E-Nose Application for Detecting Banana Fruit Ripe Levels Using Artificial Neural Network Backpropagation Method

Abstract

Bananas are one of the largest horticultural commodities in Indonesia because in each region of Indonesia there are different types of bananas. Bananas are climacteric because they will feel the experience even though they have been harvested. Currently, the introduction of banana ripeness is still done in a conventional way by utilizing sight and smell. However, this method is not effective in determining fruit maturity because we cannot distinguish between ripe bananas and bananas that are in the early stages because they have almost the same color and aroma. So a system is designed that resembles the human sense of smell to accurately identify the level of ripeness of the fruit. The system is named Electronic Nose or abbreviated as e-Nose. The design of the e-Nose will be done using the Artificial Neural Network Backpropagation method. The results obtained from the application of E-Nose to detect the level of ripeness of bananas with the Artificial Neural Network Backpropagation method, which is a tool capable of predicting the ripeness condition of the bananas being tested so that accurate predictions are obtained and the prediction results are displayed on the website. The accuracy results obtained from the use of the Backpropagation Neural Network method for 3 categories (immature bananas, ripe bananas, and rotten bananas) are 100%, with an epoch of 2000.

Low-cost electronic-nose (LC-e-nose) systems for the evaluation of plantation and fruit crops: recent advances and future trends

“…58 The results achieved 97% and 100% accuracy using support vector machine (SVM), decision tree (DTC), and naïve Bayes classifier methods. 59 MOS arrays have been used for analyzing the ripeness of bananas by dividing the device into three parts: microcontroller, sensor array, and pump machine. The sensor array was composed of thirteen mixed brand sensors to cover most chemicals.…”

“…The analog digital converter (ADC), which is an electronic circuit to measure signal values and data acquisition time of one minute to complete the acquisition, is one of the features of this lab-made device. 59…”

“…3c) was used to detect banana ripeness levels using artificial neural networks (ANNs). 59 The equipment was composed of a separate sample chamber (A), an air pump (B), a set of sensors (C) that was specially designed in a relatively large format, a motherboard (D), and a computer for data treatment (E and F). Since e-noses rely on temperature and humidity control, the number of sensors may impact the ability to control the dissipated heat, which may lead to overheating that may affect measurements.…”

Electronic nose as a tool for early detection of diseases and quality monitoring in fresh postharvest produce: A comprehensive review

“…Hendrick et al (2022) developed an EN containing 13 gas sensors that utilized the BPNN approach to distinguish between unripe, mature, and rotten stages of banana ripeness. The purpose of backpropagation is to reduce errors in the ANN's output.…”

“…According to the GC-MS findings, C6 aldehydes were mostly present in immature white berries whereas ester and furanone concentrations increased in ripening berries. Hendrick et al (2022) developed an EN containing 13 gas sensors that utilized the BPNN approach to distinguish between unripe, mature, and rotten stages of banana ripeness. The purpose of backpropagation is to reduce errors in the ANN's output.…”