Cited by AI for Smart Agriculture – A Deep Learning Based Turmeric Leaf Disease Detection

Data Fusion and Dimensionality Reduction for Pest Management in Pitahaya Cultivation DOI

Wilson Chango, Monica Mazon Fierro, Juan Erazo

et al.

Computation, Journal Year: 2025, Volume and Issue: 13(6), P. 137 - 137

Published: June 3, 2025

This study addresses the critical need for effective data fusion strategies in pest prediction pitahaya (dragon fruit) cultivation Ecuadorian Amazon, where heterogeneous sources—such as environmental sensors and chlorophyll measurements—offer complementary but fragmented insights. Current agricultural monitoring systems often fail to integrate these streams, limiting early detection accuracy. To overcome this, we compared late approaches using comprehensive experiments. Multidimensionality is a central challenge: datasets span temporal (hourly sensor readings), spatial (plot-level samples), spectral (chlorophyll reflectance) dimensions. We applied dimensionality reduction techniques—PCA, KPCA (linear, polynomial, RBF), t-SNE, UMAP—to preserve relevant structure enhance interpretability. Evaluation metrics included proportion of information retained (score) cluster separability (silhouette score). Our results demonstrate that yields superior integrated representations, with PCA KPCA-linear achieving highest scores (0.96 vs. 0.94), KPCA-poly best definition (silhouette: 0.32 0.31). Statistical validation Friedman test (χ2 = 12.00, p 0.02) Nemenyi post hoc comparisons (p < 0.05) confirmed significant performance differences. KPCA-RBF performed poorly (score: 0.83; silhouette: 0.05), although t-SNE UMAP offered visual insights, they underperformed clustering 0.12). These findings make three key contributions. First, better captures cross-domain interactions before reduction, improving robustness. Second, offers an non-linear mapping suitable tropical agroecosystem complexity. Third, our framework, when deployed Joya de los Sachas, improved accuracy by 12.60% over manual inspection, leading more targeted pesticide use. contributes precision agriculture providing low-cost, scalable smallholder farmers. Future work will explore hybrid pipelines sensor-agnostic models extend generalizability.

Language: Английский

Citations

Advanced sound-based pest detection in agriculture using deep learning and adaptive optimization DOI

Sahana Lokesh R,

K. SailajaKumar,

R. S. Soundariya

et al.

International Journal of Information Technology, Journal Year: 2025, Volume and Issue: unknown

Published: June 4, 2025

Language: Английский

Citations

Role of Big Data Analytics in Intelligent Agriculture DOI

M. Abinaya,

G. Vadivu,

A. Prasanth

et al.

Studies in computational intelligence, Journal Year: 2024, Volume and Issue: unknown, P. 19 - 48

Published: Jan. 1, 2024

Language: Английский

Citations

AI for Smart Agriculture – A Deep Learning Based Turmeric Leaf Disease Detection DOI

R. Selvaraj,

M. S. Geetha Devasena,

T. Satheesh

et al.

2022 7th International Conference on Communication and Electronics Systems (ICCES), Journal Year: 2024, Volume and Issue: unknown, P. 1491 - 1495

Published: Dec. 16, 2024

Language: Английский

Citations