Expiry Prediction and Reducing Food Wastage using IoT and ML

This paper details development of a low-cost, small-size, and portable electronic nose (E-nose) for the prediction of the expiry date of food products. The Sensor array is composed of commercially available metal oxide semiconductors sensors like MQ2 sensor, temperature sensor, and humidity sensor, which were interfaced with the help of ESP8266 and Arduino Uno for data acquisition, storage, and analysis of the dataset consisting of the odor from the fruit at different ripening stages. The developed system is used to analyze gas sensor values from various fruits like bananas and tomatoes. Responding signals of the e-nose were extracted and analyzed. Based on the obtained data we applied a few machine learning algorithms to predict if a banana is stale or not. Logistic regression, Decision Tree Classifier, Support Vector Classifier (SVC) & K-Nearest Neighbours (KNN) classifiers were the binary classification algorithms used to determine whether the fruit became stale or not. We achieved an accuracy of 97.05%. These results prove that e-nose has the potential of assessing fruits and vegetable freshness and predict their expiry date, thus reducing food wastage

Shannon entropy on near-infrared spectroscopy for nondestructively determining water content in oil palm

Indonesia is the world’s largest producer of palm oil. To preserve its competitive advantages, the Indonesian oil palm sector must expand high-quality palm oil output. In oil palm quality control, the water content is a crucial parameter as it can be used as a reference to determine the right harvest time. Thus, this study proposed a near-infrared (NIR) spectroscopy as a fast and non-destructive analysis to assess oil palm water content. NIR spectra were processed using Shannon entropy to describe the characteristics at each wavelength. In this study, oil palm fruit samples at various maturity levels were collected with eight different maturity fractions. Based on the analysis, the Shannon entropy value is closely related to any changes in the water content of palm oil. The entropy value has a decreasing trend as the water content increases. The proposed technique can predict the water content of an oil palm with satisfactory performance with values of 0.9746 of coefficient of determination (R2) and 2,487 of root mean square error (RMSE). Application of this model will lead to a fast and accurate prediction system related to oil palm water content

Application Of AHP For Determining The Best Of Palm Oil Fresh Fruit Bunch

This study covers the importance of high quality of palm oil Fresh Fruit Bunches (FFB) to ensure high production in palm oil industry. The level of palm oil FFB maturity will affect to oil extraction rate (OER) which is the main key performance indicator for palm oil industry. The most important process to classify the palm oil FFB ripeness is the grading process. Therefore, the quality grading process of FFB needs to be conducted properly to ensure that high-quality palm oil FFB is selected for production. Usually, the grading process performed by some graders in each mill manually. A sample from each lorry was taken in the grading process. However, this method takes time and may lead to errors in the classification process, especially if the graders have less experience. One of the useful tools that can be employed to make decisions in classification process is Analytical Hierarchy Process (AHP). The main concern was to ensure the reliability of AHP technique achievable. The methodology in this study consists of five phases ie; data collection from expert grader and industries visited, identifying the most important criteria, analysis by AHP method, validation by TOPSIS technique and finally the ranked of the best criteria of high quality FFB. The Expert Choice Software and Microsoft Office Excel are tools that used to analyze the data collected from expert graders in the AHP and TOPSIS technique. The main objective of this study is to determine the best quality of FFB using AHP. The result found that the number of detached fruitlets is the most important criteria to determine the FFB ripeness with 0.560 priority vector followed by color with 0.219 priority vector compared to other criteria. The sensitivity analysis performed to ensure the results are consistent and reliable. It will help the graders to conduct a proper grading process at mills to increase the quality of OER