International Journal of Science, Engineering and TechnologyAuthors: M. Vasumathidevi, M. Lasya, B. Lakshmi Tirupathamma, Ch. Seema, K. Harshitha
Abstract: Traditional farming practices are not long-term viable due to the growing freshwater shortage and diminishing soil fertility, which pose serious threats to global food security. Because it eliminates soil-borne diseases, uses up to 95% less water, and supports sustainable development goals, aeroponic farming is a soilless cultivation method that emerges as an effective substitute. However, crop productivity in aeroponics is highly dependent on the quality of the surrounding air, in contrast to soil-based agriculture, where yield is determined by the fertility of the soil. In order to predict crop suitability in aeroponic systems, this paper proposes a novel methodology that combines the Air Quality Index (AQI) with real-time air pollutant concentrations of PM2.5, PM10, NO2, CO, and SO2. Based on scientific literature, AQI values and pollutant thresholds were mapped to crop tolerance levels to create a custom dataset. The suggested system uses a Random Forest classifier to suggest crops that are most suited to the local air conditions, calculates AQI using the US-EPA formula, and processes pollutant inputs. The model performs reliably, achieving 80% prediction accuracy while remaining robust to noisy data inputs. The study presents a scalable, flexible framework for crop selection that promotes sustainable agriculture and emphasizes the significance of air quality as a crucial component of soilless farming. In order to further improve reliability, future scope will involve adding more crops, incorporating environmental variables like temperature and humidity, and cross-referencing predictions with yield data collected at the field level.
DOI: http://doi.org/10.5281/zenodo.20390151
