RECENT DEVELOPMENTS OF ARTIFICIAL INTELLIGENCE FOR BANANA: APPLICATION AREAS, LEARNING ALGORITHMS, AND FUTURE CHALLENGES

Almeyda, Estefani; Ipanaqué, William

doi:10.1590/1809-4430-Eng.Agric.v42nepe20210144/2022

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Review Paper, Special Issue: Artificial Inteligence • Eng. agríc. (Online) 42 (spe) • 2022 • https://doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210144/2022 copy

RECENT DEVELOPMENTS OF ARTIFICIAL INTELLIGENCE FOR BANANA: APPLICATION AREAS, LEARNING ALGORITHMS, AND FUTURE CHALLENGES

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Bananas are the world’s most traded fruits. Several analytical models using artificial intelligence (AI) have been developed to resolve challenges facing the banana supply chain. The number of publications in this field has steadily increased each year. However, a literature review regarding the trends of recent AI developments is not available. Thus, this study reviews the current scenario of scientific research involving AI in the stages of the banana supply chain (pre-harvest, harvest, post-harvest, processing and retail). This review covers literature published between 2015 and 2020 from online databases. Fifty-two relevant studies were retrieved from 23 countries. Consequently, we propose an AI-performance framework based on real applications implemented for bananas: the application domain, learning algorithms, performance metrics, and reported impacts. This paper discovers 11 AI-application areas for bananas, such as ripeness, leaf diseases, quality grading, crop type, crop yield, and soil control. Moreover, this review summarizes the main functionality of learning algorithms found in the literature (ANN, CNN, SVM, and K-NN). Finally, the future challenges are discussed. This comprehensive review will help researchers understand AI applications in the banana sector and analyze the knowledge gap for future studies.

