Machine learning applied to canopy hyperspectral image data to support biological control of soil-borne fungal diseases in baby leaf vegetables

Pane, Catello; Manganiello, Gelsomina; Nicastro, Nicola; Ortenzi, Luciano; Pallottino, Federico; Cardi, Teodoro; Costa, Corrado

doi:10.1016/j.biocontrol.2021.104784

Cited by 11 publications

(7 citation statements)

References 44 publications

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“…The LV models with the highest mean performance value were the most robust, according to Swierenga et al [ 29 ]. Moreover, for PLSDA, the variable importance in projection (VIP) scores were calculated [ 30 ]; for SNN, the variable impact, analogue to VIP, was calculated following the procedure described by Pane et al [ 31 ]. Both indicators were used to estimate the importance of each variable in predicting the correct identity according to each model.…”

Section: Methodsmentioning

confidence: 99%

Influence of the Drying Process on the Volatile Profile of Different Capsicum Species

Taiti,

Comparini,

Moscovini

et al. 2024

Plants

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Chili is a globally significant spice used fresh or dried for culinary, condiment, and medicinal purposes. Growing concerns about food safety have increased the demand for high-quality products and non-invasive tools for quality control like origin tracing and safety assurance. Volatile analysis offers a rapid, comprehensive, and safe method for characterizing various food products. Thus, this study aims to assess the impact of the drying process on the aromatic composition of various Capsicum species and to identify key compounds driving the aromatic complexity of each genetic makeup. To accomplish these objectives, the aroma was examined in fruits collected from 19 different pepper accessions (Capsicum sp.) belonging to four species: one ancestral (C. chacoense) and three domesticated pepper species (C. annuum, C. baccatum and C. chinense). Fresh and dried samples were analyzed using a headspace PTR-TOF-MS platform. Our findings reveal significant changes in the composition and concentration of volatile organic compounds (VOCs) from fresh to dried Capsicum. Notably, chili peppers of the species C. chinense consistently exhibited higher emission intensity and a more complex aroma compared to other species (both fresh and dried). Overall, the data clearly demonstrate that the drying process generally leads to a reduction in the intensity and complexity of the aromatic compounds emitted. Specifically, fresh peppers showed higher volatile organic compounds content compared to dried ones, except for the two sweet peppers studied, which exhibited the opposite behavior. Our analysis underscores the variability in the effect of drying on volatile compound composition among different pepper species and even among different cultivars, highlighting key compounds that could facilitate species classification in dried powder. This research serves as a preliminary guide for promoting the utilization of various pepper species and cultivars as powder, enhancing product valorization.

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Section: Methodsmentioning

confidence: 99%

Influence of the Drying Process on the Volatile Profile of Different Capsicum Species

Taiti,

Comparini,

Moscovini

et al. 2024

Plants

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“…The biocontrol activity of the two Trichoderma isolates was investigated in vitro against Fol through the dual culture approach following the same procedure reported in [19]. The inoculum consisted of a 0.5 cm diameter mycelial plug excised from the edges of a 7-day-old actively growing fungal culture of both the pathogen and Trichoderma strains.…”

Section: Dual Culture Assaymentioning

confidence: 99%

“…Recently, high-resolution hyperspectral imaging data were used to identify high-performing synthetic vegetational indices and to develop ML models that can trace and predict the biocontrol efficacy of a large collection of Trichoderma spp. against the soil-borne diseases caused by Sclerotinia sclerotiorum and Sclerotium rolfsii on baby lettuce [19]. However, in comparison to artificial intelligence studies based on optoelectronic data for disease detection, there are very few studies aimed at evaluating and rating biological control effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Trichoderma Biocontrol Performances against Baby-Lettuce Fusarium Wilt Surveyed by Hyperspectral Imaging-Based Machine Learning and Infrared Thermography

Manganiello,

Nicastro,

Ortenzi

et al. 2024

Agriculture

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Fusarium oxysporum f. sp. lactucae is one of the most aggressive baby-lettuce soilborne pathogens. The application of Trichoderma spp. as biocontrol agents can minimize fungicide treatments and their effective targeted use can be enhanced by support of digital technologies. In this work, two Trichoderma harzianum strains achieved 40–50% inhibition of pathogen radial growth in vitro. Their effectiveness in vivo was surveyed by assessing disease incidence and severity and acquiring hyperspectral and thermal features of the canopies being treated. Infected plants showed a reduced light absorption in the green and near-red regions over time, reflecting the disease progression. In contrast, Trichoderma-treated plant reflectance signatures, even in the presence of the pathogen, converged towards the healthy control values. Seventeen vegetation indices were selected to follow disease progression. The thermographic data were informative in the middle–late stages of disease (15 days post-infection) when symptoms were already visible. A machine-learning model based on hyperspectral data enabled the early detection of the wilting starting from 6 days post-infection, and three different spectral regions sensitive to baby-lettuce wilting (470–490 nm, 740–750 nm, and 920–940 nm) were identified. The obtained results pioneer an effective AI-based decision support system (DSS) for crop monitoring and biocontrol-based management.

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“…As a result, it allows us to face old problems in a new and effective perspective [ 18 , 19 , 20 ]. In particular, some authors have used AI algorithms for the extraction of synthetic indices [ 21 , 22 ] and to perform early detection on biotic and abiotic stress in rocket leaf [ 23 ]. Moreover, AI can push hyperspectral analysis towards real-time applications when combined with open source and commercial spectrometers [ 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time AI-Assisted Push-Broom Hyperspectral System for Precision Agriculture

Neri,

Caponi,

Bonacci

et al. 2024

Sensors

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In the ever-evolving landscape of modern agriculture, the integration of advanced technologies has become indispensable for optimizing crop management and ensuring sustainable food production. This paper presents the development and implementation of a real-time AI-assisted push-broom hyperspectral system for plant identification. The push-broom hyperspectral technique, coupled with artificial intelligence, offers unprecedented detail and accuracy in crop monitoring. This paper details the design and construction of the spectrometer, including optical assembly and system integration. The real-time acquisition and classification system, utilizing an embedded computing solution, is also described. The calibration and resolution analysis demonstrates the accuracy of the system in capturing spectral data. As a test, the system was applied to the classification of plant leaves. The AI algorithm based on neural networks allows for the continuous analysis of hyperspectral data relative up to 720 ground positions at 50 fps.

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Machine learning applied to canopy hyperspectral image data to support biological control of soil-borne fungal diseases in baby leaf vegetables

Cited by 11 publications

References 44 publications

Influence of the Drying Process on the Volatile Profile of Different Capsicum Species

Influence of the Drying Process on the Volatile Profile of Different Capsicum Species

Trichoderma Biocontrol Performances against Baby-Lettuce Fusarium Wilt Surveyed by Hyperspectral Imaging-Based Machine Learning and Infrared Thermography

Real-Time AI-Assisted Push-Broom Hyperspectral System for Precision Agriculture

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