Northern maize leaf blight is one of the major diseases that endanger the health of maize. The complex background of the field and different light intensity make the detection of diseases more difficult. A multi-scale feature fusion instance detection method, based on convolutional neural network, is proposed to detect maize leaf blight. The proposed technique incorporates three major steps of data set preprocessing part, fine-tuning network and detection module. In the first step, the improved retinex is used to process data sets, which successfully solves the problem of poor detection effects caused by high-intensity light. In the second step, the improved RPN is utilized to adjust the anchor box of diseased leaves. The improved RPN network identifies and deletes negative anchors, which reduces the search space of the classifier and provides better initial information for the detection network. In this paper, a transmission module is designed to connect the fine-tuning network with the detection module. On the one hand, the transmission module fuses the features of the low-level and high-level to improve the detection accuracy of small target diseases. On the other hand, the transmission module converts the feature map associated with the fine-tuning network to the detection module, thus realizing the feature sharing between the detection module and the fine-tuning network. In the third step, the detection module takes the optimized anchor as input, focuses on detecting the diseased leaves. By sharing the features of the transmission module, the time-consuming process of using candidate regions layer by layer to detect is eliminated. Therefore, the efficiency of the whole model has reached the efficiency of the one-stage model. In order to further optimize the detection effect of the model, we replace the loss function with generalized intersection over union (GIoU). After 60000 iterations, the highest mean average precision (mAP) reaches 91.83%. The experimental results indicate that the improved model outperforms several existing methods in terms of greater precision and frames per second (FPS).

With the continuous development of economy and the change in consumption concept, the demand for meat, a nutritious food, has been dramatically increasing. Meat quality is tightly related to human life and health, and it is commonly measured by sensory attribute, chemical composition, physical and chemical property, nutritional value, and safety quality. This paper surveys four types of emerging non-destructive detection techniques for meat quality estimation, including spectroscopic technique, imaging technique, machine vision, and electronic nose. The theoretical basis and applications of each technique are summarized, and their characteristics and specific application scope are compared horizontally, and the possible development direction is discussed. This review clearly shows that non-destructive detection has the advantages of fast, accurate, and non-invasive, and it is the current research hotspot on meat quality evaluation. In the future, how to integrate a variety of non-destructive detection techniques to achieve comprehensive analysis and assessment of meat quality and safety will be a mainstream trend.