KEYWORDS
review; banana; predictive model; machine learning; deep learning

	Application area	Description	Number of studies (n=52)	References
Pre-Harvest	Crop type	Crop type detection. Identifying banana plantations among other crops.	5	(Ringland et al., 2019Ringland J, Bohm M, Baek SR (2019) Characterization of food cultivation along roadside transects with Google Street View imagery and deep learning. Computers and Electronics in Agriculture 158:36–50. DOI: https://doi.org/10.1016/j.compag.2019.01.014 https://doi.org/10.1016/j.compag.2019.01... ), (Neupane et al., 2019Neupane B, Horanont T, Hung ND (2019) Deep learning based banana plant detection and counting using high-resolution red–green-blue (RGB) images collected from unmanned aerial vehicle (UAV). PLOS ONE 14(10):e0223906. DOI: https://doi.org/10.1371/journal.pone.0223906 https://doi.org/10.1371/journal.pone.022... ), (Zhao et al., 2019Zhao H, Chen Z, Jiang H, Jing W, Sun L, Feng M (2019) Evaluation of three deep learning models for early crop classification using Sentinel-1A imagery time - A Case Study in Zhanjiang, China. Remote Sensing 11(22):2673. DOI: https://doi.org/10.3390/rs11222673 https://doi.org/10.3390/rs11222673... ), (Mandal et al., 2020Mandal D, Kumar V, Rao YS (2020) An assessment of temporal RADARSAT-2 SAR data for crop classification using KPCA based support vector machine. Geocarto International: 1-28. DOI: https://doi.org/10.1080/10106049.2020.1783577 https://doi.org/10.1080/10106049.2020.17... ), (Sinha et al., 2020Sinha P, Robson A, Schneider D, Kilic T, Mugera HK, Ilukor J, Tindamanyire JM (2020) The potential of in-situ hyperspectral remote sensing for differentiating 12 banana genotypes grown in Uganda. ISPRS Journal of Photogrammetry and Remote Sensing 167:85–103. DOI: https://doi.org/10.1016/j.isprsjprs.2020.06.023 https://doi.org/10.1016/j.isprsjprs.2020... )
	Soil	Soil quality classification. Classifying the type or quality of soil in banana cultivation.	4	(David & Guico, 2019David AIC, Guico MLC (2019) Presence or absence of Fusarium oxysporum f. sp. cubense tropical Race 4 (TR4) classification using machine learning methods on soil properties IEEE. Region 10 Annual International Conference. (TENCON). IEEE, Jeju, Proceedings… DOI: https://doi.org/10.1109/TENCON.2018.8650116 https://doi.org/10.1109/TENCON.2018.8650... ), (Vite Cevallos et al., 2020Vite Cevallos H, Carvajal Romero H, Barrezueta Unda S (2020) Aplicación de algoritmos de aprendizaje automático para clasificar la fertilidad de un suelo bananero. Revista Conrado 16(72):15–19. Available: https://conrado.ucf.edu.cu/index.php/conrado/article/view/1202 https://conrado.ucf.edu.cu/index.php/con... ), (Yuan et al., 2020Yuan J, Wen T, Zhang H, Zhao M, Penton CR, Thomashow LS, Shen Q (2020) Predicting disease occurrence with high accuracy based on soil macroecological patterns of Fusarium wilt. ISME Journal 14(12):2936–2950. DOI: https://doi.org/10.1038/s41396-020-0720-5 https://doi.org/10.1038/s41396-020-0720-... ), (Vigneswaran & Selvaganesh, 2020Vigneswaran E, Selvaganesh M (2020) Decision support system for crop rotation using. 4th International Conference on Inventive Systems and Control (ICISC). Machine Learning, Proceedings… DOI: https://doi.org/10.1109/ICISC47916.2020.9171120 https://doi.org/10.1109/ICISC47916.2020.... )
	Diseases	Disease detection and classification. Detecting diseased banana leaves or classifying the type of disease that affect leaves.	11	(Singh & Misra, 2017Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture 4(1):41–49. DOI: https://doi.org/10.1016/j.inpa.2016.10.005 https://doi.org/10.1016/j.inpa.2016.10.0... ), (Amara et al., 2017Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. In: Proceedings of Gesellschaft für Informatik Datenbanksysteme für business, Technologie und Web (BTW Workshop). Alemania, dblp computer science bibliography. p.79-88. Proceedings… Available: https://dblp.org/rec/conf/btw/AmaraBA17.html. Accessed Jan 08, 2020. https://dblp.org/rec/conf/btw/AmaraBA17.... ), (Ferentinos, 2018Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145:311–318. DOI: https://doi.org/10.1016/j.compag.2018.01.009 https://doi.org/10.1016/j.compag.2018.01... ), (Aruraj et al., 2019Aruraj A, Alex A, Subathra MSP, Sairamya NJ, Thomas George S, Vinodh Ewards SE (2019) Detection and classification of diseases of banana plant using local binary pattern and support vector machine. In: International Conference on Signal Processing and Communication (ICSPC). Proceedings... DOI: https://doi.org/10.1109/ICSPC46172.2019.8976582 https://doi.org/10.1109/ICSPC46172.2019.... ), (Liao et al., 2019Liao W, Ochoa D, Gao L, Zhang B, Philips W (2019) Morphological analysis for banana disease detection in close range hyperspectral remote sensing images. