Abstract
Plant disease is one of the primary causes of crop yield reduction. With the development of computer vision and deep learning technology, autonomous detection of plant surface lesion images collected by optical sensors has become an important research direction for timely crop disease diagnosis. In this paper, an anthracnose lesion detection method based on deep learning is proposed. Firstly, for the problem of insufficient image data caused by the random occurrence of apple diseases, in addition to traditional image augmentation techniques, Cycle-Consistent Adversarial Network (CycleGAN) deep learning model is used in this paper to accomplish data augmentation. These methods effectively enrich the diversity of training data and provide a solid foundation for training the detection model. In this paper, on the basis of image data augmentation, densely connected neural network (DenseNet) is utilized to optimize feature layers of the YOLO-V3 model which have lower resolution. DenseNet greatly improves the utilization of features in the neural network and enhances the detection result of the YOLO-V3 model. It is verified in experiments that the improved model exceeds Faster R-CNN with VGG16 NET, the original YOLO-V3 model, and other three state-of-the-art networks in detection performance, and it can realize real-time detection. The proposed method can be well applied to the detection of anthracnose lesions on apple surfaces in orchards.
Highlights
Nowadays, in fruit agricultural production, most of the farming works rely on the manual labor of fruit planters
The wrong judgment will directly lead to the wrong implementation of farming, which could have a serious impact on crop yield
In order to meet the demand of image quantity for training deep neural networks, three traditional data augmentation methods, including color, brightness, and angle transformation, are implemented
Summary
In fruit agricultural production, most of the farming works rely on the manual labor of fruit planters. A great quantity of simple and repetitive labors consumes time and energy and increases production costs and brings more uncertainties to agricultural production. The wrong judgment will directly lead to the wrong implementation of farming, which could have a serious impact on crop yield. With the continuous progress of precision agricultural technology [1], sensors have become the prime sources of crop information. As one of the major components of sensor information, image data plays a significant role in obtaining crop growth status and judging crop health states [2]. With the development of vision sensors, automation and intelligence of agricultural production have been promoted, and various image processing approaches have been applied in agricultural production [3, 4]
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