Predicting Maturity of Coconut Fruit from Acoustic Signal with Applications of Deep Learning

Abstract

This paper aims to develop an effective AI-driven method to predict the maturity level of coconut (Cocos nucifera) fruits using acoustic signals. The proposed sound-based autonomous approach exploits various deep learning models, including customized CNN pretrained networks, i.e., the ResNet50, InceptionV3, and MobileNetV2, models for maturity level classification of the coconuts. The proposed study also demonstrates the effectiveness of various deep learning models to automatically predict the maturity of coconuts into three classes, i.e., premature, mature, and overmature coconuts, for inspecting the coconut fruits by using a small amount of input acoustic data. We use an open-access dataset containing a total of 122 raw acoustic signals, which is the result of knocking 122 coconut samples. The results achieved by the proposed method for coconut maturity prediction are found to be promising, which enables producers to accurately determine the yield and product quality.

Keywords

• coconut fruit
• maturity levels
• fruit quality
• deep learning
• artificial intelligence
• acoustic signals