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Proceedings…p.3697-3700. DOI: https://doi.org/10.1109/IGARSS.2019.8899087 https://doi.org/10.1109/IGARSS.2019.8899... ), (Tsai et al., 2019Tsai C-F, Chen Y-C, Tsai C-E (2019) Real life image recognition of Panama disease by an effective deep learning approach. International Conference on Machine Learning and Cybernetics. Proceedings…1–5. DOI: https://doi.org/10.1109/ICMLC48188.2019.8949269 https://doi.org/10.1109/ICMLC48188.2019.... ), (Campos Calou et al., 2020Calou VBC, Teixeira AdS, Moreira LCJ, Lima CS, de Oliveira JB, de Oliveira MRR (2020) The use of UAVs in monitoring yellow sigatoka in banana. Biosystems Engineering 193:115–125. DOI: https://doi.org/10.1016/j.biosystemseng.2020.02.016 https://doi.org/10.1016/j.biosystemseng.... ), (Athiraja & Vijayakumar, 2021Athiraja A, Vijayakumar P (2021) Banana disease diagnosis using computer vision and machine learning methods. Journal of Ambient Intelligence and Humanized Computing 12(6):6537–6556. DOI: https://doi.org/10.1007/s12652-020-02273-8 https://doi.org/10.1007/s12652-020-02273... ), (Chaudhari & Patil, 2020Chaudhari V, Patil M (2020) Banana leaf disease detection using K-means clustering and Feature extraction techniques. In: IEEE International Conference on Advances in Computing, Communication & Materials (ICACCM). Proceedings…p126–130. DOI: https://doi.org/10.1109/icaccm50413.2020.9212816 https://doi.org/10.1109/icaccm50413.2020... ), (Gomez Selvaraj et al., 2020Gomez Selvaraj M, Vergara A, Montenegro F, Alonso Ruiz H, Safari N, Raymaekers D, Ocimati W, Ntamwira J, Tits L, Omondi AB, Blomme G (2020) Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin. ISPRS Journal of Photogrammetry and Remote Sensing 169:110–124. DOI: https://doi.org/10.1016/j.isprsjprs.2020.08.025 https://doi.org/10.1016/j.isprsjprs.2020... ), (Criollo et al., 2020Criollo A, Mendoza M, Saavedra E, Vargas G (2020) Design and evaluation of a convolutional neural network for banana leaf diseases classification. In: Proceedings of IEEE Engineering International Research Conference (EIRCON). Proceedings…p1-4. DOI: https://doi.org/10.1109/eircon51178.2020.9254072 https://doi.org/10.1109/eircon51178.2020... )
	Pest	Classification of pest incidence. Estimating the level of pest incidence in banana plantations.	1	(Almeyda et al., 2020Almeyda E, Paiva J, Ipanaque W (2020) Pest incidence prediction in organic banana crops with machine learning techniques. In: Proceedings of IEEE Engineering International Research Conference (EIRCON). Proceedings…p1-4. DOI: https://doi.org/10.1109/eircon51178.2020.9254034 https://doi.org/10.1109/eircon51178.2020... )
Harvest	Ripeness	Classification of ripeness level. Analyzing the ripeness of the fruit and classifying the stage of ripeness.	13	(Adebayo et al., 2016Adebayo SE, Hashim N, Abdan K, Hanafi M, Mollazade K (2016) Prediction of quality attributes and ripeness classification of bananas using optical properties. Scientia Horticulturae 212:171–182. DOI: https://doi.org/10.1016/j.scienta.2016.09.045 https://doi.org/10.1016/j.scienta.2016.0... ), (Adebayo et al., 2017Adebayo SE, Hashim N, Abdan K, Hanafi M, Zude-Sasse M (2017) Prediction of banana quality attributes and ripeness classification using artificial neural network. Acta Horticulturae 1152(1152):335–344. DOI: https://doi.org/10.17660/ActaHortic.2017.1152.45 https://doi.org/10.17660/ActaHortic.2017... ), (Mohapatra et al., 2017Mohapatra A, Shanmugasundaram S, Malmathanraj R (2017) Grading of ripening stages of red banana using dielectric properties changes and image processing approach. Computers and Electronics in Agriculture 143:100–110. DOI: https://doi.org/10.1016/j.compag.2017.10.010 https://doi.org/10.1016/j.compag.2017.10... ), (Zhang et al., 2018Zhang Y, Lian J, Fan M, Zheng Y (2018) Deep indicator for fine-grained classification of banana’s ripening stages. EURASIP Journal on Image and Video Processing 2018(1):1–10. DOI: https://doi.org/10.1186/s13640-018-0284-8 https://doi.org/10.1186/s13640-018-0284-... ), (Chen et al., 2018Chen LY, Wu CC, Chou TI, Chiu SW, Tang KT (2018) Development of a dual MOS electronic nose/camera system for improving fruit ripeness classification. Sensors 18(10):3256. DOI: