{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:22:04Z","timestamp":1760059324592,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>This study addresses the critical need for effective data fusion strategies in pest prediction for pitahaya (dragon fruit) cultivation in the Ecuadorian Amazon, where heterogeneous data sources\u2014such as environmental sensors and chlorophyll measurements\u2014offer complementary but fragmented insights. Current agricultural monitoring systems often fail to integrate these data streams, limiting early pest detection accuracy. To overcome this, we compared early and late fusion approaches using comprehensive experiments. Multidimensionality is a central challenge: the datasets span temporal (hourly sensor readings), spatial (plot-level chlorophyll samples), and spectral (chlorophyll reflectance) dimensions. We applied dimensionality reduction techniques\u2014PCA, KPCA (linear, polynomial, RBF), t-SNE, and UMAP\u2014to preserve relevant structure and enhance interpretability. Evaluation metrics included the proportion of information retained (score) and cluster separability (silhouette score). Our results demonstrate that early fusion yields superior integrated representations, with PCA and KPCA-linear achieving the highest scores (0.96 vs. 0.94), and KPCA-poly achieving the best cluster definition (silhouette: 0.32 vs. 0.31). Statistical validation using the Friedman test (\u03c72 = 12.00, p = 0.02) and Nemenyi post hoc comparisons (p &lt; 0.05) confirmed significant performance differences. KPCA-RBF performed poorly (score: 0.83; silhouette: 0.05), and although t-SNE and UMAP offered visual insights, they underperformed in clustering (silhouette &lt; 0.12). These findings make three key contributions. First, early fusion better captures cross-domain interactions before dimensionality reduction, improving prediction robustness. Second, KPCA-poly offers an effective non-linear mapping suitable for tropical agroecosystem complexity. Third, our framework, when deployed in Joya de los Sachas, improved pest prediction accuracy by 12.60% over manual inspection, leading to more targeted pesticide use. This contributes to precision agriculture by providing low-cost, scalable strategies for smallholder farmers. Future work will explore hybrid fusion pipelines and sensor-agnostic models to extend generalizability.<\/jats:p>","DOI":"10.3390\/computation13060137","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T06:21:51Z","timestamp":1748931711000},"page":"137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data Fusion and Dimensionality Reduction for Pest Management in Pitahaya Cultivation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3231-0153","authenticated-orcid":false,"given":"Wilson","family":"Chango","sequence":"first","affiliation":[{"name":"Department of Systems and Computation, Pontifical Catholic University of Ecuador, Esmeraldas Campus PUCESE, Esmeraldas 080101, Ecuador"},{"name":"Faculty of Informatics and Electronics, Escuela Superior Polit\u00e9cnica de Chimborazo ESPOCH, Riobamba 060155, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5303-9174","authenticated-orcid":false,"given":"M\u00f3nica","family":"Maz\u00f3n-Fierro","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Chimborazo UNACH, Riobamba 060101, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0152-5645","authenticated-orcid":false,"given":"Juan","family":"Erazo","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Escuela Superior Polit\u00e9cnica de Chimborazo ESPOCH, Riobamba 060155, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8745-2373","authenticated-orcid":false,"given":"Guido","family":"Maz\u00f3n-Fierro","sequence":"additional","affiliation":[{"name":"Faculty of Business Administration, Escuela Superior Polit\u00e9cnica de Chimborazo ESPOCH, Riobamba 060155, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1205-3017","authenticated-orcid":false,"given":"Santiago","family":"Logro\u00f1o","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Electronics, Escuela Superior Polit\u00e9cnica de Chimborazo ESPOCH, Riobamba 060155, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8723-1041","authenticated-orcid":false,"given":"Pedro","family":"Pe\u00f1afiel","sequence":"additional","affiliation":[{"name":"Environmental Engineering, Escuela Superior Polit\u00e9cnica de Chimborazo ESPOCH, Riobamba 060155, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3657-5407","authenticated-orcid":false,"given":"Jaime","family":"Sayago","sequence":"additional","affiliation":[{"name":"Department