Detection and Quantitative Prediction of Diplocarpon earlianum Infection Rate in Strawberry Leaves using Population-based Recurrent Neural Network

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Alajas:2022:IEMTRONICS,

• author = "Oliver John Alajas and Ronnie Concepcion and Argel Bandala and Edwin Sybingco and Ryan Rhay Vicerra and Elmer P. Dadios and Christan Hail Mendigoria and Heinrick Aquino and Leonard Ambata and Bernardo Duarte",
• booktitle = "2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)",
• title = "Detection and Quantitative Prediction of Diplocarpon earlianum Infection Rate in Strawberry Leaves using Population-based Recurrent Neural Network",
• year = "2022",
• abstract = "Fragaria ananassa, a member of the rose family's flowering plants, commonly recognized as strawberry, is prone to Diplocarpon earlianum infection that causes leaf scorch. Assessment via visual inspection of strawberries by farmers is normally ineffective, destructive, and laborious. To address this challenge, the use of integrated computer vision and machine learning techniques was done to classify a healthy from a scorch-infected strawberry leaf image and to estimate the leaf region infection rate (LRIR). A dataset made up of 204 normally healthy and 161 scorch-infected strawberry leaf images was used. Images were initially preprocessed and segmented via graph-cut segmentation to extract the region of interest for feature extraction and selection. The hybrid combination of neighborhood and principal component analysis (NCA-PCA) was used to select desirable features. Multigene genetic programming (MGGP) was used to formulate the fitness function that will be essential for determining the optimized neuron configurations of the recurrent neural network (RNN) through genetic algorithm (GA), and cuckoo search algorithm (CSA), and artificial bee colony (ABC). Four classification machine learning models were configured in which the classification tree (CTree) bested other detection models with an accuracy of 10percent and exhibited the shortest inference time of 14.746 s. The developed ABC-RNN3 model outperformed GA-RNN3 and CSA-RNN3 in performing non-invasive LRIR prediction with an R2 value of 0.948. With the use of the NCA-PCA-CTree3-ABC-RNN3 hybrid model, for crop disease detection and infection rate prediction, plant disease assessment proved to be more efficient and labor cost-effective than manual disease inspection methods.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/IEMTRONICS55184.2022.9795744",
• month = jun,
• notes = "Also known as \cite{9795744}",

}

Genetic Programming entries for Oliver John Y Alajas Ronnie S Concepcion II Argel A Bandala Edwin Sybingco Ryan Rhay P Vicerra Elmer Jose P Dadios Christan Hail Mendigoria Heinrick L Aquino Leonard Ambata Bernardo Duarte

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