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Genangeli A, Avola G, Bindi M, Cantini C, Cellini F, Riggi E, Gioli B. A Novel Correction Methodology to Improve the Performance of a Low-Cost Hyperspectral Portable Snapshot Camera. SENSORS (BASEL, SWITZERLAND) 2023; 23:9685. [PMID: 38139530 PMCID: PMC10748185 DOI: 10.3390/s23249685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 11/30/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023]
Abstract
The development of spectral sensors (SSs) capable of retrieving spectral information have opened new opportunities to improve several environmental and agricultural practices, e.g., crop breeding, plant phenotyping, land use monitoring, and crop classification. The SSs are classified as multispectral and hyperspectral (HS) based on the number of the spectral bands resolved and sampled during data acquisition. Large-scale applications of the HS remain limited due to the cost of this type of technology and the technical difficulties in hyperspectral data processing. Low-cost portable hyperspectral cameras (PHCs) have been progressively developed; however, critical aspects associated with data acquisition and processing, such as the presence of spectral discontinuities, signal jumps, and a high level of background noise, were reported. The aim of this work was to analyze and improve the hyperspectral output of a PHC Senop HSC-2 device by developing a general use methodology. Several signal gaps were identified as falls and jumps across the spectral signatures near 513, 650, and 930 nm, while the dark current signal magnitude and variability associated with instrumental noise showed an increasing trend over time. A data correction pipeline was successfully developed and tested, leading to 99% and 74% reductions in radiance signal jumps identified at 650 and 830 nm, respectively, while the impact of noise on the acquired signal was assessed to be in the range of 10% to 15%. The developed methodology can be effectively applied to other low-cost hyperspectral cameras.
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Affiliation(s)
- Andrea Genangeli
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, P. le delle Cascine 18, 50144 Florence, Italy
| | - Giovanni Avola
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Gaifami 18, 95126 Catania, Italy
| | - Marco Bindi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, P. le delle Cascine 18, 50144 Florence, Italy
| | - Claudio Cantini
- Institute of Bioeconomy (IBE), National Research Council (CNR), Azienda Agraria “Santa Paolina”, S.P. n° 152 Aurelia Vecchia Km 43,300, 58022 Follonica, Italy
| | - Francesco Cellini
- Centro Ricerche Metapontum Agrobios-Agenzia Lucana di Sviluppo e di Innovazione in Agricoltura (ALSIA), S.S. Jonica 106, Km 448,2, 75010 Metaponto di Bernalda, Italy
| | - Ezio Riggi
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via Gaifami 18, 95126 Catania, Italy
| | - Beniamino Gioli
- Institute of Bioeconomy (IBE), National Research Council (CNR), Via G. Caproni 8, 50145 Firenze, Italy
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Reis Pereira M, dos Santos FN, Tavares F, Cunha M. Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling. FRONTIERS IN PLANT SCIENCE 2023; 14:1242201. [PMID: 37662158 PMCID: PMC10468592 DOI: 10.3389/fpls.2023.1242201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 07/27/2023] [Indexed: 09/05/2023]
Abstract
Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine - SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants' defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.
