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Volume 13
Issue 5
10.3390/agronomy13051281
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Article

Artificial Intelligence Integrated GIS for Land Suitability Assessment of Wheat Crop Growth in Arid Zones to Sustain Food Security

by
Radwa A. El Behairy
1,
Hasnaa M. El Arwash
2,
Ahmed A. El Baroudy
1,
Mahmoud M. Ibrahim
1,
Elsayed Said Mohamed
3,
Nazih Y. Rebouh
4 and
Mohamed S. Shokr
1,*
1
Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt
2
Mechatronics Engineering Department, Alexandria Higher Institute of Engineering & Technology (AIET), Alexandria 21544, Egypt
3
National Authority for Remote Sensing and Space Sciences, Cairo 1564, Egypt
4
Department of Environmental Management (RUDN University), 6 Miklukho-Maklaya St., 117198 Moscow, Russia
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(5), 1281; https://doi.org/10.3390/agronomy13051281
Submission received: 27 March 2023 / Revised: 15 April 2023 / Accepted: 27 April 2023 / Published: 29 April 2023
(This article belongs to the Special Issue The Effect of Appropriate Agriculture Management on Soil and Sustainable Crop Productivity)
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Abstract

Developing countries all over the world face numerous difficulties with regard to food security. The purpose of this research is to develop a new approach for evaluating wheat’s suitability for cultivation. To this end, geographical information systems (GIS) and fuzzy inference systems (FIS) are used as the most appropriate artificial intelligence (AI) tools. Outcomes of investigations carried out in the western Nile Delta, Egypt. The fuzzy inference system used was Mamdani type. The membership functions used in this work are sigmoidal, Gaussian, and zmf membership. The inputs in this research are chemical, physical, and fertility soil indices. To predict the final soil suitability using FIS, it is required to implement 81 IF-THEN rules that were written by some experts. The obtained results show the effectiveness of FIS in predicting the wheat crop’s suitability compared to conventional methods. The research region is split into four classes: around 241.3 km2 is highly suitable for wheat growth, and 224 km2 is defined as having moderate suitability. The third soil suitability class (low), which comprises 252.73 km2, is larger than the unsuitable class, which comprises 40 km2. The method given here can be easily applied again in an arid region. Decision-makers may benefit from the research’s quantitative findings.
Keywords: wheat cultivation; crop suitability; fuzzy inference system; GIS; drylands wheat cultivation; crop suitability; fuzzy inference system; GIS; drylands

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MDPI and ACS Style

El Behairy, R.A.; Arwash, H.M.E.; El Baroudy, A.A.; Ibrahim, M.M.; Mohamed, E.S.; Rebouh, N.Y.; Shokr, M.S. Artificial Intelligence Integrated GIS for Land Suitability Assessment of Wheat Crop Growth in Arid Zones to Sustain Food Security. Agronomy 2023, 13, 1281. https://doi.org/10.3390/agronomy13051281

AMA Style

El Behairy RA, Arwash HME, El Baroudy AA, Ibrahim MM, Mohamed ES, Rebouh NY, Shokr MS. Artificial Intelligence Integrated GIS for Land Suitability Assessment of Wheat Crop Growth in Arid Zones to Sustain Food Security. Agronomy. 2023; 13(5):1281. https://doi.org/10.3390/agronomy13051281

Chicago/Turabian Style

El Behairy, Radwa A., Hasnaa M. El Arwash, Ahmed A. El Baroudy, Mahmoud M. Ibrahim, Elsayed Said Mohamed, Nazih Y. Rebouh, and Mohamed S. Shokr. 2023. "Artificial Intelligence Integrated GIS for Land Suitability Assessment of Wheat Crop Growth in Arid Zones to Sustain Food Security" Agronomy 13, no. 5: 1281. https://doi.org/10.3390/agronomy13051281

APA Style

El Behairy, R. A., Arwash, H. M. E., El Baroudy, A. A., Ibrahim, M. M., Mohamed, E. S., Rebouh, N. Y., & Shokr, M. S. (2023). Artificial Intelligence Integrated GIS for Land Suitability Assessment of Wheat Crop Growth in Arid Zones to Sustain Food Security. Agronomy, 13(5), 1281. https://doi.org/10.3390/agronomy13051281

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