Bangladesh is an agricultural country having a tropical monsoon climate. A large variety of tropical and sub-tropical fruits abound in Bangladesh. People of Bangladesh are fruit-lovers too. Currently, most of the people of this country are failing to recognize many of the rare local fruits and the number of this portion of people is increasing day by day. Thus, not only the natural heritage but also good sources of food are being diminished. Performing a machine vision based recognition of these fruits can help people recognize them. In this paper, we perform an in-depth exploration of a computer vision approach for recognizing rare local fruits of Bangladesh. A number of rare local fruits are classified based on the features extracted from their images. For our experiment, we have used a total of 480 images of 6 rare local fruits. We perform some preprocessing on the captured image and then expected features are extracted using image segmentation. Classification of the fruits is accomplished using support vector machines (SVMs). We have achieved 94.79% classification accuracy, which is not only good but also promising for future research.

The global world is dependent and integrated with all the ecosystems. To survive in the race, we must need to know about the birds and their habitats and importance to the existence of the human race on earth. However, it is difficult to recognize several species of birds, animals, and so on. In this paper, we presented a methodology to recognize local birds of Bangladesh using transfer learning techniques. The whole research work has been done using transfer learning in six different CNN architecture namely DenseNet201, InceptionResNetV2, MobileNetV2, ResNet50, ResNet152V2, and Xception. As to defeat the lack of much image data, augmentation is performed on the collected image data too. All the models are trained by 2800 data images and tested by 700 data images. Among all the discussed models, MobileNetV2 model exhibits the best performance in terms of various indicators such as F1-score, precision, recall, and accuracy. The accuracy, precision, recall, and F1 Score of MobileNetV2 are 96.71%, 96.93%, 96.71%, 96.75%. Then a comparative analysis has been performed for this work among the approaches as well. The obtained result shows that the working method is optimal and efficient for recognizing local birds of Bangladesh.