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| 005 | 20250930132714.0 | ||
| 024 | 7 | _ | |a 10.1016/j.atech.2025.101364 |2 doi |
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| 082 | _ | _ | |a 630 |
| 100 | 1 | _ | |a Agarwal, Avinash |0 P:(DE-Juel1)207602 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Synergistic 3D, multispectral, and thermal image analysis via supervised machine learning for improved detection of root rot symptoms in hydroponically grown flat-leaf parsley |
| 260 | _ | _ | |c 2025 |b Elsevier B.V. |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Root rot in hydroponically grown leafy vegetables is difficult to detect via conventional manual and machine vision-based approaches as symptoms of infection are not clearly visible on the canopy at earlier stages of infection. Hence, the present study investigates the potential of using machine learning for assessing canopy information obtained from multiple imaging platforms synergistically to improve root rot detection. Herein, flat-leaf parsley seedlings were grown in an experimental hydroponic vertical farm and inoculated with Pythium irregulare and Phytophthora nicotianae. Subsequently, the seedlings were imaged via 3D, multispectral, and thermal sensors at various stages of growth to obtain twenty-six image-based plant features. Following a preliminary screening of redundant features via regression analysis, data for seventeen image features associated with morphometric, spectral, and thermal attributes was co-analyzed using supervised machine learning by Support Vector Machines (SVM). Models using all eleven spectral features provided 98 % accuracy compared to 90 % for all five morphometric features and 94 % for canopy temperature alone. Inclusion of temporal data improved model performance by ca. 0.5 %, 1.5 %, and 8 % for spectral, thermal, and morphometric datasets, respectively. Exhaustive feature selection using different SVM kernels and maximum feature thresholds showed that combining features across the three imaging platforms along with temporal information enabled better identification of infected samples (>99 %) with as low as three features in comparison to using considerably more features from individual imaging systems. Hence, fusion of data from multiple imaging systems and using it with temporal information enabled better real-time high-throughput monitoring of root rot. |
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| 700 | 1 | _ | |a de Jesus Colwell, Filipe |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Bello Rodriguez, Julian |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Sommer, Sarah |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Barman, Monica |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Correa Galvis, Viviana Andrea |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Hill, Tom R. |0 P:(DE-HGF)0 |b 6 |
| 700 | 1 | _ | |a Boonham, Neil |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Prashar, Ankush |0 P:(DE-HGF)0 |b 8 |
| 773 | _ | _ | |a 10.1016/j.atech.2025.101364 |g Vol. 12, p. 101364 - |0 PERI:(DE-600)3094269-X |p 101364 |t Smart agricultural technology |v 12 |y 2025 |x 2772-3755 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1046239/files/1-s2.0-S2772375525005957-main.pdf |y OpenAccess |
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