Potassium deficiency diagnosis method of apple leaves based on MLR-LDA-SVM - Xu, Kun; Sun, Lin-Lin; Wang, Jing; Liu, Shuang-Xi; Yang, Hua-Wei; Xu, Ning; Zhang, Hong-Jian; Wang, Jin-Xing (Frontiers in Plant Science, 2023)
This study addresses the challenges of subjectivity, cost, and timeliness associated with traditional methods of diagnosing potassium deficiency in apple tree leaves. The study proposes a model that utilizes image processing technology and machine learning techniques to enhance detection accuracy during each growth period. Leaf images were collected at different growth stages and processed through denoising and segmentation. Color and shape features of the leaves were extracted, and a multiple regression analysis model was used to screen for critical features. Linear discriminant analysis was then employed to optimize the data and obtain apple leaves' optimal shape and color feature factors during each growth period.Various machine-learning methods, including SVM, DT, and KNN, were used to diagnose potassium deficiency. The MLR-LDA-SVM model was the optimal model based on comprehensive evaluation indicators. Field experiments were conducted to verify the accuracy of the diagnostic model, achieving high diagnostic accuracy during different growth periods.The model can accurately diagnose whether potassium deficiency exists in apple tree leaves during each growth period. This provides theoretical guidance for intelligent and precise water and fertilizer management in orchards.