Early detection of black Sigatoka in banana leaves using hyperspectral images

Fajardo, Jorge Ugarte; Andrade, Oswaldo Bayona; Bonilla, R C Carmen; Cevallos-Cevallos, Juan Manuel; Maridueña-Zavala, María Gabriela; Donoso, Daniel Ochoa; Villardón, José Luis Vicente

doi:10.1002/aps3.11383

Cited by 19 publications

(4 citation statements)

References 37 publications

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“…Many of these studies have shown the efficacy of supervised methods such as partial least squares discriminant analysis (PLS-DA) [52,[84][85][86] and machine learning algorithms such as Random Forest [87][88][89][90] in identifying more significant environmental variables and predicting susceptibility levels with a high degree of accuracy.…”

Section: Discussionmentioning

confidence: 99%

Mapping of the Susceptibility of Colombian Musaceae Lands to a Deadly Disease: Fusarium oxysporum f. sp. cubense Tropical Race 4

Rodríguez-Yzquierdo,

Olivares,

Silva-Escobar

et al. 2023

Horticulturae

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Fusarium oxysporum f. sp. cubense Tropical Race 4 (Foc TR4) (Syn. Fusarium odoratissimum) is a devastating soil-borne pathogen that infects the roots of banana plants and causes Fusarium wilt disease. Colombia is one of the world’s leading banana producers; therefore, new uncontrolled outbreaks could have serious consequences. Despite this, little is known about the susceptibility of Musaceae lands in Colombia to Foc TR4. This work presents a pioneering study on the susceptibility of Colombian soils to Foc TR4. For this, a study was carried out to characterize climatic, edaphic, and density factors of Musaceae productive systems at the Colombian level, articulated with expert criteria to map and define areas with different levels of susceptibility to Foc R4T. These criteria are typically selected based on the existing scientific literature, consultation with domain experts, and consideration of established methods for assessing soil health and disease susceptibility in Musaceae plantations. By joining the analyzed susceptibility factors, differentiated areas were generated that imply a greater or lesser predisposition to the disease. Subsequently, a validation of the classification was made with Random Forest. The results indicate that at the level of climate, soil, and farm density as a fit factor, practically 50% of the cultivated territory of Musaceae are areas high and very highly susceptible to the pathogen (572,000 km2). The results showed that from the total Musaceae area, Antioquia, Bolívar, Chocó, and Santander turned out to be the departments with the highest proportion of very high susceptibility class of the production farms. The analysis of Random Forest classification performance shows that the model has a relatively low out-of-bag (OOB) error rate (0.023). The study on the susceptibility is highly novel and original, as it represents the first systematic investigation of Foc TR4 susceptibility in Colombian soils. This paper provides important insights into the susceptibility of Musaceae lands in Colombia to Foc TR4. The study highlights the need for ongoing monitoring, containment, and control measures to prevent the spread of this deadly pathogen and protect Colombia’s important banana industry.

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Section: Discussionmentioning

confidence: 99%

Mapping of the Susceptibility of Colombian Musaceae Lands to a Deadly Disease: Fusarium oxysporum f. sp. cubense Tropical Race 4

Rodríguez-Yzquierdo,

Olivares,

Silva-Escobar

et al. 2023

Horticulturae

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“…Near-infrared spectroscopy (NIRs) is an emerging method that is both rapid and nondestructive [8]. It is used for qualitative and quantitative analysis of the chemical composition of fruits such as apples [9], bananas [10], peaches [11], kiwifruits [12], and pears [13]. Tis method is becoming increasingly popular as a solution to the limitations and challenges of traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Application of Near-Infrared Spectroscopy to Rapidly Classify the Chinese Quince Fruits from Different Habitats

Diao,

Tang,

Huang

et al. 2024

Journal of Food Quality

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The ecological habitats of Chinese quince (Chaenomeles speciosa Nakai) fruits affect their phenotype. Currently, limited or no rapid method exists for classifying Chinese quince fruit from different ecosystems. This study developed a partial least squares discriminant analysis (PLS-DA) classification model to effectively and nondestructively classify 663 Chinese quince fruit samples from six environments in 2020. PLS-DA models and other variable selection approaches were used in this study. The near-infrared spectroscopy (NIRs) absorption spectra of raw Chinese quince fruit samples from six habitats showed a similar shape. The spectra of each environment showed little variance. The raw fruit spectra varied significantly among habitat categories after the first derivative preprocessing phase. The uninformative variable elimination (UVE) variable selection approach had greater calibration and validation set specificity of 0.93 and 0.98. This study found the best classification specificity using the UVE variable selection approach compared to other methods including the PLS-DA model without variable selection. The UVE approach improved Yunnan habitat categorization specificity from 86% to 88% when integrated with PLS-DA. Additionally, the validation set for quinces originating from Anhui, Chongqing, Hubei, Shandong, and Zhejiang achieved an ideal classification score of 100%. The findings of the study indicated that PLS-DA can serve as an alternative approach for classifying the habitats of Chinese quince fruits. When used in conjunction with other methods, this technique can assist researchers, scientists, and industry professionals in identifying the main factors responsible for significant variations in the habitats, composition, and quality of Chinese quince fruits.

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“…When the conveyor belt moves horizontally, the hyperspectral camera can collect the hyperspectral image of the measured object (Xu et al., 2020). This method has good applicability to the measured objects on the collection plane, such as meat (Zhang et al., 2022) and leaves (Chen et al., 2020; Ugarte Fajardo et al., 2020). However, there are two difficulties in the detection of surface defects of spherical fruit (usually considered Lambertian objects, i.e., a completely diffuse reflection object).…”

Section: Introductionmentioning

confidence: 99%

Full‐surface defect detection of navel orange based on hyperspectral online sorting technology

Shang

Xue

Zhang

et al. 2023

Journal of Food Science

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The whole-surface hyperspectral image acquisition of navel orange is particularly important for surface defect detection and quality classification. Because the light intensity at the edge of the navel orange is lower than that in the middle, the defects on the surface of the navel orange cannot be effectively identified. In this paper, a hyperspectral online sorting device for the whole-surface defects of navel orange is proposed. First of all, the image data of navel orange is collected by online detection sorting equipment and the spectral image of the characteristic wave peak of 1655.72 nm was extracted. Then, the light intensity at the edge of the navel orange is enhanced by nonuniformity correction based on quadratic curve fitting, and the light intensity correction of the navel orange is realized. Finally, the corrected image is segmented by the threshold to obtain surface defects, and the number of surface defect pixels is improved effectively compared with that before light intensity correction. Ultimately, the online sorting test is carried out, and the detection accuracy is 100%. This indicates that this method effectively improves the sensitivity of defect detection. At the same time, the dimensionality reduction of hyperspectral data is also carried out, which is conducive to improving the efficiency of online detection.

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Early detection of black Sigatoka in banana leaves using hyperspectral images

Cited by 19 publications

References 37 publications

Mapping of the Susceptibility of Colombian Musaceae Lands to a Deadly Disease: Fusarium oxysporum f. sp. cubense Tropical Race 4

Mapping of the Susceptibility of Colombian Musaceae Lands to a Deadly Disease: Fusarium oxysporum f. sp. cubense Tropical Race 4

Application of Near-Infrared Spectroscopy to Rapidly Classify the Chinese Quince Fruits from Different Habitats

Full‐surface defect detection of navel orange based on hyperspectral online sorting technology

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