Detection of the diseases on tomatoes in advance and making early intervention and treating increases the production amount, efficiency and quality which will satisfy the consumer with a more affordable shelf price. In this way, the efforts of the farmers who are waiting for the harvest throughout the season will not be wasted. In this paper a compact convolutional neural network (CNN) is proposed for diseases identification task where the network is comprised of only 6 layers that is why it is computationally cheap in terms of parameters employed in the network. This network is trained by using PlantVillage’s tomato crops dataset which consists of 10 classes (9 diseases and 1 healthy). The proposed network is first compared with well-known pre-trained ImageNet deep networks using transfer learning approach. The results show that the proposed network performed better than pre-trained knowledge transferred deep network models and it is shown that there is no need to constitute very large, complicated network architectures to achieve a superior tomato diseases identification performance. Furthermore, to increase the performance of proposed network, data augmentation techniques are also employed during the network training. The proposed network achieves an accuracy, F₁ score, Matthews correlation coefficient, true positive rate and true negative rate of 99.70%, 98.49%, 98.31%, 98.49% and 99.81%, respectively using 9,077 unseen test images. Our results are better than or similar to the results of the state-of-the-art deep neural network approaches that used PlantVillage database and the proposed method employs the cheapest architecture.

Multimodal biometric systems are preferred as a defense compared to unimodal systems. This study introduces an open access multimodal vein database named FYO with each letter dedicated to each author’s name. The database involves three biometric traits; palm vein, dorsal vein and wrist vein of the same individuals, to explore and enhance research in the area of using these traits to create a spoof-proof multimodal authentication system. The vein images of FYO are acquired using medical vein finder in a controlled environment. Comparisons are performed to show the differences with the existing well known databases and the state-of-the-art recognition algorithms. Hand-crafted feature extractors such as Binarized Statistical Image Features (BSIF), Gabor filter and Histogram of Oriented Gradients (HOG) are applied to show the viability of the vein datasets. Additionally, a deep learning based Convolutional Neural Networks (CNN) architecture is proposed with two models using decision-level fusion of palmar, dorsal and wrist biometric traits on vein images. Unimodal systems, multimodal systems and the proposed architecture are tested on several vein datasets including palmar, dorsal and wrist vein images. Experimental results based on accuracy and computation time on our FYO datasets showed competitive output with that of other databases such as Tongji Contactless Palm Vein database, VERA, PUT, Badawi and Bosphorus hand vein databases. Moreover, the proposed CNN architecture on three vein biometric traits show superior performance compared to hand-crafted methods.