With an overwhelming increase in the demand of autonomous systems, especially in the applications related to intelligent robotics and visual surveillance, come stringent accuracy requirements for complex object recognition. A system that maintains its performance against a change in the object’s nature is said to be sustainable and it has become a major area of research for the computer vision research community in the past few years. In this work, we present a sustainable deep learning architecture, which utilizes multi-layer deep features fusion and selection, for accurate object classification. The proposed approach comprises three steps: (1) By utilizing two deep learning architectures, Very Deep Convolutional Networks for Large-Scale Image Recognition and Inception V3, it extracts features based on transfer learning, (2) Fusion of all the extracted feature vectors is performed by means of a parallel maximum covariance approach, and (3) The best features are selected using Multi Logistic Regression controlled Entropy-Variances method. For verification of the robust selected features, the Ensemble Learning method named Subspace Discriminant Analysis is utilized as a fitness function. The experimental process is conducted using four publicly available datasets, including Caltech-101, Birds database, Butterflies database and CIFAR-100, and a ten-fold validation process which yields the best accuracies of 95.5%, 100%, 98%, and 68.80% for the datasets respectively. Based on the detailed statistical analysis and comparison with the existing methods, the proposed selection method gives significantly more accuracy. Moreover, the computational time of the proposed selection method is better for real-time implementation.

Effective recognition of fruit leaf diseases has a substantial impact on agro-based economies. Several fruit diseases exist that badly impact the yield and quality of fruits. A naked-eye inspection of an infected region is a difficult and tedious process; therefore, it is required to have an automated system for accurate recognition of the disease. It is widely understood that low contrast images affect identification and classification accuracy. Here a parallel framework for real-time apple leaf disease identification and classification is proposed. Initially, a hybrid contrast stretching method to increase the visual impact of an image is proposed and then the MASK RCNN is configured to detect the infected regions. In parallel, the enhanced images are utilized for training a pre-trained CNN model for features extraction. The Kapur's entropy along MSVM (EaMSVM) approach-based selection method is developed to select strong features for the final classification. The Plant Village dataset is employed for the experimental process and achieve the best accuracy of 96.6% on the ensemble subspace discriminant analysis (ESDA) classifier. A comparison with the previous techniques illustrates the superiority of the proposed framework. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Pakistan’s economy is largely driven by agriculture, and wheat, mostly, stands out as its second most produced crop every year. On the other hand, the average consumption of wheat is steadily increasing as well, due to which its exports are not proportionally growing, thereby, threatening the country’s economy in the years to come. This work focuses on developing an accurate wheat production forecasting model using the Long Short Term Memory (LSTM) neural networks, which are considered to be highly accurate for time series prediction. A data pre-processing smoothing mechanism, in conjunction with the LSTM based model, is used to further improve the prediction accuracy. A comparison of the proposed mechanism with a few existing models in literature is also given. The results verify that the proposed model achieves better performance in terms of forecasting, and reveal that while the wheat production will gradually increase in the next ten years, the production to consumption ratio will continue to fall and pose threats to the overall economy. Our proposed framework, therefore, may be used as guidelines for wheat production in particular, and is amenable to other crops as well, leading to sustainable agriculture development in general.

Melanoma belongs to the category of inoperable type of skin cancers, and its occurrence rate has increased tremendously over the past three decades [1]. According to statistics provided by the World Health Organization (WHO), almost 132,000 new cases of melanoma are reported each year worldwide. It has been reported [2] that diagnosis of melanoma, in its early stages, significantly increases chances of the patient's survival. Dermatoscopy, also knows as dermoscopy is a non-invasive clinical procedure used for

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