Comparison of EfficientNet-B0 and ResNet-50 for Detecting Diseases in Cocoa Fruit
Abstract
Cocoa is a plant that is very susceptible to disease. One of the diseases that often attacks cocoa is black spots on the fruit. Detecting diseases in cocoa fruit is usually done manually by experts, which has limitations in providing information and is very expensive. this study proposes a model for detecting cocoa fruit diseases based on deep learning, namely convolution neural networks (CNN). This study compares CNN architectures, namely EfficientNetB0 and ResNet50 because these two architectures are very popular. EfficientNetB0 is known to be efficient in utilizing model parameters and the ability to achieve high accuracy, while ResNet50 uses Residual block recognition which allows deeper and more accurate model training. The dataset used is 3344 healthy cocoa fruit images, 943 black pod rot images and 103 pod borer images. From this study, the results for the accuracy of both methods are equally superior with an accuracy of 96% while for the precision of the EfficientNetB0 architecture is superior to ResNet50 with a value of 95.7% while for recall and f1-score ResNet50 is superior with a recall value of 94.7% and f1-score 93.3%. Based on the Confusion Matrix, it can be seen that ResNet50 is able to predict pod borer accurately so it can be concluded that in this study ResNet 50 is superior to EfficientNetB0. However, ResNet50 requires more parameters than EfficientNetB0 so ResNet50 requires a very large amount of data and when using a small amount of data EfficientNetB0 is more suitable for use.
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References
Asiva Noor Rachmayani, “statistik-kakao-indonesia-2020,” p. 6, 2015.
P. Tassinari et al., “A computer vision approach based on deep learning for the detection of dairy cows in free stall barn,” Comput. Electron. Agric., vol. 182, no. May 2020, p. 106030, 2021, doi: 10.1016/j.compag.2021.106030.
M. Tsuneki, “Deep learning models in medical image analysis,” J. Oral Biosci., vol. 64, no. 3, pp. 312–320, 2022, doi: 10.1016/j.job.2022.03.003.
Z. Unal, “Smart Farming Becomes even Smarter with Deep Learning - A Bibliographical Analysis,” IEEE Access, vol. 8, pp. 105587–105609, 2020, doi: 10.1109/ACCESS.2020.3000175.
L. Chen, Deep Learning and Practice with MindSpore. 2021. doi: 10.1007/978-981-16-2233-5.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, pp. 436–444, May 2015, doi: 10.1038/nature14539.
P. Dhiman, V. Kukreja, and A. Kaur, “Citrus Fruits Classification and Evaluation using Deep Convolution Neural Networks: An Input Layer Resizing Approach,” 2021 9th Int. Conf. Reliab. Infocom Technol. Optim. (Trends Futur. Dir. ICRITO 2021, pp. 1–4, 2021, doi: 10.1109/ICRITO51393.2021.9596357.
B. Gupta, S. Bomble, O. Gaikar, S. Chalekar, S. R. Vispute, and K. Rajeswari, “Convolutional Neural Networks for Detection of Crop Diseases and Weed,” 2022 6th Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2022, pp. 1–5, 2022, doi: 10.1109/ICCUBEA54992.2022.10010772.
R. A. Godmalin, C. J. Aliac, and L. Feliscuzo, “Classification of Cacao Pod if Healthy or Attack by Pest or Black Pod Disease Using Deep Learning Algorithm,” 4th IEEE Int. Conf. Artif. Intell. Eng. Technol. IICAIET 2022, pp. 1–5, 2022, doi: 10.1109/IICAIET55139.2022.9936817.
R. Yanac Montesino, J. A. Rosales-Huamani, and J. L. Castillo-Sequera, “Detection of phytophthora palmivora in cocoa fruit with deep learning,” Iber. Conf. Inf. Syst. Technol. Cist., no. June, pp. 23–26, 2021, doi: 10.23919/CISTI52073.2021.9476279.
K. Sing Soh, E. Gubin Moung, K. John Julius Danker, J. A. Dargham, and A. Farzamnia, “Cocoa Diseases Classification using Deep Learning Algorithm,” ITM Web Conf., vol. 63, p. 01014, 2024, doi: 10.1051/itmconf/20246301014.
“https://www.kaggle.com/datasets/zaldyjr/cacao-diseases.”
H. Nisa’, C., Puspaningrum, E. Y., & Maulana, “Penerapan Metode Convolutional Neural Network untuk Klasifikasi Penyakit Daun Apel pada Imbalanced Data,” in Prosiding Seminar Nasional Informatika Bela Negara, 2020.
S. Arnandito and T. B. Sasongko, “Comparison of EfficientNetB7 and MobileNetV2 in Herbal Plant Species Classification Using Convolutional Neural Networks,” J. Appl. Informatics Comput., vol. 8, no. 1, pp. 176–185, 2024, doi: 10.30871/jaic.v8i1.7927.
S. Arnandito and T. Sasongko, “Comparison of EfficientNetB7 and MobileNetV2 in Herbal Plant Species Classification Using Convolutional Neural Networks”, JAIC, vol. 8, no. 1, pp. 176-185, Jul. 2024.
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