Optimal Deep Learning Model to Identify the Development of Pomegranate Fruit in Farms
Sripad Joshi, Sandeep Kumar Panda, Sathya AR
Abstract
In this study, estimating the maturing condition in gardens helps to enhance the process of post-harvesting. Collecting fruits on the basis of their developmental stage will minimize storage costs and maximize market value. Additionally, estimated ripeness of the fruits can be more useful for indicators for detecting water shortage and to determine the water used during irrigation. The purpose of the study is to develop the new direction of technology to detect the ripeness stage between two classes: ripe and unripe. We employ deep Neural Network (DNN) classifiers for the prediction of ripe and unripe class. The results of our proposed classifiers give the sensitivity 96.2%, specificity 94.2% with accuracy of results 94.5%, over a dataset of 200 images of each class. The ROC (receiver operating characteristic) area values curve close to 0.98 in all-class during training. We believe this is a notable performance that allows a suitable non-intrusive maturing prediction that will enhance cultivation techniques.