Differentiation between organic and non-organic green onions using image classification with hyperparameter tuning

Abstract

Differentiation between agricultural organic and non-organic crops involves professional laboratory techniques using expensive devices. This research domain requires a real-world dataset (RWD) which is limited depending on the subject or issue of the research study. Thus, this work presented real-world green onions image datasets collected from various locations in Iloilo, Philippines. The gathered datasets fit ground truth criteria with notable information (e.g., size, width, height, resolutions, the weather during the time it captures, and place) for similarity differentiation. Moreover, this study aimed to design and develop a non-intrusive image classification using Deep Learning (DL) methods such as Convolutional Neural Networks (CNN) and Transfer Learning Models provided hyperparameter tuning. Hyperparameters are sets of variables that govern the training process of DL models. These variables remained constant over the training process and directly impacted the performance until it acquired results around 99% training and 96.25% validation accuracies. With this, an application was developed and successfully assisted users in differentiating organic and non-organic green onions using image classification.