Peer reviewed publications:

2024

43. Sadeh, R, Ben-David, R, Herrmann, I, and Peleg, Z (2024). Spectral-genomic chain-model approach enhance wheat yield components prediction under Mediterranean climate. Physiologia Plantarum 176(4), e14480.

42. Sabag, I, Bi, Y, Sahoo, MM, Herrmann, I, Morota, G, and Peleg, Z (2024). Leveraging genomics and temporal high-throughput phenotyping to enhance association mapping and yield prediction in sesame. The Plant Genome, e20481.

41. Avneri, A., Peleg, Z., Bonfil, DJ., Sadeh, R., Perach, O., Herrmann, I., Abbo, S., Lati, RN. (2024). Optimization of chickpea irrigation in a semi-arid climate based on morpho-physiological parameters. European Journal of Agronomy, 156(127171).

40. Sadeh, R., Avneri, A., Tubul, Y., Lati, RN., Bonfil, DJ., Peleg, Z., Herrmann, I. (2024). Chickpea leaf water potential estimation from ground and VENµS satellite. Precision Agriculture (25), 1658-1683.

39. Alemu, MD., Ben-Zeev, S., Hellwig, T., Barak, V., Shoshani, G., Chen, A., Razzon, S., Herrmann, I., Vorobyova, S., Hübner, S., Saranga, Y. (2024). Genomic dissection of productivity, lodging and morpho-physiological traits in Eragrostis tef under contrasting water availabilities. Plants People Planet, 1-19.

38. Kooistra, L., Berger, K., Brede, B., …. Herrmann, I., …… Verrelst, J. (2024). Remotely sensed optical time series for monitoring vegetation productivity. Biogeosciences, 21(2): 473-511.

2023

37. Adejimi, OE., Sadhasivam, G., Schmilovitch, Z., Shapiro, OH., and Herrmann, I. (2023). Applying hyperspectral transmittance for inter-genera classification of cyanobacterial and algal cultures. Algal Research, 103067.

36. Bacher, H., Montagu, A., Herrmann, I., Walia, H., Schwartz, N., and Peleg, Z. (2023). Stress-induced deeper rooting introgression enhance wheat yield under terminal drought. Journal of Experimental Botany, erad059.

35. Avneri, A., Aharon, S., Brook, A., Atsmon, G., Smirnov, E., Sadeh, R., Abbo, S., Peleg, Z., Herrmann, I., Bonfil, DJ., and Lati, RN. (2023). UAS-based imaging for prediction of chickpea crop biophysical parameters and yield. Computers and Electronics in Agriculture, 205, 107581.

2022

34. Sahoo, MM., Perach, O., Shachter, A., Gonda, I., Porwal, A., Dudai, N., & Herrmann, I. (2022). Spectral estimation of carnosic acid content in in vivo Rosemary plants. Industrial Crops and Products, 187, 115292.

33. Berger, K., Machwitz, M., Kycko, M., Kefauver, S.C., Van Wittenberghe, S., Gerhards, M., Verrelst, J., Atzberger, C., van der Tol, C., Damm, A., Rascher, U., Herrmann, I., …… & Schlerf, M. (2022). Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review. Remote Sensing of Environment, 280, 113198.

32. Mulero, G., Bacher, H., Kleiner, U., Peleg, Z., and Herrmann I. (2022). Spectral Estimation of In Vivo Wheat Chlorophyll a/b Ratio under Contrasting Water Availabilities. Remote Sensing, 14(11), 2585.

31. Arun, PV., Sadeh, R., Avneri, A., Tubul, Y., Camino, C., Buddhiraju, K. M., Porwal, A., Lati, R. N., Zarco-Tejada, P. J., Peleg, Z., & Herrmann, I. (2022). Multimodal Earth observation data fusion: Graph-based approach in shared latent space. Information Fusion, 78, 20-39.

30. Salvoldi, M., Tubul, Y., Karnieli A., and Herrmann, I. (2022). VENµS-Derived NDVI and REIP at Different View Azimuth Angles. Remote Sensing, 14(1), 184.