https://doi.org/10.3390/s18103256 https://doi.org/10.3390/s18103256... ), (Mazen & Nashat, 2019Mazen FMA, Nashat AA (2019) Ripeness classification of bananas using an artificial neural network. Arabian Journal for Science and Engineering 44(8):6901–6910. DOI: https://doi.org/10.1007/s13369-018-03695-5 https://doi.org/10.1007/s13369-018-03695... ), (Pu et al., 2019Pu YY, Sun DW, Buccheri M, Grassi M, Cattaneo TMP, Gowen A (2019) Ripeness classification of Bananito fruit (Musa acuminata, AA): A comparison study of visible spectroscopy and hyperspectral imaging. Food Analytical Methods 12(8):1693–1704. DOI: https://doi.org/10.1007/s12161-019-01506-7 https://doi.org/10.1007/s12161-019-01506... ), (Sabilla et al., 2019Sabilla IA, Wahyuni CS, Fatichah C, Herumurti D (2019) Determining banana types and ripeness from image using machine learning methods. In: IEEE International Conference of Artificial Intelligence and Information Technology (ICAIIT). Proceedings… DOI: https://doi.org/10.1109/ICAIIT.2019.8834490 https://doi.org/10.1109/ICAIIT.2019.8834... ), (Vetrekar et al., 2019aVetrekar N, Ramachandra R, Raja KB, Gad RS (2019a) Multi-spectral imaging for artificial ripened banana detection. In: European Workshop on Visual Information Processing (EUVIP). Proceedings... DOI: https://doi.org/10.1109/EUVIP47703.2019.8946158 https://doi.org/10.1109/EUVIP47703.2019.... ), (Vetrekar et al., 2019bVetrekar N, Ramachandra R, Raja KB, Gad RS (2019b) Multi-spectral imaging to detect artificial ripening of banana: A comprehensive empirical study. In: IEEE International Conference on Imaging Systems and Techniques (IST). Proceedings... DOI: https://doi.org/10.1109/IST48021.2019.9010525 https://doi.org/10.1109/IST48021.2019.90... ), (Saad et al., 2019Saad D, Rotzer S, Zimmermann M (2019) Set-based design in agile development: Developing a banana sorting module – A practical approach. IEEE International Conference on Industrial Engineering and Engineering Management 159–164. DOI: https://doi.org/10.1109/IEEM44572.2019.8978748 https://doi.org/10.1109/IEEM44572.2019.8... ), (Zhu & Spachos, 2020Zhu L, Spachos P (2020) Food grading system using support vector machine and YOLOv3 methods. In: IEEE Symposium on Computers and Communications (ISCC). Proceedings… p1-6. DOI: https://doi.org/10.1109/ISCC50000.2020.9219589 https://doi.org/10.1109/ISCC50000.2020.9... ), (Ni et al., 2020Ni J, Gao J, Deng L, Han Z (2020) Monitoring the change process of banana freshness by GoogLeNet. IEEE Access 8:228369–228376. DOI: https://doi.org/10.1109/ACCESS.2020.3045394:228369 https://doi.org/10.1109/ACCESS.2020.3045... )
Harvest	Age bunch	Classification of banana age bunches. Detecting and classifying the level of maturity or shape of bunches on the banana plant.	1	(Fu et al., 2020Fu L, Duan J, Zou X, Lin J, Zhao L, Li J, Yang Z (2020) Fast and accurate detection of banana fruits in complex background orchards. IEEE Access 8:196835–196846. DOI: https://doi.org/10.1109/ACCESS.2020.3029215:196835 https://doi.org/10.1109/ACCESS.2020.3029... )
Post-Harvest	Quality grading	Banana grade classification. Classifying the quality of banana fruit at post-harvest.	5	(Sanaeifar et al., 2016Sanaeifar A, Bakhshipour A, De La Guardia M (2016) Prediction of banana quality indices from color features using support vector regression. Talanta 148:54–61. DOI: https://doi.org/10.1016/j.talanta.2015.10.073 https://doi.org/10.1016/j.talanta.2015.1... ), (Olaniyi et al., 2017Olaniyi EO, Adekunle AA, Odekuoye T, Khashman A (2017) Automatic system for grading banana using GLCM texture feature extraction and neural network arbitrations. Journal of Food Process Engineering 40(6):1–10. DOI: https://doi.org/10.1111/jfpe.12575 https://doi.org/10.1111/jfpe.12575... ), (Piedad et al., 2018Piedad EJ, Larada JI, Pojas GJ, Ferrer LVV (2018) Postharvest classification of banana (Musa acuminata) using tier-based machine learning. Postharvest Biology and Technology 145:93–100. DOI: https://doi.org/10.1016/j.postharvbio.2018.06.004 https://doi.org/10.1016/j.postharvbio.20... ), (Le et al., 2019Le T-T, Lin C-Y, Piedad EJ (2019) Deep learning for noninvasive classification of clustered horticultural crops – A case for banana fruit tiers. Postharvest Biology and Technology 156. DOI: https://doi.org/10.1016/j.postharvbio.2019.05.023:110922 https://doi.org/10.1016/j.postharvbio.20... ), (Ucat & Cruz, 2019Ucat RC, Cruz JCD (2019) Postharvest grading classification of cavendish banana using deep learning and Tensorflow. In: International Symposium on Multimedia and Communication Technology (ISMAC). Proceedings... DOI: https://doi.org/10.1109/ISMAC.2019.8836129 https://doi.org/10.1109/ISMAC.2019.88361... )
