Grape Pseudocercospora Leaf Specked Area Estimation Using Hybrid Genetic Algorithm and Recurrent Neural Network

Abstract

Grapes are prone to Pseudocercospora vitis fungus which causes Isariopsis leaf speck disease to the crop’s leaves, flower, and most importantly the fruit. Typical manual inspection of vineyard farmers is normally ineffective, destructive, and laborious. To address this, the use of integrated computer vision, machine learning, and computational intelligence techniques were realized to sort out healthy grape leaf image from a fungus-specked leaf image and to estimate the specked area percentage (SAP). A dataset made up of 343 normally healthy and 200 fungus-specked grape leaf images were initially pre-processed and segmented via graph cut prior to feature extraction and selection. Significant features were identified using classification tree (CTree). A multigene genetic programming tool called GPTIPSv2 was utilized to generate the fitness function needed for the optimization process done via genetic algorithm (GA). An optimal hidden neuron counts of 110, 50, and 10 were selected for a three-layered GA-optimized recurrent neural network (GA-RNN). Linear discriminant analysis (LDA) topped other disease recognition models with an accuracy of 99.99%. The developed GA-RNN model outperformed Gaussian process regression (GPR), regression tree (RTree), regression support vector machine (RSVM), and linear regression (RLinear) in performing leaf specked area estimation with an R2 value of 0.822. The developed CTree-LDA2-GA-RNN2 hybrid model has been proven to be the most viable model for this task.