Convolutional Neural Network for the Detection of Cocoa Maturity with an Approach for the Analysis of Images Captured at Different Distances

Abstract:

This study presents an approach to improve cocoa harvesting using image recognition technology. Convolutional neural networks (CNN) along with the help of the Roboflow platform were used to analyze images of cocoa fruits and determine their maturity stage in an uncontrolled environment. The study focused mainly on the effectiveness of taking pictures at different distances, dividing the images into three categories (0.10m-0.30m, 0.30m-1.00m and 1.00m-3.00m) each trained with 400 images in order to evaluate the performance of each one in terms of its mAP, precision and recall. Subsequently, a fourth neural network was developed using all the data collected, with a total of 1,200 images used for training, among which 3,255 unripe cocoa fruits and 2,555 ripe cocoa fruits were found. The results obtained were a mAP of 92.3%, accuracy of 91.6% and recall of 85.1%, demonstrating the high accuracy of the model in the classification of cocoa fruits. This neural network has the potential to improve the quality of the final product by accurately determining the state of maturity of the cocoa, which is essential for the industry.

• Periodical: Engineering Headway (Volume 12)
• Pages: 3-8
• DOI: https://doi.org/10.4028/p-JM3tGd
• Online since: September 2024
• Authors: José Antonio Menjivar Rivera, Alicia María Reyes-Duke*
• Keywords: Artificial Intelligence, Cocoa Maturity, Convolutional Neural Network, Distance

References

[1] Fadilah, N., Mohamad-Saleh, J., Abdul Halim, Z., Ibrahim, H., & Syed Ali, S. S. (2012). Intelligent Color Vision System for Ripeness Classification of Oil Palm Fresh Fruit Bunch. Sensors, 12(10), Article 10

DOI: 10.3390/s121014179

[2] G. E. Bueno, K. A. Valenzuela, and E. R. Arboleda, "Maturity classification of cacao through spectrogram and convolutional neural network," Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 3, pp.228-233, 2020. doi: 10.14710/jtsiskom.2020.13733, [Online].

DOI: 10.14710/jtsiskom.2020.13733

[3] J. R. C. P. de Oliveira and R. A. F. Romero, "Transfer Learning Based Model for Classification of Cocoa Pods," 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 2018, pp.1-6.

DOI: 10.1109/IJCNN.2018.8489126

[4] R. Dandavate and V. Patodkar, "CNN and Data Augmentation Based Fruit Classification Model," 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 2020, pp.784-787.

DOI: 10.1109/I-SMAC49090.2020.9243440

[5] Kamilaris A, Prenafeta-Boldú FX. A review of the use of convolutional neural networks in agriculture. The Journal of Agricultural Science. 2018;156(3):312-322. doi:10.1017/S0021859618000436[6] K. Kangune, V. Kulkarni and P. Kosamkar, "Grapes Ripeness Estimation using Convolutional Neural network and Support Vector Machine," 2019 Global Conference for Advancement in Technology (GCAT), Bangalore, India, 2019, pp.1-5.

DOI: 10.1109/GCAT47503.2019.8978341

[7] F. E. Ramirez Rios and A. María Reyes Duke, "Building of a Convolutional Neuronal Network for the prediction of mood states through face recognition based on object detection with YOLOV8 and Python," 2023 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), San Salvador, El Salvador, 2023, pp.1-6.

DOI: 10.1109/ICMLANT59547.2023.10372862

[8] Kumi, S., Kelly, D., Woodstuff, J., Lomotey, R. K., Orji, R.,& Deters, R. (2022). Cocoa Companion: Deep Learning-Based Smartphone Application for Cocoa Disease Detection. Procedia Computer Science, 203, 87-94

DOI: 10.1016/j.procs.2022.07.013

[9] Restrepo-Arias, J. F., Salinas-Agudelo, M. I., Hernandez Pérez, M. I., Marulanda-Tobón, A., & Giraldo-Carvajal, M. C. (2023). RipSetCocoaCNCH12 Dataset [Data set]. Zenodo

DOI: 10.3390/data8060112

[10] Worasawate, D., Sakunasinha, P., & Chiangga, S. (2022). Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach. AgriEngineering, 4(1), Article 1

DOI: 10.3390/agriengineering4010003

[11] Davur, Y. J., Kämper, W., Khoshelham, K., Trueman, S. J., & Bai, S. H. (2023). Estimating the Ripeness of Hass Avocado Fruit Using Deep Learning with Hyperspectral Imaging. Horticulturae, 9(5), Article 5

DOI: 10.3390/horticulturae9050599

[12] N. Nafi'Iyah, R. Wardhani and E. Prakasa, "Identification of Banana Ripeness using Convolutional Neural Network Approaches," 2023 International Conference on Computer, Control, Informatics and its Applications (IC3INA), Bandung, Indonesia, 2023, pp.330-335.

DOI: 10.1109/IC3INA60834.2023.10285749

[13] K. C. Gunawan and Z. S. Lie, "Apple Ripeness Level Detection Based On Skin Color Features With Convolutional Neural Network Classification Method," 2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), Malang, Indonesia, 2021, pp.1-6.

DOI: 10.1109/ICEEIE52663.2021.9616629

[14] D. Deepa, A. Sivasangari, R. Roonwal and R. Nayan, "Pothole Detection using Roboflow Convolutional Neural Networks," 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2023, pp.560-564.

DOI: 10.1109/ICICCS56967.2023.10142700

[15] A. G. Menjivar, "Mobile App Development for Wireless Monitoring and Configuration of Sensors using ESP8266," 2022 IEEE Central America and Panama Student Conference (CONESCAPAN), San Salvador, El Salvador, 2022, pp.1-6.

DOI: 10.1109/CONESCAPAN56456.2022.9959569

[16] D. A. Jimenez-Nixon, J. F. M. Corrales and A. M. Reyes-Duke, "Coral Detection using Artificial Neural Networks based on Blurry Images for Reef Protection in Cayo Blanco, Honduras," 2022 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), Soyapango, El Salvador, 2022, pp.1-6.

DOI: 10.1109/ICMLANT56191.2022.9996481