Work

Modeling habitat suitability for saffron (Crocus sativus L.) using AI-based approaches to support sustainable industrial cultivation

Year: 2025

Type: article

Abstract: Saffron is a highly strategic medicinal plant in Iran that requires precise habitat suitability analysis to aid expansion of its cultivation areas. The objective of this study was to create a habitat suitability map for saffron in the region, identify the factors that have the most significant influence on saffron growth, and evaluate various machine learning models to determine which algorithm offers the best accuracy for the modeling process. For this purpose, 17 layers of important factors that affect saffron growth were collected and integrated into the model. Three machine learning algorithms were utilized for the modeling process: Classification and Regression Trees (CART), Multivariate Adaptive Regression Splines (MARS), and Mixture Discriminant Analysis (MDA), all implemented in R software. The evaluation of variable importance and machine learning model performance was conducted using the PLS and GLM algorithms and Receiver Operating Characteristic (ROC)Area Under the Curve (AUC), respectively. The results revealed that CART, MARS, and MDA achieved high accuracies of 97.6, 95.1, and 92.6 %, respectively. This study identified several critical factors affecting saffron growth, including rainfall, slope, and elevation. Habitat suitability maps identified 44–49 % of the study area as “high” and “very high” suitability classes, concentrated mainly in the southern and eastern regions. In contrast, 22–26 % of the land is classified as “low” suitability, predominantly located in the central part of the study area, while the remaining 25–34 % is categorized as “moderate” suitability. Based on the suitability map, we devised a strategic plan to expand the cultivation area in regions with high suitability and to avoid areas with low suitability. Integrating these maps into regional agricultural planning enables stakeholders to anticipate environmental challenges, minimize financial risks, and enhance the reliability of yield forecasts. This approach also facilitates efficient allocation of resources and minimizes trial and error when extending cultivation to new areas.

Source: Industrial Crops and Products

DOI: 10.1016/j.indcrop.2025.121974

Authors: Ghazal Shafiee Sarvestani, Mohsen Edalat, Emran Dastres, Rouhollah Naderi, Hamid Reza Pourghasemi

Institutions: Shiraz University, Shahid Beheshti University

Language: English

Topic: Soil and Land Suitability Analysis

Subfield: Management, Monitoring, Policy and Law

Field: Environmental Science

Domain: Physical Sciences

SDGs: Zero hunger, Life on land

Open Access status: gold

Estimated APC paid: $3,720