	Fruit recognition	Recognition of the fruit-shape. Classifying types of banana cultivars or detecting banana among other fruits.	4	(Dittakan et al., 2017Dittakan K, Theera-Ampornpunt N, Witthayarat W, Hinnoy S, Klaiwan S, Pratheep T (2017) Banana cultivar classification using scale invariant shape analysis. In: International Conference on Information Technology (INCIT). Proceedings... DOI: https://doi.org/10.1109/INCIT.2017.8257854 https://doi.org/10.1109/INCIT.2017.82578... ), (Mureşan & Oltean, 2018Mureşan H, Oltean M (2018) Fruit recognition from images using deep learning. Acta Universitatis Sapientiae, Informatica 10(1):26–42. DOI: https://doi.org/10.2478/ausi-2018-0002 https://doi.org/10.2478/ausi-2018-0002... ), (Xue et al., 2020Xue G, Liu S, Ma Y (2020) A hybrid deep learning-based fruit classification using attention model and convolution autoencoder. Complex and Intelligent Systems Oct 1-11. DOI: https://doi.org/10.1007/s40747-020-00192-x https://doi.org/10.1007/s40747-020-00192... ), (Sugadev et al., 2020Sugadev M, Sucharitha K, Sheeba IR, Velan B (2020) Computer vision based automated billing system for fruit stores. In: 3rd International Conference on Smart Systems and Inventive Technology (ICSSIT). Proceedings. DOI: https://doi.org/10.1109/ICSSIT48917.2020.9214101 https://doi.org/10.1109/ICSSIT48917.2020... )
	Crop yield	Crop yield forecasting. Classifying the yield level of the banana crop (tons/hectare) or estimating the harvest period (days).	5	(Rathod & Mishra, 2018Rathod S, Mishra GC (2018) Statistical models for forecasting mango and banana yield of Karnataka, India. Journal of Agricultural Science and Technology 20(4):803–816. Available: http://jast.modares.ac.ir/article-23-19768-en.html http://jast.modares.ac.ir/article-23-197... ), (Rebortera & Fajardo, 2019bRebortera M, Fajardo A (2019b) Forecasting banana harvest yields using deep learning. In: International Conference on System Engineering Technology (ICSET). Proceedings…p380-384. DOI: https://doi.org/10.1109/ICSEngT.2019.8906427 https://doi.org/10.1109/ICSEngT.2019.890... ), (Rebortera & Fajardo, 2019aRebortera M, Fajardo A (2019a) An enhanced deep learning approach in forecasting banana harvest yields. International Journal of Advanced Computer Science and Applications 10(9):275–280. DOI: https://doi.org/10.14569/IJACSA.2019.0100935 https://doi.org/10.14569/IJACSA.2019.010... ), (de Souza et al., 2019de Souza AV, Bonini Neto A, Cabrera Piazentin J, Dainese Junior BJ, Perin Gomes E, Dos Santos Batista Bonini C, Ferrari Putti F (2019) Artificial neural network modelling in the prediction of bananas’ harvest. Scientia Horticulturae 257. DOI: https://doi.org/10.1016/j.scienta.2019.108724:108724 https://doi.org/10.1016/j.scienta.2019.1... ), (de Lima Neto et al., 2020de Lima Neto AJ, Deus JAL, Rodrigues Filho VA, Natale W, Parent LE (2020) Nutrient diagnosis of fertigated “prata” and “cavendish” banana (Musa spp.) at plot-scale. Plants 9(11):1467. DOI: https://doi.org/10.3390/plants9111467 https://doi.org/10.3390/plants9111467... )
Processing	Process parameters	Estimation of process parameters. Predicting output parameters in the culinary banana drying process.	2	(Guiné et al., 2015Guiné RPF, Barroca MJ, Gonçalves FJ, Alves M, Oliveira S, Mendes M (2015) Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. Food Chemistry 168:454–459. DOI: https://doi.org/10.1016/j.foodchem.2014.07.094 https://doi.org/10.1016/j.foodchem.2014.... ), (Khawas et al., 2016Khawas P, Dash KK, Das AJ, Deka SC (2016) Modeling and optimization of the process parameters in vacuum drying of culinary banana (Musa ABB) slices by application of artificial neural network and genetic algorithm. Drying Technology 34(4):491–503. DOI: https://doi.org/10.1080/07373937.2015.1060605 https://doi.org/10.1080/07373937.2015.10... )
Retail	Production for consumers	Forecasting of banana production. From the total production (country) for the last years, forecasting production for the following years.	1	(Rehman et al., 2018Rehman A, Deyuan Z, Hussain I, Iqbal MS, Yang Y, Jingdong L (2018) Prediction of major agricultural fruits production in Pakistan by using an econometric analysis and machine learning technique. International Journal of Fruit Science 18(4):445–461. DOI: https://doi.org/10.1080/15538362.2018.1485536 https://doi.org/10.1080/15538362.2018.14... )

