AI-Driven Pheno-Parenting: A Deep Learning Based Plant Phenotyping Trait Analysis Model on a Novel Soilless Farming Dataset

Agriculture 4.0 will be data-driven and utilize modern technology in order to monitor plant life cycles traits resulting better yield. Pheno-parenting, a new concept derives certain methodologies adopted phenotyping which monitors plants at various stages of their cycle supports growth by deploying tools technologies. In this work, small-scale Hydroponic system was set up for image data collection analysis, consists thirty three species (i.e., Petunia, Pansy, Calendula) with ten from each grown. A Deep Neural Network (DNN) based approach has been analyse the key tasks such as recognition, health yield stage identification. Calendula have correctly recognised above 95% detection accuracy all test cases The result thus obtained indicates, side-view images are more effective identifying tracking growth. On other hand, top-view photographs do job capturing texture colour characteristics leaves budding flowers. Growth Development Index (GDI) metric, first rises an increase nutrient input 31 ml then saturates, proposed provide understanding growth, health, productivity.