of Systems and Computation, Pontifical Catholic University of Ecuador, Esmeraldas Campus PUCESE, Esmeraldas 080101, Ecuador"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Di\u00e9guez-Santana, K., Sarduy-Pereira, L.B., Sabl\u00f3n-Coss\u00edo, N., Bautista-Santos, H., S\u00e1nchez-Galv\u00e1n, F., and Ru\u00edz Cede\u00f1o, S.D.M. (2022). Evaluation of the Circular Economy in a Pitahaya Agri-Food Chain. Sustainability, 14.","DOI":"10.3390\/su14052950"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1038\/s41893-024-01387-7","article-title":"Economic Drivers of Deforestation in the Brazilian Legal Amazon","volume":"7","author":"Haddad","year":"2024","journal-title":"Nat. Sustain."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109386","DOI":"10.1016\/j.compag.2024.109386","article-title":"Precision Farming for Sustainability: An Agricultural Intelligence Model","volume":"226","author":"Vinod","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","unstructured":"Armstrong, L. (2020). Decision-support systems for pest monitoring and management. Decision Support Systems for Sustainable Pest Management, Burleigh Dodds Science Publishing."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s00484-019-01831-w","article-title":"Evaluation of forewarning models for mustard aphids in different agro-climatic zones of India","volume":"64","author":"Tharranum","year":"2020","journal-title":"Int. J. Biometeorol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Zhu, L. (2023). A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications. Drones, 7.","DOI":"10.3390\/drones7060398"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1007\/s13762-021-03801-5","article-title":"UAV-Based Remote Sensing in Plant Stress Imaging Using High-Resolution Thermal Sensor for Digital Agriculture Practices: A Meta-Review","volume":"20","author":"Awais","year":"2022","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dai, M., Shen, Y., Li, X., Liu, J., Zhang, S., and Miao, H. (2024). Digital Twin System of Pest Management Driven by Data and Model Fusion. Agriculture, 14.","DOI":"10.3390\/agriculture14071099"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e1551","DOI":"10.1002\/widm.1551","article-title":"Machine Learning for Pest Detection and Infestation Prediction: A Comprehensive Review","volume":"14","author":"Mittal","year":"2024","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ali, M.A., Sharma, A.K., and Dhanaraj, R.K. (2024). Multi-Features and Multi-Deep Learning Networks to Identify, Prevent and Control Pests in Tremendous Farm Fields Combining IoT and Pests Sound Analysis. Res. Sq., preprint.","DOI":"10.21203\/rs.3.rs-4290726\/v1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tinoco-Jaramillo, L., Vargas-Tierras, Y., Habibi, N., Caicedo, C., Chanaluisa, A., Paredes-Arcos, F., Viera, W., Almeida, M., and V\u00e1squez-Castillo, W. (2024). Agroforestry Systems of Cocoa (Theobroma cacao L.) in the Ecuadorian Amazon. Forests, 15.","DOI":"10.3390\/f15010195"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dieguez-Santana, K., Sarduy-Pereira, L., Ruiz-Reyes, E., and Sabl\u00f3n Coss\u00edo, N. (2025). Application of the Circular Economy in Research in the Agri-Food Supply Chain: Bibliometric, Network, and Content Analysis. Sustainability, 17.","DOI":"10.3390\/su17051899"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"137096","DOI":"10.1016\/j.jclepro.2023.137096","article-title":"Industry 4.0 Technologies in Agri-Food Sector and Their Integration in the Global Value Chain: A Review","volume":"408","author":"Karaca","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"78","DOI":"10.9734\/ijpss\/2023\/v35i213948","article-title":"Exploring the Use of Aromatic Compounds in Crop Growth and Protection","volume":"35","author":"Manjunath","year":"2023","journal-title":"Int. J. Plant Soil Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Aljawasim, B.D., Samtani, J.B., and Rahman, M. (2023). New Insights in the Detection and Management of Anthracnose Diseases in Strawberries. Plants, 12.","DOI":"10.3390\/plants12213704"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.inffus.2019.12.001","article-title":"A Survey on Machine Learning for Data Fusion","volume":"57","author":"Meng","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e1458","DOI":"10.1002\/widm.1458","article-title":"A Review on Data Fusion in Multimodal Learning Analytics and Educational Data Mining","volume":"12","author":"Chango","year":"2022","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Stahlschmidt, S.R., Ulfenborg, B., and