Stage	Application area	India	China	Philippines	Taiwan	Thailand	Malaysia	Brazil	Peru	Ecuador	Germany	Greece, Belgium, and Colombia	Romania	Portugal	USA and Australia	Iran and Turkey	Egypt, Ireland, Indonesia, and Canada
Pre-Harvest	Crop type	✓	✓			✓									✓
	Soil	✓	✓	✓						✓
	Diseases	✓			✓			✓	✓		✓	✓
	Pest								✓
Harvest	Ripeness	✓	✓		✓		✓				✓						✓
Harvest	Age bunch		✓
Post-Haevest	Quality grading			✓	✓											✓
	Fruit recognition	✓	✓			✓							✓
	Crop yield	✓		✓				✓
Processing	Process parameters	✓												✓
Retail	Production		✓

Stages	Domain area	Algorithm		Metrics reported
Stages	Domain area	Supervised	Unsupervised	Metrics reported
Pre-Harvest	Crop type	CNN, LSTM, GRU, RF, SVM, CPPLS	PCA	Accuracy: 64%–97%
	Soil	ANN, SVM, GBC, MLP, BN, LGR, DT, RF, NFL	KNN	Accuracy: 87%–98%
	Diseases	CNN, MDC, GA, SVM, ANN, ANFIS, CRB, RF	KNN, KM, PCA	Accuracy: 85%–99%
	Pest	LGR, SVM	PCA	Accuracy: 79%
Harvest	Ripeness	CNN, ANN, FCM, NB, DT, DAC, SIMCA, PLSDA, ProCRC	KNN, PCA	Accuracy: 94%–100% RMSE: 0.0308–1.1170
Harvest	Age bunch	CNN, SVM	-	Accuracy: 99%
Post-Harvest	Quality grading	CNN, ANN, SVR, RBF, SVM, RF	-	Accuracy: 94%–100% RMSE: 0.0218–4.910
	Fruit recognition	CNN, ANN, SVM, DT, NB, AODE, BN, LGR	KNN	Accuracy: 84%–99%
	Crop yield	ANN, RF, LSTM, NLSVR	-	Accuracy: 75% RMSE:34.8–46.69 MSE: 0.00397
Processing	Process parameters	ANN, GA	-	MSE: 57.85–721.06
Retail	Production	OLSR, MLReg	-	Accuracy: 60%

Top	Technique	Learning Task	No. papers where appear
1	SVM	Classification, and imagen pre-processing	22
2	CNN	Object Classification	18
3	KNN	Clustering, and imagen pre-processing	9
4	ANN	Regression and classification	8
5	RF	Regression	6

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[1] *Corresponding author. Universidad de Piura/ Piura, Perú. E-mail: ealmeydaa@gmail.com