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Affiliation(s)
- Mafalda Reis Pereira
- Faculdade de Ciências da Universidade do Porto (FCUP), Rua do Campo Alegre, Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto, Portugal
| | - Filipe Neves dos Santos
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto, Portugal
| | - Fernando Tavares
- Faculdade de Ciências da Universidade do Porto (FCUP), Rua do Campo Alegre, Porto, Portugal
- CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, Vairão, Portugal
- BIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vairão, Vairão, Portugal
| | - Mário Cunha
- Faculdade de Ciências da Universidade do Porto (FCUP), Rua do Campo Alegre, Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto, Portugal
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Tomaszewski M, Nalepa J, Moliszewska E, Ruszczak B, Smykała K. Early detection of Solanum lycopersicum diseases from temporally-aggregated hyperspectral measurements using machine learning. Sci Rep 2023; 13:7671. [PMID: 37169807 PMCID: PMC10175501 DOI: 10.1038/s41598-023-34079-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 04/24/2023] [Indexed: 05/13/2023] Open
Abstract
Some plant diseases can significantly reduce harvest, but their early detection in cultivation may prevent those consequential losses. Conventional methods of diagnosing plant diseases are based on visual observation of crops, but the symptoms of various diseases may be similar. It increases the difficulty of this task even for an experienced farmer and requires detailed examination based on invasive methods conducted in laboratory settings by qualified personnel. Therefore, modern agronomy requires the development of non-destructive crop diagnosis methods to accelerate the process of detecting plant infections with various pathogens. This research pathway is followed in this paper, and an approach for classifying selected Solanum lycopersicum diseases (anthracnose, bacterial speck, early blight, late blight and septoria leaf) from hyperspectral data captured on consecutive days post inoculation (DPI) is presented. The objective of that approach was to develop a technique for detecting infection in less than seven days after inoculation. The dataset used in this study included hyperspectral measurements of plants of two cultivars of S. lycopersicum: Benito and Polfast, which were infected with five different pathogens. Hyperspectral reflectance measurements were performed using a high-spectral-resolution field spectroradiometer (350-2500 nm range) and they were acquired for 63 days after inoculation, with particular emphasis put on the first 17 day-by-day measurements. Due to a significant data imbalance and low representation of measurements on some days, the collective datasets were elaborated by combining measurements from several days. The experimental results showed that machine learning techniques can offer accurate classification, and they indicated the practical utility of our approaches.
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Affiliation(s)
- Michał Tomaszewski
- Faculty of Electrical Engineering, Automatic Control and Informatics, Department of Computer Science, Opole University of Technology, Prószkowska 76 Street, 45-758, Opole, Poland.
| | - Jakub Nalepa
- Department of Algorithmics and Software, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland
- KP Labs, Konarskiego 18C, 44-100, Gliwice, Poland
| | - Ewa Moliszewska
- Faculty of Natural Sciences and Technology, Institute of Environmental Engineering and Biotechnology, University of Opole, Ks. B. Kominka 6a Street, 45-032, Opole, Poland
| | - Bogdan Ruszczak
- Faculty of Electrical Engineering, Automatic Control and Informatics, Department of Computer Science, Opole University of Technology, Prószkowska 76 Street, 45-758, Opole, Poland
- KP Labs, Konarskiego 18C, 44-100, Gliwice, Poland
| | - Krzysztof Smykała
- Faculty of Electrical Engineering, Automatic Control and Informatics, Department of Computer Science, Opole University of Technology, Prószkowska 76 Street, 45-758, Opole, Poland
- QZ Solutions Sp. z o.o., Ozimska 72A Street, 45-310, Opole, Poland
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Ye M, Feng H, Hu J, Yu Q, Liu S. Managingtomato bacterial wilt by suppressing Ralstonia solanacearum population in soil and enhancing host resistance through fungus-derived furoic acid compound. FRONTIERS IN PLANT SCIENCE 2022; 13:1064797. [PMID: 36452092 PMCID: PMC9703000 DOI: 10.3389/fpls.2022.1064797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 10/21/2022] [Indexed: 06/17/2023]
Abstract
Synthetic chemical pesticides are primarily used to manage plant pests and diseases, but their widespread and unregulated use has resulted in major health and environmental hazards. Using biocontrol microbes and their bioactive compounds is a safe and sustainable approach in plant protection. In this study, a furoic acid (FA) compound having strong antibacterial activity against soil-borne phytopathogenic bacterium Ralstonia solanacearum [causal agent of bacterial wilt (BW) disease] was isolated from Aspergillus niger and identified as 5-(hydroxymethyl)-2-furoic acid compound through spectroscopic analyses (liquid chromatography-mass spectrometry (MS), electron ionization MS, and NMR). The SEM study of bacterial cells indicated the severe morphological destructions by the FA compound. The FA was further evaluated to check its potential in enhancing host resistance and managing tomato BW disease in a greenhouse experiment and field tests. The results showed that FA significantly enhanced the expression of resistance-related genes (PAL, LOX, PR1, and PR2) in tomato and caused a significant reduction (11.2 log10 colony-forming units/g) of the R. solanacearum population in soil, resulting in the reduction of bacterial wilt disease severity on tomato plants and increase in plant length (58 ± 2.7 cm), plant biomass (28 ± 1.7 g), and root length (13 ± 1.2 cm). The findings of this study suggested that the fungus-derived FA compound can be a potential natural compound of biological source for the soil-borne BW disease in tomato.
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