Spectral imaging in crop monitoring and disease diagnosis: A comprehensive review

CABI Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 26, 2025

Abstract Spectral imaging is a technique that captures and analyzes the spectral information of an object, such as its reflectance, transmittance, or fluorescence. It has been widely used in various fields, remote sensing, food quality assessment. In recent years, also emerged promising tool for crop disease diagnosis, it can provide rapid, non-destructive, accurate detection plant pathogens symptoms. This review aims to concise overview principles, methods, applications, challenges diagnosis. First, we introduce basic sensing concepts types imaging, hyperspectral, multispectral imaging. Second, discuss main steps techniques involved analysis, image acquisition, processing, feature extraction, classification. Third, present some representative examples applications fungal, bacterial, viral, nematode infections. Finally, highlight importance artificial intelligence integration alongside current limitations future directions

Language: Английский

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Multivariate Time Series Models for Soil Nutrient and Yield Prediction in Site Specific Integrated Nutrient Management

Communications in Soil Science and Plant Analysis, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: April 6, 2025

Language: Английский

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Soil Conservation and Information Technologies: A Literature Review

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100935 - 100935

Published: April 1, 2025

Language: Английский

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Point-to-Interval Prediction Method for Key Soil Property Contents Utilizing Multi-Source Spectral Data

Agronomy, Journal Year: 2024, Volume and Issue: 14(11), P. 2678 - 2678

Published: Nov. 14, 2024

Key soil properties play pivotal roles in shaping crop growth and yield outcomes. Accurate point prediction interval of serve as crucial references for making informed decisions regarding fertilizer applications. Traditional testing methods often entail laborious resource-intensive chemical analyses. To address this challenge, study introduced a novel approach leveraging spectral data fusion techniques to forecast key properties. The initial datasets were derived from UV–visible–near-infrared (UV-Vis-NIR) mid-infrared (MIR) data, which underwent preprocessing stages involving smoothing denoising fractional-order derivative[s] (FOD) transform techniques. After extracting the characteristic bands both types three strategies developed, further enhanced using machine learning Among these strategies, outer-product analysis algorithm proved particularly effective improving accuracy. For predictions, metrics such coefficient determination (R2) error demonstrated significant enhancements compared predictions based solely on single-source data. Specifically, R2 values increased by 0.06 0.41, underscoring efficacy combined with partial least squares regression (PLSR). In addition, coverage width criterion establish reliable intervals properties, including organic matter (SOM), total nitrogen (TN), hydrolyzed (HN), available potassium (AK). These developed within framework kernel density estimation (KDE) model, facilitates quantification uncertainty property estimates. phosphorus (AP), preliminary assessment its concentration was also provided. By integrating advanced learning, paves way more agricultural decision sustainable management strategies.

Language: Английский

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Developing novel spectral indices for precise estimation of soil pH and organic carbon with hyperspectral data and machine learning

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(12)

Published: Nov. 26, 2024

Language: Английский

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Aplicaciones de la inteligencia artificial en el monitoreo y conservación ambiental: una revisión exploratoria

REVISTA AMBIENTAL AGUA AIRE Y SUELO, Journal Year: 2024, Volume and Issue: 15(2), P. 48 - 68

Published: Sept. 27, 2024

Este artículo explora el uso de la inteligencia artificial en vigilancia y preservación del agua, aire suelo. El análisis examinó estudios revisador por pares publicados entre 2020 2024, con un enfoque específico contribución a mejora las técnicas gestión ambiental. procedimiento selección se limitó treinta tres investigaciones pertinentes, que clasificaron dominios principales, calidad suelo, contaminación monitoreo ambiental, aplicaciones IA. Las artificial, incluido aprendizaje automático profundo, muestran gran potencial para mejorar precisión predicciones optimizar asignación recursos varios campos ambientales. Los usos principales esta tecnología son evaluar predecir los niveles gestionar hídricos. La integración IA métodos convencionales eficacia Sin embargo, existen dificultades continuas garantizar confiabilidad datos, capacidad modelos aplicarse diferentes escenarios exitosa estos diversas situaciones. ha demostrado su generar cambios significativos conservación medio ambiente. posteriores deberían dar prioridad ampliación conjuntos incorporación tecnologías desarrollo resolución consecuencias socioeconómicas, fin aprovechar al máximo abordar cuestiones ambientales complejas.

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