Blockchain-based proxy re-encryption access control method for biological risk privacy protection of agricultural products.

In today's globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed.

A novel method for Pu-erh tea face traceability identification based on improved MobileNetV3 and triplet loss.

Ensuring the traceability of Pu-erh tea products is crucial in the production and sale of tea, as it is a key means to ensure their quality and safety. The common approach used in traceability systems is the utilization of bound Quick Response (QR) codes or Near Field Communication (NFC) chips to track every link in the supply chain. However, counterfeiting risks still persist, as QR codes or NFC chips can be copied and inexpensive products can be fitted into the original packaging.

Trusted-auditing chain: A security blockchain prototype used in agriculture traceability.

Traceability systems have changed the way food safety is managed and data is stored. Blockchain tracking services now provide customers with an infrastructure that allows them to easily access data online. However, there are limitations to these new capabilities, such as a lack of transparency and the existence of privacy and security challenges.

Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges.

Concern about food safety has become a hot topic, and numerous researchers have come up with various effective solutions. To ensure the safety of food and avoid financial loss, it is important to improve the safety of food information in addition to the quality of food. Additionally, protecting the privacy and security of food can increase food harvests from a technological perspective, reduce industrial pollution, mitigate environmental impacts, and obtain healthier and safer food.

Detecting Pests From Light-Trapping Images Based on Improved YOLOv3 Model and Instance Augmentation.

Light traps have been widely used as effective tools to monitor multiple agricultural and forest insect pests simultaneously. However, the current detection methods of pests from light trapping images have several limitations, such as exhibiting extremely imbalanced class distribution, occlusion among multiple pest targets, and inter-species similarity. To address the problems, this study proposes an improved YOLOv3 model in combination with image enhancement to better detect crop pests in real agricultural environments.

Detection of Small-Sized Insects in Sticky Trapping Images Using Spectral Residual Model and Machine Learning.

One fundamental component of Integrated pest management (IPM) is field monitoring and growers use information gathered from scouting to make an appropriate control tactics. Whitefly () and thrips () are two most prominent pests in greenhouses of northern China. Traditionally, growers estimate the population of these pests by counting insects caught on sticky traps, which is not only a challenging task but also an extremely time-consuming one.

Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model.

In intensive aquaculture, the number of fish in a shoal can provide valuable input for the development of intelligent production management systems. However, the traditional artificial sampling method is not only time consuming and laborious, but also may put pressure on the fish. To solve the above problems, this paper proposes an automatic fish counting method based on a hybrid neural network model to realize the real-time, accurate, objective, and lossless counting of fish population in far offshore salmon mariculture.

An adaptive image enhancement method for a recirculating aquaculture system.

Due to the low and uneven illumination that is typical of a recirculating aquaculture system (RAS), visible and near infrared (NIR) images collected from RASs always have low brightness and contrast. To resolve this issue, this paper proposes an image enhancement method based on the Multi-Scale Retinex (MSR) algorithm and a greyscale nonlinear transformation. First, the images are processed using the MSR algorithm to eliminate the influence of low and uneven illumination.