Computational Intelligence in Remote Sensing: An Editorial

Graña, Manuel; Woźniak, Michał; Ríos, Sebastián A.; Lope, Javier de

doi:10.3390/s20030633

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…Morphological classification and phenotypic prediction of tobacco plants were conducted using eight machine learning algorithms: neural networks, gradient boosting, random forest, naive Bayes, K-nearest neighbors, support vector machines, logistic regression, and stochastic gradient descent (Figures 5 and 6). Our findings are in agreement with previous research, indicating that neural networks and other machine learning algorithms can effectively classify and predict plant morphological traits and yield parameters based on hyperspectral data [15,31,42,67,68].…”

Section: Morphological and Phenotypic Classification And Prediction U...supporting

confidence: 93%

“…In recent years, the significance of artificial intelligence (AI) and classification algorithms in the analysis of remote sensing data has become increasingly evident [7,30,31]. Advanced machine algorithms and artificial intelligence facilitate the efficient processing of large and complex datasets, allowing researchers to extract meaningful information from the vast amounts of spectral data generated by reflectance hyperspectral data [7,32,33].…”

Section: Introductionmentioning

confidence: 99%

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Non−Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy

Falcioni

Santos

Crusiol

et al. 2023

Plants

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Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV−VIS−NIR−SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA3) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA3 concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R2CV values ranging from 0.81 to 0.87 and RPDP values exceeding 2.09 for all parameters. Based on Pearson’s coefficient XYZ interpolations and HVI algorithms, the NIR−SWIR band combination proved the most effective for predicting height and leaf area, while VIS−NIR was optimal for optimal energy yield, and VIS−VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s−PLS were most significant for SWIR1 and SWIR2, while i−PLS showed a more uniform distribution in VIS−NIR−SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field.

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Section: Morphological and Phenotypic Classification And Prediction U...supporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Non−Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy

Falcioni

Santos

Crusiol

et al. 2023

Plants

View full text Add to dashboard Cite

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“…With the rapid development of computer hardware and software, computational intelligence (CI) has advanced rapidly in recent years, leveraging large-scale computation to mine domain knowledge and develop various new methods for solving complex problems. CI primarily includes 5 methods: fuzzy logic, probabilistic approaches, swarm intelligence, neural networks, and evolutionary computation [ 21 ]. In agriculture, CI has been widely applied in predicting and detecting crop diseases, analyzing soil and climate data, and optimizing crop yields.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence

Wu,

Wen,

et al. 2024

Plant Phenomics

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The 3-dimensional (3D) modeling of crop canopies is fundamental for studying functional-structural plant models. Existing studies often fail to capture the structural characteristics of crop canopies, such as organ overlapping and resource competition. To address this issue, we propose a 3D maize modeling method based on computational intelligence. An initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture. The subsequent model considers the 3D phytomers of maize as intelligent agents. The aim is to maximize the ratio of sunlit leaf area, and by iteratively modifying the azimuth angle of the 3D phytomers, a 3D maize canopy model that maximizes light resource interception can be constructed. Additionally, the method incorporates a reflective approach to optimize the canopy and utilizes a mesh deformation technique for detecting and responding to leaf collisions within the canopy. Six canopy models of 2 varieties plus 3 planting densities was constructed for validation. The average R 2 of the difference in azimuth angle between adjacent leaves is 0.71, with a canopy coverage error range of 7% to 17%. Another 3D maize canopy model constructed using 12 distinct density gradients demonstrates the proportion of leaves perpendicular to the row direction increases along with the density. The proportion of these leaves steadily increased after 9 × 10 4 plants ha −1 . This study presents a 3D modeling method for the maize canopy. It is a beneficial exploration of swarm intelligence on crops and generates a new way for exploring efficient resources utilization of crop canopies.

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“…One of the principal roadblocks is the sheer volume and complexity of the obtained data. This wealth of information, although invaluable, requires advanced computational algorithms for accurate interpretation and application [11]. The intertwining of artificial intelligence, machine learning, and hyperspectral data holds promise for navigating this vast data terrain, offering nuanced insights previously out of reach [1,12].…”

Section: Introductionmentioning

confidence: 99%

Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy

Falcioni,

Gonçalves,

de Oliveira

et al. 2023

Plants

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Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms—such as PLS, VIP, iPLS-VIP, GA, RF, and CARS—the most responsive wavelengths were discerned. PLSR models consistently achieved R2 values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation.

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Computational Intelligence in Remote Sensing: An Editorial

Cited by 3 publications

References 11 publications

Non−Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy

Non−Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy

Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence

Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy

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