A Precision Agricultural Application: Manggis Fruit Classification Using Hybrid Deep Learning

Abstract

Fruits come in different variants and subspecies. While some subspecies of fruits can be easily differentiated, others may require an expertness to differentiate them. Although farmers rely on the traditional methods to identify and classify fruit types, the methods are prone to so many challenges. Training a machine to identify and classify fruit types in place of traditional methods can ensure precision fruit classification. By taking advantage of the state-of-the-art image recognition techniques, we approach fruits classification from another perspective by proposing a high performing hybrid deep learning which could ensure precision mangosteen fruit classification. This involves a proposed optimized Convolutional Neural Network (CNN) model compared to other optimized models such as Xception, VGG16, and ResNet50 using Adam, RMSprop, Adagrad, and Stochastic Gradient Descent (SGD) optimizers on specified dense layers and filters numbers. The proposed CNN model has three types of layers that make up its model, they are: 1) the convolutional layers, 2) the pooling layers, and 3) the fully connected (FC) layers. The first convolution layer uses convolution filters with a filter size of 3x3 used for initializing the neural network with some weights prior to updating to a better value for each iteration. The CNN architecture is formed from stacking these layers. Our self-acquired dataset which is composed of four different types of Malaysian mangosteen fruit, namely Manggis Hutan, Manggis Mesta, Manggis Putih and Manggis Ungu was employed for the training and testing of the proposed CNN model. The proposed CNN model achieved 94.99% classification accuracy higher than the optimized Xception model which achieved 90.62% accuracy in the second position.

Use of Convolutional Neural Networks (CNN) to recognize the quality of oranges in Peru by 2023

“…For example, the first article addressed the classification of mangosteen fruits using an optimized CNN model using the Adam optimizer, achieving improvements in the accuracy of the results. (7) Other studies also explored different optimizers as shown in table 2.…”

“…Sumari et al (7) Vasumathi et al (8) Nasir et al (9) Palakodati et al (10) Siddiqi (11) N e u r a l Networks Used On the other hand, when analyzing the articles, it was observed that some focused on the classification of a single specific fruit, such as dragon fruits. (4) However, there were also studies that addressed the classification of various types of fruits, as shown in table 3.…”

“…of fruits in the agricultural field. (4,7,11,13,14,15,16,17) In the commercial setting, this procedure often leads to errors, as sellers must correctly recognize each type of vegetable and fruit, which can be a significant challenge even for highly trained employees. (12) Finally, the results obtained in each study were analyzed, comparing the methods used, the proposed methods, the method with the best result and the levels of precision achieved, as shown in table 5.…”

“…al. (7) Trieu et al (4) Katarzyna et al (12) Vasumathi et al (8) Nasir et al (9) Gill et al (5) Palakodati et al (10) Gulzar (13) Gill et al (6) Siddiqi (11) Fu et al (14) Ibrahim et al (15) Azadnia et al (16) Meshram et al (17) Mohd Ali et al (18) Fruits…”

Classification of Fruits Based on Shape and Color using Combined Nearest Mean Classifiers

“…Multiple classifier combination goals to achieve the final decision by integrating the predictions of several individual classifiers to obtain comprehensive results. Experimental studies have shown that the combination of several classifiers has been very helpful in improving the classification accuracy [2], [3] There are several ensemble and deep learning methods for fruit classification have been proposed [4]- [7]. However, deep learning in machine learning uses large amounts of data.…”