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JACIII Vol.27 No.1 pp. 35-43
doi: 10.20965/jaciii.2023.p0035
(2023)

Research Paper:

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

Oliver John Y. Alajas*,†, Ronnie S. Concepcion II**, Maria Gemel B. Palconit*, Argel A. Bandala*, Edwin Sybingco*, Ryan Rhay P. Vicerra**, Elmer P. Dadios**, Christan Hail R. Mendigoria*, Heinrick L. Aquino*, and Luigi Gennaro Izzo***

*Department of Electronics and Computer Engineering, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

**Department of Manufacturing Engineering and Management, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

***Department of Agricultural Sciences, University of Naples Federico II (UNINA)
Via Università 100, Portici 80055, Italy

Corresponding author

Received:
April 10, 2022
Accepted:
July 5, 2022
Published:
January 20, 2023
Keywords:
computational intelligence, computer vision, grape leaf disease, Pseudocercospora fungus
Abstract
Grape <i>Pseudocercospora</i> Leaf Specked Area Estimation Using Hybrid Genetic Algorithm and Recurrent Neural Network

Image segmentation of grape leaf images

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.

Cite this article as:
O. Alajas, R. II, M. Palconit, A. Bandala, E. Sybingco, R. Vicerra, E. Dadios, C. Mendigoria, H. Aquino, and L. Izzo, “Grape Pseudocercospora Leaf Specked Area Estimation Using Hybrid Genetic Algorithm and Recurrent Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.1, pp. 35-43, 2023.
Data files:
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Last updated on Feb. 08, 2023