2021

29. Goldberg, K., Herrmann, I., Hocberg, U., & Rozenstein, O. (2021). Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel. Remote Sensing, 13(17), 3488.

28. Mishra, P., Sadeh, R., Ryckewaert, M., Bino, E., Polder, G., Boer, M., Rutledge D. N., & Herrmann, I. (2021). A generic workflow combining deep learning and chemometrics for processing close-range spectral images to detect drought stress in Arabidopsis thaliana to support digital phenotyping. Chemometrics and Intelligent Laboratory Systems, 216, 104373.

27. Mishra, P., & Herrmann, I. (2021). GAN meets chemometrics: segmenting spectral images with pixel2pixel image translation with conditional generative adversarial networks. Chemometrics and Intelligent Laboratory Systems, 215, 104362. 

26. Mishra, P., Sadeh, R., Bino, E., Polder, G., Boer, M., Rutledge D. N., & Herrmann, I. (2021). Complementary chemometrics and deep learning for semantic segmentation of tall and wide visible and near-infrared spectral images of plants. Computers and Electronics in Agriculture, 186, 106226. 

25. Herrmann, I., & Berger, K. (2021). Remote and Proximal Assessment of Plant Traits. Remote Sensing, 13(10), 1893.

24. Mahajan, G. R., Das, B., Murgaokar, D., Herrmann, I., Berger, K., Sahoo, R. N., Patel, K., Desai, A., Morajkar, S., and Kulkarni, R. M., (2021). Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models. Remote Sensing, 13(4), 641.

23. Mishra, P., Herrmann, I., & Angileri, M. (2021). Improved prediction of potassium and nitrogen in dried bell pepper leaves with visible and near-infrared spectroscopy utilizing wavelength selection techniques. Talanta, 225, 121971.

2020

22. Gold, K. M., Townsend, P. A., Larson, E. R., Herrmann, I., & Gevens, A. J. (2020). Contact reflectance spectroscopy for rapid, accurate, and non-destructive Phytophthora infestans clonal lineage discrimination. Phytopathology, 110(4): 851-862.

21. Gold, K. M., Townsend, P. A., Chlus, A., Herrmann, I., Couture, J. J., & Gevens, A. J. (2020). Hyperspectral measurements enable pre-symptomatic detection and differentiation of contrasting physiological effects of late blight and early blight in potato. Remote Sensing, 12(2): 286.

20. Gold, K. M., Townsend, P. A., Herrmann, I., & Gevens, A. J. (2020). Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning. Plant Science, 110316.

19. Herrmann, I., Bdolach, E., Montekyo, Y., Rachmilevitch, S., Townsend, P. A., & Karnieli, A. (2020). Assessment of maize yield and phenology by drone-mounted superspectral camera. Precision Agriculture, 21(1): 51-76.

2019

18. Shelef, O., Summerfield, L., Lev-Yadun, S., Villamarin-Cortez, S., Sadeh, R., Herrmann, I., & Rachmilevitch, S. (2019). Thermal benefits from white variegation of Silybum marianum leaves. Frontiers in Plant Science, Plant Abiotic Stress, 10(688).

17. Herrmann, I., Vosberg, S., Townsend, P.A., & Conley, S.P. (2019). Spectral data collection by dual field of view system under changing atmospheric conditions – a case study of estimating early season soybean populations. Sensors, 19(3): 457.

16. Arun, P. V., Herrmann, I., Budhiraju, K. M., & Karnieli, A. (2019). Convolutional network architectures for super-resolution/sub-pixel mapping of drone-derived images. Pattern Recognition, 88: 431-446.

2018

15. Herrmann, I., Vosberg, S., Ravindran, P., Singh, A., Chang, H. X., Chilvers, M. I., Conley S. P., & Townsend, P. A. (2018). Leaf and canopy level detection of Fusarium virguliforme (sudden death syndrome) in soybean. Remote Sensing, 10(3): 426. Cover story.

