Artificial intelligence applications in precision agriculture to predict the effect of Root-Knot nematodes and grafting on vegetable crop health from proximal remote sensing machines

Yassine Hamdane; Khaoula Abrougui; Francisco Javier Sorribas; José Luis Araus; Shawn C. Kefauver

doi:10.56027/JOASD.192022

Auteurs

Yassine Hamdane
Khaoula Abrougui
Francisco Javier Sorribas
José Luis Araus
Shawn C. Kefauver

DOI :

https://doi.org/10.56027/JOASD.192022

Mots-clés :

Artificial intelligence, precision agriculture, crop health indicator, grafting, remote sensing machines

Résumé

In precision agriculture, the Normalized Difference Vegetative Index (NDVI) considers the spectral characteristics of healthy green vegetation. This index is an effective way of detecting the green state of plants. This is why we choose to use NDVI as a reference index to predict the effect of Root-Knot nematodes and grafting on vegetable crop health from proximal remote sensing machines. These machines were used to estimate different physiological, biochemical, and agronomic parameters as indicators of stress (GA, GGA, SPAD, and canopy temperature). Leaf level pigments were measured using a handheld sensor (SPAD). Canopy vigor and biomass were assessed using vegetation indices derived from RGB images and the NDVI was measured with a portable spectroradiometer (Greenseeker). The plant level water stress was assessed indirectly by plant temperature using an infrared thermometer. We conclude that the grafted plants were less stressed and more protected against nematode attack. The comparison of NDVI index predicted by AI models showed that artificial neural network MLP demonstrated the best prediction performance than the linear regression method. However, their R-squared decreased from 0.820 to 0.772, and NRMSE increased from 12.3% to 12.4%, respectively.

Références

Abd-Elgawad, M.M. (2020). Biological control agents in the integrated nematode management of potato in Egypt. Egypt. J. Biol. Pest Control 30, 1–13.

Abdipour, M., Younessi-Hmazekhanlu, M., Ramazani, S.H.R. (2019). Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower 496 (Carthamus tinctorius, L.). Ind. Crops Prod 127, 185–194.

Abrougui, K., Nour El Houda, B., Meriem, B., Maria, B., Joel, S., Stéphane, D., Roua, A., Sayed, C., Neji, T., Shawn, C.K. (2022a). Assessing Phytosanitary Application Efficiency of a Boom Sprayer Machine Using RGB Sensor in Grassy Fields. Sustainability 14, 3666.

Abrougui, K., Ridha, G., Aymen, O., Nour El Houda, B., Fatma, H., Yassine, B., Roua, A., Chiheb, K., Bessem, A., Sayed, C., Shawn, K. (2022b). Contribution of UAV-airborne imagery in the study of machine-soil-plant interaction in potato cultivation. Journal of Oasis ‎Agriculture and Sustainable Development pp: 71-78.

Araus, J.L., Kefauver, S.C. (2018). Breeding to adapt agriculture to climate change: Affordable phenotyping solutions. Curr. Opin. Plant Biol 45, 237–247.

Bayatvarkeshi, M., Bhagat, S.K., Mohammadi, K., Kisi, O., Farahani, M., Hasani, A., Deo, R., Yaseen, Z.M. (2021). Modeling soil temperature using air temperature features in diverse climatic conditions with complementary machine learning models. Comput. Electron. Agric 185, 106158.

Blok, V.C., Jones, J.T., Phillips, M.S., Trudgill, D.L. (2008). Parasitism genes and host range disparities in biotrophic nematodes: The conundrum of polyphag versus specialisation. Bioessays 30, 249–259.

Citakoglu, H. (2017). Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey. Arch. Meteorol. Geophys. Bioclimatol. Ser. B 130, 545–556.

Duncan, G.A.; Gates, R.; Montross, M.D. Measuring Relative Humidity in Agricultural Environments; Agricultural Engineering Extension Publications-Uknowledge: Lexington, KY, USA, 2005.

Expósito, A., García, S., Giné, A., Escudero, N., Sorribas, F.J. (2019). Cucumis metuliferus reduces Meloidogyne incognita virulence against the Mi1.2 resistance gene in a tomato–melon rotation sequence. Pest Manag. Sci 75, 1902–1910.

Feng, Y., Cui, N., Hao, W., Gao, L., Gong, D. (2019). Estimation of soil temperature from meteorological data using different machine learning models. Geoderma 338, 67–77.

Gitelson, A.A., Gritz, Y., Merzlyak, M.N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. In: Journal of Plant Physiology 160(3), 271-282. http://doi.org/10.1078/0176- 1617-00887.

Gracia-Romero, A., Kefauver, S.C., Fernandez-Gallego, J.A., Vergara-Díaz, O., Nieto-Taladriz, M.T., Araus, J.L. (2019). UAV and ground image-based phenotyping: A proof of concept with Durum wheat. Remote Sens 11, 1244.

Guan, W., Zhao, X., Dickson, D.W., Mendes, M.L., Thies, J. (2014). Root-knot nematode resistance, yield, and fruit quality of specialty melons grafted onto Cucumis metulifer. Hort Sci 49, 1046–1051.

Hamdane, Y. (2020). Comparison of proximal remote sensing devices of vegetable crops to determine the role of grafting in plant resistance to Root-Knot nematodes. Master’s in Environmental Agrobiology. Department of Evolutionary Biology, Ecology and Environmental Sciences. University of Barcelona, Spain p32.

