergraduate students see how to set up predictive models for agricultural quality testing and provides a reliable way to make real‑time decisions in food production.
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| Reference Paper |
Deep Learning for Combined Water Quality Testing and Crop Recommendation |
| Domain |
Food Technology |
| Sub-Domain |
Food Quality Testing |
| PDF Download |
Download / View PDF |
| Get Help |
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