Accurate and timely classification of diseases during strawberry planting can help growers deal with them in timely manner, thereby reducing losses. However, the classification of strawberry diseases in real planting ...Accurate and timely classification of diseases during strawberry planting can help growers deal with them in timely manner, thereby reducing losses. However, the classification of strawberry diseases in real planting environments is facing severe challenges, including complex planting environments, multiple disease categories with small differences, and so on. Although recent mobile vision technology based deep learning has achieved some success in overcoming the above problems, a key problem is how to construct a non-destructive, fast and convenient method to improve the efficiency of strawberry disease identification for the multi-region, multi-space and multi-time classification requirements. We develop and evaluate a rapid, low-cost system for classifying diseases in strawberry cultivation. This involves designing an easy-to-use cloudbased strawberry disease identification system, combined with our novel self-supervised multi-network fusion classification model, which consists of a Location network, a Feedback network and a Classification network to identify the categories of common strawberry diseases. With the help of a novel self-supervision mechanism, the model can effectively identify diseased regions of strawberry disease images without the need for annotations such as bounding boxes. Using accuracy, precision, recall and F1 to evaluate the classification effect, the results of the test set are 92.48, 90.68, 86.32 and 88.45%, respectively. Compared with popular Convolutional Neural Networks(CNN) and five other methods, our network achieves better disease classification effect. Currently, the client(mini program) has been released on the We Chat platform. The mini program has perfect classification effect in the actual test, which verifies the feasibility and effectiveness of the system, and can provide a reference for the intelligent research and application of strawberry disease identification.展开更多
基金supported by the Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2016-AII)。
文摘Accurate and timely classification of diseases during strawberry planting can help growers deal with them in timely manner, thereby reducing losses. However, the classification of strawberry diseases in real planting environments is facing severe challenges, including complex planting environments, multiple disease categories with small differences, and so on. Although recent mobile vision technology based deep learning has achieved some success in overcoming the above problems, a key problem is how to construct a non-destructive, fast and convenient method to improve the efficiency of strawberry disease identification for the multi-region, multi-space and multi-time classification requirements. We develop and evaluate a rapid, low-cost system for classifying diseases in strawberry cultivation. This involves designing an easy-to-use cloudbased strawberry disease identification system, combined with our novel self-supervised multi-network fusion classification model, which consists of a Location network, a Feedback network and a Classification network to identify the categories of common strawberry diseases. With the help of a novel self-supervision mechanism, the model can effectively identify diseased regions of strawberry disease images without the need for annotations such as bounding boxes. Using accuracy, precision, recall and F1 to evaluate the classification effect, the results of the test set are 92.48, 90.68, 86.32 and 88.45%, respectively. Compared with popular Convolutional Neural Networks(CNN) and five other methods, our network achieves better disease classification effect. Currently, the client(mini program) has been released on the We Chat platform. The mini program has perfect classification effect in the actual test, which verifies the feasibility and effectiveness of the system, and can provide a reference for the intelligent research and application of strawberry disease identification.