Hamdane, Y., Adrian, G.R., Maria, B., Rut, S.B., Aida, M.F., Francisco, J.S., José Luis, A., Shawn, C.K. (2022). Comparison of Proximal Remote Sensing Devices of Vegetable Crops to Determine the Role of Grafting in Plant Resistance to Meloidogyne incognita. Agronomy 12, 1098.

Hanifeh, I., Juan, H.C., Pierre, P., Hamidreza, S., Abdolmajid, M. (2022). A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction in Ordinary Climate Conditions and Extremely Hot Events. Sustainability 14, 8065. 5

Huang, S., Tang, L., Hupy, J.P., Wang, Y., Shao, G.A. (2021). Commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res 32, 1–6.

Hunt, E.R., Doraiswamy, P.C., Mcmurtrey, J.E., Daughtry, C.S.T., Perry, E.M., Akhmedov, B.A. (2013). Visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int. J. Appl. Earth Obser. Geoinf 21, 103–112.

Kashaija, I., Kizito, F., McIntyre, B., Sali, H. (2004). Spatial distribution of roots, nematode populations and root necrosis in highland banana in Uganda. Nematology 6, 7–12.

Kaufmann, H., Segl, K., Itzerott, S., Bach, H., Wagner, A., Hill, J., Heim, B., Oppermann, K., Heldens, W., Stein, E., Müller, A., van der Linden, S., Leitão, P. J., Rabe, A., Hostert, P. (2010). Hyperspectral algorithms: report in the frame of EnMAP preparation activities, (Scientific Technical Report ; 10/08), Potsdam : Deutsches GeoForschungsZentrum GFZ, 268 p. http://doi.org/10.2312/GFZ.b103-10089.

Kefauver, S.C., El-Haddad, G., Vergara-Diaz, O., Araus, J.L. (2015). RGB picture vegetation indexes for High-Throughput Phenotyping Platforms (HTPPs). In Proceedings of the SPIE Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, Toulouse, France, 22–24 September.

Kefauver, S.C., Vicente, R., Vergara-Díaz, O., Fernandez-Gallego, J.A., Kerfal, S., Lopez, A., Melichar, J.P.E., Serret Molins, M.D., Araus, J.L. (2017). Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley. Front. Plant Sci 8, 01733.

Khemis, C., Abrougui, K., Mohammadi, A., Gabsi, K., Dorbolo, S., Mercatoris, B., Mutuku, E., Cornelis, W., Chehaibi, ‎S. (2022). Development of artificial neural networks to predict the effect of tractor speed on soil compaction ‎using penetrologger test results. Processes 10: 1109 https:// doi.org/10.3390/pr10061109‎.

Konica, M.O. Chlorophyll Meter SPAD-502 Plus-A Lightweight Handheld Meter for Measuring the Chlorophyll Content of Leaves without Causing Damage to Plants. (2012). Available online: http://www.konikcaminolta.com/instrments/download/ catalog/color/pdf/spad502plus_e1.pdf (accessed on 20 August 2022).

Kowalski, P.A., Kusy, M. (2017). Determining significance of input neurons for probabilistic neural network by sensitivity analysis procedure. Comput. Intell 34, 895–916.

Li, C., Zhang, Y., Ren, X. (2020). Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network. Algorithms 13, 173.

Mehdizadeh, S., Fathian, F., Safari, M.J.S., Khosravi, A. (2020). Developing novel hybrid models for estimation of daily soil temperature at various depths. Soil Tillage Res 197, 104513.

Mehmet, A.B., Ehsan, H., Mehmet, F., Seyed, E., Aghakouchaki, H., Ercan, I. (2022). A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings. Appl. Sci 12, 5138.

Miguel, A., Maroto, J. V., San Bautista, A., Baixauli, C., Cebolla, V., Pascual, B., Guardiola, J. L. (2004). The grafting of triploid watermelon is an advantageous alternative to soil fumigation by methyl bromide for control of Fusarium wilt. Scientia Horticulturae 103(1), 9-17.

Silva-Sánchez, A., Buil-Salafranca, J., Cabral, A.C., Uriz-Ezcaray, N., García-Mendívil, H.A., Sorribas, F.J., Gracia-Romero, A. (2019). Comparison of proximal remote sensing devices for estimating physiological responses of eggplants to root-knot nematodes. Proceedings 18, 9.

Sorribas, F.J., Ornat, C., Verdejo-Lucas, S., Galeano, M., Valero, J. (2005). Effectiveness and profitability of the Mi-resistant tomatoes to control root-knot nematodes. Eur. J. Plant Pathol 111, 29–38.

Sung-Sik, P., Peter, O., Seung-Wook, W., Dong-Eun, L. (2020). A Simple and Sustainable Prediction Method of Liquefaction-Induced Settlement at Pohang Using an Artificial Neural Network. Sustainability 12, 4001.

Tabari, H., Sabziparvar, A.A., Ahmadi, M. (2011). Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Arch. Meteorol. Geophys. Bioclimatol. Ser. B 110, 135–142.

Thenkabail, P.S., Smith, R.B., De Pauw, E. (2000). Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ 71, 158–182.

Thenkabail, P.S., Smith, R.B., De Pauw, E. (2002). Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogramm. Eng. Remote Sens 68, 607–622.

Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ 8, 127–150.

Zaman-Allah, M., Vergara, O., Araus, J.L., Tarekegne, A., Magorokosho, C., Zarco-Tejada, P.J., Hornero, A., Albà, A.H., Das, B., Craufurd, P. (2015). Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant Methods 11, 35.