Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Abstract

Recent advances in computer vision have allowed broad applications in every area of life, and agriculture is not left out. For the agri-food industry, the use of advanced technology is essential. Owing to deep learning’s capability to learn robust features from images, it has witnessed enormous application in several fields. Fruit detection and classification remains challenging due to the form, color, and texture of different fruit species. While studying the impact of computer vision on fruit detection and classification, we pointed out that till 2018 many conventional machine learning methods were utilized while a few methods exploited the application of deep learning methods for fruit detection and classification. This has prompted us to pursue an extensive study on surveying and implementing deep learning models for fruit detection and classification. In this article, we intensively discussed the datasets used by many scholars, the practical descriptors, the model’s implementation, and the challenges of using deep learning to detect and categorize fruits. Lastly, we summarized the results of different deep learning methods applied in previous studies for the purpose of fruit detection and classification. This review covers the study of recently published articles that utilized deep learning models for fruit identification and classification. Additionally, we also implemented from scratch a deep learning model for fruit classification using the popular dataset “Fruit 360” to make it easier for beginner researchers in the field of agriculture to understand the role of deep learning in the agriculture domain.

Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal

“…In addition, we also classify the inter-subject classification using the DT classifier based on the leave one out model. We obtained some performance measures such as recall, specificity, precision, accuracy, F1, and Area Under the Curve (AUC) [ 42 , 43 , 78 , 79 ]. The standard performance measures are described in Equations (1)–(5).…”

Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network

“…To examine the results of the mixture on cells, bulk of cell images is generated by advanced microscopes during experiments which persuade analysts toward image analysis. Image segmentation is one of the critical aspects of image analysis [ 7 – 9 ]. Cell culture and drug process and convolutional neural network (CNN) [ 10 , 11 ] have been applied to cell biology.…”

Role of Inflammation, Oxidative Stress, and Mitochondrial Changes in Premenstrual Psychosomatic Behavioral Symptoms with Anti-Inflammatory, Antioxidant Herbs, and Nutritional Supplements

“…In addition, computational intelligence has an important role in the medical areas such as detection, treatment, localization, and recommendation. Currently, some intelligence techniques such as machine learning, deep learning [ 23 , 24 ], and the Internet of Things (IoT) [ 25 ] with processing techniques such as signal processing, image processing, text processing, video processing, and audio processing are used in the medical fields. Previously, the Siddiqui group used physiological signals to detect sleep disorders [ 26 – 29 ].…”

“…In addition, computational intelligence has an important role in the medical areas such as detection, treatment, localization, and recommendation. Currently, some intelligence techniques such as machine learning, deep learning [23,24], and the Internet of Things (IoT) [25] with 2…”