Optimization of CART Models Using Metaheuristics for Predicting Peach Firmness
Source abstract
Abstract
The current advancements in the field of machine learning can have an important application in agriculture and global food security. Machine learning has considerable potential in establishing knowledge-based farming systems. One of the main challenges of data-driven agriculture is to minimize food waste and establish more sustainable farming systems. The prediction of the right harvest time is one of the ways to obtain the mentioned goals. This paper describes multiple machine learning algorithms that are used to predict peach firmness. By accurately predicting peach firmness based on various peach measurement data, a more precise harvest time can be obtained. The evaluation of nature-inspired metaheuristic optimization algorithms in enhancing machine learning model accuracy is the primary objective of this paper. The possibility of improving the peach firmness prediction accuracy of regression tree models using various metaheuristic optimization techniques implemented in GA and metaheuristicOpt R packages is studied. The RMSE on test data of the default regression tree model is 1.722285, while the regression tree model optimized using the gray wolf optimization algorithm scored the lowest RMSE of 1.570924. The obtained results show that it is possible to improve the peach firmness prediction accuracy of the regression tree model by 8.8% using the described method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Greenhouse Technology and Climate Control · Plant Physiology and Cultivation Studies · Evolutionary Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Informatics and Digital Technologies, University of Rijeka, 51000 Rijeka, Croatia
Center for Artificial Intelligence and Cybersecurity, University of Rijeka, 51000 Rijeka, Croatia
Faculty of Maritime Studies, University of Rijeka, 51000 Rijeka, Croatia
References (45)
Show source-provided references
1. Ivanovski, T., Zhang, X., Jemrić, T., Gulić, M., and Matetić, M. (2022, January 27–28). Firmness Prediction Using Optimized Regression Trees Models. Proceedings of the 33rd DAAAM International Symposium on Intelligent Manufacturing and Automation, Vienna, Austria.10.2507/33rd.daaam.proceedings.067 ↗
2. Zhang, G., Fu, Q., Fu, Z., Li, X., Matetić, M., Bakaric, M.B., and Jemrić, T. (2020). A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption. Appl. Sci., 10.10.3390/app10041348 ↗
3. Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation10.1007/s11042-017-5243-3 ↗
4. Color Distribution Analysis for Ripeness Prediction of Golden Apollo Melon10.12928/telkomnika.v16i4.8432 ↗
5. Ivanovski, T., Guoxiang, Z., Jemrić, T., Gulić, M., and Matetić, M. (October, January 28). Fruit firmness prediction using multiple linear regression. Proceedings of the 43rd International Convention MIPRO, Opatija, Croatia.
6. An Overview and Comparison of Selected State-of-the-Art Algorithms Inspired by Nature
7. Eiben, A.E., and Smith, J.E. (2015). Introduction to Evolutionary Computing, Springer. [2nd ed.].10.1007/978-3-662-44874-8 ↗
8. Evaluating banana ripening status from measuring dielectric properties10.1016/j.jfoodeng.2011.03.032 ↗
9. Influence of Frequency of Electric Excitation Signal on Dielectric Property of Fuji Apples with Red-dot Disease
10. Real-time pixel based early apple bruise detection using short wave infrared hyperspectral imaging in combination with calibration and glare correction techniques10.1016/j.foodcont.2016.02.007 ↗
11. Dielectric Based Sensing System for Banana Ripeness Assessment10.7763/IJESD.2014.V5.493 ↗
12. Sabzi, S., Abbaspour-Gilandeh, Y., García-Mateos, G., Ruiz-Canales, A., Molina-Martínez, J.M., and Arribas, J.I. (2019). An Automatic Non-Destructive Method for the Classification of the Ripeness Stage of Red Delicious Apples in Orchards Using Aerial Video. Agronomy, 9.10.3390/agronomy9020084 ↗
13. A computing offloading strategy for UAV based on improved bat algorithm10.1016/j.cogr.2023.07.005 ↗