Synnergren, J. (2022). Multimodal Deep Learning for Biomedical Data Fusion: A Review. Briefings Bioinform., 23.","DOI":"10.1093\/bib\/bbab569"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.inffus.2021.11.006","article-title":"Multi-Sensor Information Fusion Based on Machine Learning for Real Applications in Human Activity Recognition: State-of-the-Art and Research Challenges","volume":"80","author":"Qiu","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.inffus.2022.09.019","article-title":"Current Advances and Future Perspectives of Image Fusion: A Comprehensive Review","volume":"90","author":"Karim","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1145\/3649597","article-title":"Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science","volume":"33","author":"Betz","year":"2024","journal-title":"ACM Trans. Softw. Eng. Methodol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chabalala, K., Boyana, S., Kolisi, L., Thango, B., and Matshaka, L. (2024). Digital Technologies and Channels for Competitive Advantage in SMEs: A Systematic Review. Preprints, 2024100020.","DOI":"10.20944\/preprints202410.0020.v1"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Omia, E., Bae, H., Park, E., Kim, M.S., Baek, I., Kabenge, I., and Cho, B.K. (2023). Remote Sensing in Field Crop Monitoring: A Comprehensive Review of Sensor Systems, Data Analyses and Recent Advances. Remote Sens., 15.","DOI":"10.3390\/rs15020354"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s43014-023-00205-5","article-title":"State-of-the-Art Non-Destructive Approaches for Maturity Index Determination in Fruits and Vegetables: Principles, Applications, and Future Directions","volume":"6","author":"Anjali","year":"2024","journal-title":"Food Prod. Process. Nutr."},{"key":"ref_25","first-page":"1679","article-title":"K-hyperparameter tuning in high-dimensional genomics using joint optimization of deep differential evolutionary algorithm and unsupervised transfer learning from intelligent GenoUMAP embeddings","volume":"17","author":"Gikera","year":"2024","journal-title":"Int. J. Inf. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/978-3-031-41933-1_6","article-title":"Data Preprocessing","volume":"Volume Part F1359","author":"Jansen","year":"2024","journal-title":"Synthesis Lectures on Information Concepts, Retrieval, and Services"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"102536","DOI":"10.1016\/j.inffus.2024.102536","article-title":"Deep learning based multimodal biomedical data fusion: An overview and comparative review","volume":"112","author":"Duan","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e13301","DOI":"10.1111\/1541-4337.13301","article-title":"Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives","volume":"23","author":"Guo","year":"2024","journal-title":"Compr. Rev. Food Sci. Food Saf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111530","DOI":"10.1016\/j.jece.2023.111530","article-title":"Multi-source and multimodal data fusion for improved management of a wastewater treatment plant","volume":"11","author":"Strelet","year":"2023","journal-title":"J. Environ. Chem. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1016\/j.inffus.2022.10.032","article-title":"A survey of identity recognition via data fusion and feature learning","volume":"91","author":"Qin","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hakim, S.B., Adil, M., Acharya, K., and Song, H.H. (2024). Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach. arXiv.","DOI":"10.1007\/978-981-96-3531-3_10"},{"key":"ref_32","first-page":"1","article-title":"Maximizing Forecasting Precision: Empowering Multivariate Time Series Prediction with QPCA-LSTM","volume":"2","author":"Boddu","year":"2024","journal-title":"Comput. Econ."},{"key":"ref_33","first-page":"381","article-title":"Pest and disease management in agricultural production with artificial intelligence: Innovative applications and development trends","volume":"4","author":"Li","year":"2024","journal-title":"Adv. Resour. Res."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/6\/137\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:46:32Z","timestamp":1760031992000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/6\/137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,3]]},"references-count":33,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["computation13060137"],"URL":"https:\/\/doi.org\/10.3390\/computation13060137","relation":{},"ISSN":["2079-3197"],"issn-type":[{"type":"electronic","value":"2079-3197"}],"subject":[],"published":{"date-parts":[[2025,6,3]]}}}