Contemporary vision and pattern recognition issues such as image, face, fingerprint identification, and recognition, DNA sequencing, often have a large number of properties and classes. To handle such types of complex problems, one type of feature descriptor is not enough. To overcome these issues, this paper proposed a multi-model recognition and classification strategy using multi-feature fusion approaches. One of the growing topics in computer and machine vision is fruit and vegetable identification and categorization. A fruit identification system may be employed to assist customers and purchasers in identifying the species and quality of fruit. Using Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) deep learning applications, a multi-model fruit image identification system was created. For performance assessment in terms of accuracy analysis, the proposed framework is compared to ANFIS, RNN, CNN, and RNN-CNN. The motivation for adopting deep learning is that these models categorize pictures without the need for any intervention or process. The suggested fruit recognition method offers efficient and promising results, according to the findings of the experiments in terms of accuracy and F-measure performance analysis.

Fruit classification is found to be one of the rising fields in computer and machine vision. Many deep learning-based procedures worked out so far to classify images may have some ill-posed issues. The performance of the classification scheme depends on the range of captured images, the volume of features, types of characters, choice of features from extracted features, and type of classifiers used. This paper aims to propose a novel deep learning approach consisting of Convolution Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) application to classify the fruit images. Classification accuracy depends on the extracted and selected optimal features. Deep learning applications CNN, RNN, and LSTM were collectively involved to classify the fruits. CNN is used to extract the image features. RNN is used to select the extracted optimal features and LSTM is used to classify the fruits based on extracted and selected images features by CNN and RNN. Empirical study shows the supremacy of proposed over existing Support Vector Machine (SVM), Feed-forward Neural Network (FFNN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) competitive techniques for fruit images classification. The accuracy rate of the proposed approach is quite better than the SVM, FFNN, and ANFIS schemes. It has been concluded that the proposed technique outperforms existing schemes.

Fruit image classification is an ill‐posed problem. Many machine learning techniques have been developed until now to improve the classification problem of fruit images. However, the performance of these techniques depends upon the quality of acquired fruit images. Thus, the performance of competitive fruit classification techniques reduces for images captured under poor environmental conditions, such as haze, fog, smog etc. To overcome this issue, type‐II fuzzy‐based fruit image improvement approach is employed to improve the visibility of weather degraded fruit images. After that, fruit images will be classified using an integrated classification model. The integrated model combines two well‐known models (i.e. CNN and RNN). CNN is utilised to evaluate the discriminative features of fruit images. RNN is utilised to asses sequential labels. Extensive analysis shows that the proposed integrated classification model outperforms competitive fruit image classification techniques in terms of accuracy and coefficient of correlation.