Abstract
The objective of this research is to propose a deep learning based-prediction model for pineapple sweetness. In this research, we use a Convolutional Neural Network (CNN) to predict sweetness of pineapples from images. The dataset contains 4,860 pineapple images for training. Based on the CNN designed it is found that the best image size is 300 × 300 pixels resized to 30 × 30 pixels. The classification accuracy of training and testing are 72.38% and 78.50%, respectively. In addition, the root mean square error values for training and testing are 0.1362 and 0.1156, respectively. When developed as a mobile application, the accuracy of the application is 80.15%, the root mean square error value is 0.0156 and the reliability is 95.00%.
Highlights
Pineapples originate from South America, where pineapples are well tolerated for various environments and are considered fruit of Thailand’s economy
Pineapple varieties that are grown in Thailand, which consists of many species such as Pattawia varieties, Intharachit varieties (Native varieties), Phuket varieties and Phan Nang Lare varieties, etc
We propose the predictions of ripe fresh pineapple sweetness without destroying the fruit
Summary
Pineapples originate from South America, where pineapples are well tolerated for various environments and are considered fruit of Thailand’s economy. Pineapples are one type of healthy fruits that are diverse because they are rich in minerals and vitamins. Most people choose to eat ripe fresh pineapple, preferable the sweetness. The main problem is how to select pineapple that are sweet, suitable for eating. Tapping the sound of pineapple to listen to the density of pineapple is a method that farmers generally use to measure sweetness. Measuring pineapple taste sensation requires experts that are farmers who have such knowledge to listen and analyse the tapped sound. Experienced farmers are not trained and usually not available. The traditional method is a unique ability for individuals whose values depend on listening [2].
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