2017

14. Paz-Kagan, T., Caras, T., Herrmann, I.^, Shachak M., & Karnieli, A. (2017). Multiscale Mapping of Species Diversity under Changed Land Use Using Imaging Spectroscopy. Ecological Applications, 27(5): 1466 -1684.

13. Matzrafi, M., Herrmann, I. Nansen, C., Kliper, T., Zait, Y., Ignat, T., Siso, D., Rubin, B., Karnieli A., & Eizenberg, H. (2017). Hyperspectral technologies for assessing seed germination and trifloxysulfuron-methyl response in Amaranthus palmeri (Palmer amaranth). Frontiers in Plant Science, section Agroecology and Land Use Systems, 8(474).

12. Herrmann, I., Berenstein, M., Paz-Kagan, T., Sade, A., & Karnieli, A. (2017). Spectral assessment of two-spotted spider mite damage levels in the leaves of greenhouse-grown pepper and bean. Biosystems Engineering, 157: 72-85.

2016

11. Paz-Kagan, T., Ohana-Levi, N., Herrmann, I., Zaady, E., Henkin, Z., & Karnieli, A. (2016). Grazing intensity effects on soil quality: A spatial analysis of Mediterranean grassland. Catena, 146, 100-110.

10. Ilani T., Herrmann, I., Karnieli, A., & Arye, G. (2016). Characterization of the biosolids composting process by hyperspectral analysis, Waste Management, 48, 106-114.

2015

9.   Timmermans, W., Van der Tol, C., …… Herrmann, I., ……Su B. (2015). An overview of the Regional Experiments For Land-atmosphere EXchanges 2012 (REFLEX) Campaign. Acta Geophysica, 63(6), 1465-1484.

2014

8.   Nguy-Robertson, A., Peng, Y., Gitelson, A., Arkebauer T. J., Pimstein, A., Herrmann, I., Karnieli, A., Rundquist, D. C., & Bonfil, D. J. (2014). Estimating green LAI in four crops: Potential of determining optimal spectral bands for a universal algorithm. Agricultural and Forest Meteorology, 192-193: 140-148.

2013

7.   Cierniewski, J., Karnieli, A., Kuśnierek, K., Goldberg, A., & Herrmann, I. (2013). Approximating the average daily surface albedo with respect to soil roughness and latitude. International Journal of Remote Sensing, 34(9-10): 3416-3424.

6.   Herrmann, I., Shapira, U., Kinast, S., Karnieli, A., & Bonfil, D. J. (2013). Ground-level hyperspectral imagery for detecting weeds in wheat field, Precision Agriculture, 14: 637-659.

5.   Shapira U., Herrmann, I., Karnieli, A., & Bonfil, D. J. (2013). Field spectroscopy for weed detection in wheat and chickpea fields, International Journal of Remote Sensing, 34(17): 6094-6108.

2012

4.   Herrmann, I., Berenstein M., Sade, A., Karnieli, A., Bonfil, D. J., & Weintraub, P. G. (2012). Spectral monitoring of two-spotted spider mite damage to pepper leaves. Remote Sensing Letters, 3(4): 277-283.

2011

3.   Herrmann, I., Pimstein A., Karnieli, A., Cohen, Y., Alchanatis, V., & Bonfil, D. J. (2011). LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands, Remote Sensing of Environment, 115: 2141-2151.

2010

2.   Cierniewski, J., Karnieli, A., Herrmann, I., Krolewicz, S., & Kusnierek, K. (2010). Soil surface illumination at micro-relief scale and soil BRDF data collected by a hyperspectral camera. International Journal of Remote Sensing, 31(8): 2151-2157.

1.   Herrmann, I., Karnieli, A., Bonfil, D. J., Cohen, Y., & Alchanatis, V. (2010). SWIR-based spectral indices for assessing nitrogen content in potato fields, International Journal of Remote Sensing, 31(19): 5127-5143.

Popular publications:

1.   Tubul Y. Sadeh R. Tarshish R. Mulero G. Abonete P. Nacson T. & Herrmann I. (2020) Spectral assessment of plant traits: can we assess what we do not see? http://www.israelagri.com/?CategoryID=403&ArticleID=1817