Grape leaf disease detection based on attention mechanisms

Cotton leaf disease detection method based on improved SSD

Biometrics plays an important role in authentication applications since they are strongly linked to holders. With an increasing growth of e-commerce and e-government, one can expect that biometric-based authentication systems are possibly deployed over the open networks in the near future. However, due to its openness, the Internet poses a great challenge to the security and privacy of biometric authentication. Biometric data cannot be revoked, so it is of paramount importance that biometric data should be handled in a secure way. In this paper we present a scheme achieving privacy-preserving fingerprint authentication between two parties, in which fingerprint minutiae matching algorithm is completed in the encrypted domain. To improve the efficiency, we exploit homomorphic encryption as well as garbled circuits to design the protocol. Our goal is to provide protection for the security of template in storage and data privacy of two parties in transaction. The experimental results show that the proposed authentication protocol runs efficiently. Therefore, the protocol can run over open networks and help to alleviate the concerns on security and privacy of biometric applications over the open networks.

The knowledge within wheat production chain data has multiple levels and complex semantic relationships, making it difficult to extract knowledge from them. Therefore, this paper proposes a fine-grained knowledge extraction method for the wheat production chain based on ontology. For the first time, the conceptual layers of ploughing, planting, managing, and harvesting were defined around the main agricultural activities of the wheat production chain. Based on this, the entities, relationships, and attributes in the conceptual layers were defined at a fine-grained level, and a spatial–temporal association pattern layer with four conceptual layers, twenty-eight entities, and forty-two relationships was constructed. Then, based on the characteristics of the self-constructed dataset, the Word2vec-BiLSTM-CRF model was designed for extracting the knowledge within it, i.e., the entity–relationship–attribute model and the Word2vec-BiLSTM-CRF model in this paper were compared with the four SOTA models. The results show that the accuracy and F1 value improved by 8.44% and 8.89%, respectively, compared with the BiLSTM-CRF model. Furthermore, the entities of the pest and disease dataset were divided into two different granularities for the comparison experiment; the results show that for entities with “disease names” and “pest names”, the recognition accuracy at the fine-grained level is improved by 32.71% and 31.58%, respectively, compared to the coarse-grained level, and the recognition performance of various fine-grained entities has been improved.

Accurate plant classification based on deep learning is important for precision agriculture, such as weed control, crop monitoring, and smart farming systems. The accuracies of deep learning models rely on datasets. Although many datasets have been proposed in recent decades, they have the common limitations in terms of scale, less environmental diversity, and challenges of data integration. To solve these problems, in this paper, we introduce a new dataset named OpenPlant, which is a large-scale and open dataset containing 635,176 RGB images across 1167 plant species. OpenPlant includes diverse growth stages of plants, plant structures, and environmental conditions, and its annotations were carefully verified to ensure quality. The proposed OpenPlant can be a benchmark for agricultural plant classification. In this paper, we benchmarked 10 widely used convolutional neural networks (CNNs), 6 vision transformers (ViTs), and 12 vision–language models (VLMs) to provide a comprehensive evaluation. The OpenPlant dataset offers a comprehensive benchmark for agricultural research using deep learning and the results provide insights into future directions.