Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms

Abstract

Fruit that has reached maturity is ready to be harvested. The prediction of fruit maturity and quality is important not only for farmers or the food industry but also for small retail stores and supermarkets where fruits are sold and purchased. Fruit maturity classification is the process by which fruits are classified according to their maturity in their life cycle. Nowadays, deep learning (DL) has been applied in many applications of smart agriculture such as water and soil management, crop planting, crop disease detection, weed removal, crop distribution, strong fruit counting, crop harvesting, and production forecasting. This study aims to find the best deep learning algorithms which can be used for the prediction of fruit maturity and quality for the shelf life of fruit. In this study, two datasets of banana fruit are used, where we create the first dataset, and the second dataset is taken from Kaggle, named Fruit 360. Our dataset contains 2100 images in 3 categories: ripe, unripe, and over-ripe, each of 700 images. An image augmentation technique is used to maximize the dataset size to 18,900. Convolutional neural networks (CNN) and AlexNet techniques are used for building the model for both datasets. The original dataset achieved an accuracy of 98.25% for the CNN model and 81.75% for the AlexNet model, while the augmented dataset achieved an accuracy of 99.36% for the CNN model and 99.44% for the AlexNet model. The Fruit 360 dataset achieved an accuracy of 81.96% for CNN and 81.75% for the AlexNet model. We concluded that for all three datasets of banana images, the proposed CNN model is the best suitable DL algorithm for bananas’ fruit maturity classification and quality detection.

Study of progress on application of hyperspectral imaging combined with deep learning approaches in detecting foods content

“…Handayanto et al [14] used color and texture features of bananas to classify their maturity through a neural network model with a discrimination accuracy of 95.24%. Aherwadi et al [15] trained and validated the maturity of bananas using both CNN and AlexNet models, and further classified unripe, ripe, and overripe bananas accurately. The results showed that both models had an accuracy rate of over 98% in predicting the maturity of bananas.…”

Automated Fruit Identification using Modified AlexNet Feature Extraction based FSSATM Classifier

“…|𝑌 𝐹𝑀 (𝑡) − 𝑟𝑙. 𝑃𝑟𝑒𝑦(𝑡) (11) where t is the present repetition, 𝑌 𝑀 (𝑡) indicates jackal, 𝑌 𝐹𝑀 (𝑡) designates the site of the female, besides Prey(t) is the site prey. 𝑌 1 (𝑡) and 𝑌 2 (𝑡) are the female and male golden jackals' most recent locations.…”

“…where 𝐸 0 is a random sum in the range [-1, 1], representing the prey's initial energy; T characterizes the maximum sum of repetitions; c1 is the default continuous set to 1.5; and 𝐸 1 energy In Equations ( 10) and (11), |Y M (t) − rl • Prey(t)| designates the distance among the golden jackal and prey besides "rl" is the vector of random statistics intended by the Levy flight function. 𝑟𝑙 = 0.05.…”

“…The golden jackals encircling and consuming their victim is represented mathematically as follows. 12) 𝑎𝑛𝑑 ( 14) 𝐼𝑓 (|𝐸| ≤ 1) (𝐸𝑥𝑝𝑙𝑜𝑟𝑎𝑡𝑖𝑜𝑛 𝑝ℎ𝑎𝑠𝑒) 𝑈𝑝𝑑𝑎𝑡𝑒 𝑡ℎ𝑒 𝑝𝑟𝑒𝑦 𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝑢𝑠𝑖𝑛𝑔 𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛𝑠 (10), (11), 𝑎𝑛𝑑 ( 16) 𝑈𝑝𝑑𝑎𝑡𝑒 𝑡ℎ𝑒 𝑝𝑟𝑒𝑦 𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝑢𝑠𝑖𝑛𝑔 𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛𝑠 ( 16), (17), 𝑎𝑛𝑑 (18) 𝑒𝑛𝑑 𝑓𝑜𝑟 𝑡 = 𝑡 + 1 𝑒𝑛𝑑 𝑤ℎ𝑖𝑙𝑒 𝑟𝑒𝑡𝑢𝑟𝑛 𝑌1…”

A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features

“…The AI algorithms can be trained using a large dataset of images of different fruits with varying quality. They can learn to identify the specific features that indicate the quality of the fruit [6].…”