Abstract: Cacao, scientifically known as Theobroma cacao, is a highly nutritious food and is extensively utilized in multiple sectors, including agriculture and health. Nevertheless, the agricultural sector encounters notable obstacles as a result of Cacao disease such as pod rot, predominantly attributed to the Phytophthora genus. The objective of this work is to conduct a comparative analysis to determine the most effective machine-learning technique for the detection of P. palmivora infection in Cacao pods. Few studies have delved into this topic previously, but this study focuses in utilizing a little larger dataset, achieving better model, and attaining higher accuracy. A total of 2000 images of cacao pods, both healthy and disease-infected were collected. Subsequently, the images were subjected to manual classification by a domain expert based on the discernible presence or absence of the disease. The study examined six machine learning algorithms, specifically Naïve Bayes, Random Forest, Hoeffding Tree, Multilayer Neural Network, and Convolutional Neural Network (CNN). The CNN model had 99% level of accuracy, the highest among the five machine learning algorithms in the testing phase. The methodology has the potential to significantly advance sustainable agricultural practices and disease management. To enhance the model's recognition capabilities, additional datasets encompassing a broader range of Cacao varieties is necessary.

Jude B. Rola, Jomari Joseph A. Barrera, Maricel V. Calhoun, Jonah Flor Oraño – Maaghop, Magdalene C. Unajan, Joshua Mhel Boncalon, Elizabeth T. Sebios and Joy S. Espinosa, “Convolutional Neural Network Model for Cacao Phytophthora Palmivora Disease Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 15(8), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150897

@article{Rola2024,
title = {Convolutional Neural Network Model for Cacao Phytophthora Palmivora Disease Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150897},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150897},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {8},
author = {Jude B. Rola and Jomari Joseph A. Barrera and Maricel V. Calhoun and Jonah Flor Oraño – Maaghop and Magdalene C. Unajan and Joshua Mhel Boncalon and Elizabeth T. Sebios and Joy S. Espinosa}
}