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.
Highlights
TImage classification is a very active research direction in many areas and plays a very important role
Knowing that convolutional neural networks (CNNs) and RNN-based models are widely used in fruit detection and classification, we suggest the use of Attention mechanism (AM) to extract the fruit features which are difficult to distinguish due to their size, shape, and color and enable them into another CNN for information fusion. e goal of AM which is to predict the weight vector for feature maps by model learning makes the integration one of the possible best solutions to tackle the issue of fruit detection and classification
In order to give basic understanding of what is meant by Deep learning (DL) model to beginner researchers who do not necessarily have skills in computer science especially in the area of agriculture, we present the practical implementation of a simple DL model (CNN) for a fruit classification task
Summary
TImage classification is a very active research direction in many areas and plays a very important role. Image recognition can better facilitate logistics and transportation and solve the errors in many fully automated transport vehicles due to large-scale track identification errors [3] Another application of DL is the classification of fruits. In the area of object detection and image recognition, CNN has become a highly important model for study. Based on DL’s high level of attention over recent years and contrary to current surveys, we present a thorough review of the use of DL in the processing of fruit images, in areas of classification and detection. We further illustrate the use of transfer learning in the fruit detection and classification field and compare the result with the CNN models trained and developed from scratch.
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