The output and quality of apples were greatly threatened by plant diseases. Identifying the types and grades of diseases in time was helpful to the management of diseases. When the disease occurs on a leaf, there was little change in the leaf except for the affected area. The traditional attention mechanism changed the weight of the network to all pixels in the image, which affected the ability of the network to extract the features of the lesion area. And the traditional method of data enhancement was easy to cause local similarity and local discontinuity of all sample features in the same disease grade. In this paper, the improved metric matrix for kernel regression (IMMKR) was used to reduce the influence of local similarity and local discontinuity of all sample features in the same class. Then, a new attention mechanism by fusing the lesion location based on visual features was proposed, and the attention of the model to the lesion area was strengthened. The experiments were carried out on three different diseases of apple named black rot, scab, and rust. The accuracy rate and recall rate of the new method on the combined dataset of PlantVillage and PlantDoc were 91.55% and 92.06%, respectively, which was superior to existing methods. This algorithm has important reference significance for the identification and promotion of crop diseases.

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Extensive research suggested that the core of how to use pesticides scientifically is the careful and accurate determination of the severity of crop diseases. The existing grading standards of plant leaf diseases have been excessively singular. Thus, the diseases roughly fall into general and severe grades. To address the above problems, this study considered the effect of the distribution of disease spots, and two evaluation indicators (termed the imbalance degree and main vein distance) were newly added to optimize the grading criteria of apple leaf diseases. Combined with other factors, the grade evaluation indicator was determined through PCA principal component analysis. A gradual multivariate logistic regression algorithm was proposed to evaluate apple leaf disease grade and an optimized apple leaf disease grade evaluation model was built through PCA-logistic regression analysis. In addition, three common apple leaf diseases with a total of 4500 pictures (i.e., black rot, scab, and rust) were selected from several open-source datasets as the subjects of this paper. The object detection algorithm was then used to verify the effectiveness of the new model. As indicated by the results, it can be seen from the loss curve that the loss rate reaches a stable range of around 70 at the epoch. Compared with Faster R-CNN, the average accuracy of Mask R-CNN for the type and grade recognition of apple leaf disease was optimized by 4.91%, and the average recall rate was increased by 5.19%. The average accuracy of the optimized apple leaf disease grade evaluation model was 90.12%, marking an overall increase of 20.48%. Thus, the effectiveness of the new model was confirmed.