The Evaluation of the Grade of Leaf Disease in Apple Trees Based on PCA-Logistic Regression Analysis

• Published in: Forests Vol. 14; no. 7; p. 1290
• Main Authors: Xing, Bingqian, Wang, Dian, Yin, Tianzhen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Accuracy; Algorithms; Apple; Apples; Black rot; Classification; Crop diseases; Cyanobacteria; data collection; Diseases and pests; Effectiveness; Evaluation; foliar diseases; Fruit trees; Fruits; Identification and classification; Leaves; Logistic regression; Methods; Neural networks; Object recognition; Pesticides; Physiology; Plant diseases; principal component analysis; Principal components analysis; Quality; Regression analysis; Rust fungi; Wheat; China
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f14071290

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.