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:1.8081
- @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
Citations