Smart agriculture provides a new path for the development of modern agriculture and is a feasible solution to the food problem. In the process of intelligence, data has become a critical factor in the smooth operation of smart agricultural systems. This paper proposes a supervised agricultural data circulation model and corresponding threat model. Then, the security interaction protocol of the primary data in the data circulation model was designed by using the function encryption to ensure data security and privacy during the operation of the data circulation model. The data circulation model in this paper includes three portions: agricultural research and supervision, agricultural production, and third-party service acquisition. In the first stage, the regulator is responsible for receiving the amount of resources the farm consumes. In the second stage, the service provider calculates the service fee the innovative farm should pay, and the agricultural regulatory authority distributes the key to ensure market stability. At the same time, we analyze the security of the data circulation model and prove through simulation experiments that the running time and ciphertext size of the protocol algorithm are acceptable under the security requirements.

Existing sparse representation models represent RGB channels separately without thinking about the relationship color channels, which lose some color structures inevitably. In this paper, we introduce a novel sparse representation model for color image based on geometric algebra (GA) theory and its corresponding dictionary learning algorithm, namely K-GASVD is proposed. The model represents the color image as a multivector with the spatial and spectral information in GA space, providing a kind of vectorial representation for the inherent color structures rather than a scalar representation via current sparse image models. The proposed sparse model is validated in the applications of color image denoising and reconstruction. The experimental results demonstrate that our sparse image model avoids the hue bias phenomenon successfully and retained the color structures completely. It shows its potential as a general and powerful tool in various applications of color image analysis.

The singular value decomposition (SVD) has been considered as one of the most powerful tools in numerical algebra and has witnessed great success in a wide range of image processing tasks, such as principal component analysis, linear discriminant analysis and sparse representation. However, the existing SVD algorithms cannot directly applied in octonion signals. In this paper, we propose a novel singular value decomposition algorithm for octonion signal, namely OSVD. Firstly, a new real representation according to the components of the original octonion signal is formed and the real SVD for the real matrix is performed. Then with several largest singular values and the corresponding vectors in both left and right unitary matrices selected, the octonion signal can be reconstructed successfully. It is demonstrated by the denoising experiments multispectral image with seven spectral channels that our proposed algorithm significantly outperforms existing state-of-the-art algorithms in both quantitative and visual performance.