14. Improved wireless sensor network data collection using discrete differential evolution and ant colony optimization
15. Modelling and optimizing hydraulic retention time in the biological aeration unit: Application of artificial neural network and particle swarm optimization
16. An optimized approach for solar concentrating parabolic dish based on particle swarm optimization-genetic algorithm10.1016/j.heliyon.2024.e26165 ↗
17. Optimizing Broadband Access and Network Design in Wireless Mesh Networks using Multi-Objective Particle Swarm Optimization10.1016/j.procs.2023.12.083 ↗
18. Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor
19. Fuzzy logic controller for UAV with gains optimized via genetic algorithm10.1016/j.heliyon.2024.e26363 ↗
20. Predicting biochemical oxygen demand in wastewater treatment plant using advance extreme learning machine optimized by Bat algorithm10.1016/j.heliyon.2023.e21351 ↗
21. Brain tumor recognition using an integrated bat algorithm with a convolutional neural network approach10.1016/j.measen.2022.100426 ↗
22. Improved differential evolution algorithm based convolutional neural network for emotional analysis of music data10.1016/j.asoc.2024.111262 ↗
23. Differential evolution-based neural architecture search for brain vessel segmentation
24. Decoupling control of bearingless brushless DC motor using particle swarm optimized neural network inverse system10.1016/j.measen.2023.100952 ↗
25. Optimizing the configuration of deep learning models for music genre classification10.1016/j.heliyon.2024.e24892 ↗
26. Optimization of decision trees using modified African buffalo algorithm
27. Optimizing decision trees for English Teaching Quality Evaluation (ETQE) using Artificial Bee Colony (ABC) optimization10.1016/j.heliyon.2023.e19274 ↗
28. Lantz, B. (2015). Machine Learning with R, Packt Publishing Ltd.. [2nd ed.].
29. Electrical impedance measurement on plants: A review with some insights to other fields10.1007/s40626-019-00152-y ↗
30. Ibba, P., Falco, A., Abera, B.D., Cantarella, G., Petti, L., and Lugli, P. (2020). Bio-impedance and circuit parameters: An analysis for tracking fruit ripening. Postharvest Biol. Technol., 159.10.1016/j.postharvbio.2019.110978 ↗
31. Electrical impedance spectroscopy (EIS) for biological analysis and food characterization: A review10.5194/jsss-6-303-2017 ↗
32. Electric impedance, in objective index of maturity in peach10.4141/cjps66-051 ↗
33. Ripening of Nectarine Fruit Changes in the Cell Wall, Vacuole, and Membranes Detected Using Electrical Impedance Measurements10.1104/pp.106.1.165 ↗
34. Shmueli, G., Bruce, P.C., Yahov, I., Patel, N.R., and Lichtendahl, K.C. (2018). Data Mining for Business Analytics, John Wiley & Sons, Inc.
35. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R, Springer Science+Business Media. Corrected at the printing 2017.10.1007/978-1-4614-7138-7_2 ↗
36. ANFIS Adaptive-Network-based Fuzzy Inference System10.1109/21.256541 ↗
37. Bat Algorithm: Literature Review and Applications10.1504/IJBIC.2013.055093 ↗
38. Nature-inspired optimization algorithms: Challenges and open problems10.1016/j.jocs.2020.101104 ↗
39. Du, K.-L., and Swamy, M.N.S. (2016). Search and Optimization by Metaheuristics-Techniques and Algorithms Inspired by Nature, Birkhäuser.10.1007/978-3-319-41192-7_3 ↗
40. Differential evolution: A recent review based on state-of-the-art works10.1016/j.aej.2021.09.013 ↗
41. Hassanien, A.E., and Emary, E. (2016). Swarm Intelligence: Principles, Advances, and Applications, Taylor and Francis Group.10.1201/b19133 ↗
42. Li, Y., Lin, X., and Liu, J. (2021). An Improved GrayWolf Optimization Algorithm to Solve Engineering Problems. Sustainability, 13.10.3390/su13063208 ↗
43. Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives10.3390/make1010010 ↗
44. A hybrid Dragonfly algorithm for the vehicle routing problem with stochastic demands
45. Optimization of transformer parameters at distribution and power levels with hybrid Grey wolf-whale optimization algorithm