Development of deep learning-based mobile application for predicting in-situ habitat suitability of Perilla frutescens L. in real-time

Species distribution modeling (SDM) can be a valuable tool to improve perilla production by identifying optimal locations for its cultivation in the North Eastern Hill (NEH) region of India. Numerous habitat suitability modeling techniques are available; however, requirement of sophisticated hardware and software for their execution limits their in-situ utility to agriculturalists in real-time. Integrating SDM on edge devices for habitat suitability predictions is challenging due to the computational demands and complexity of current modeling techniques. Hence, in the present study, we developed an artificial intelligence (AI)-based mobile application to predict perilla habitat suitability solely from geographical location. Maximum Entropy (MaxEnt) software with perilla occurrence data from the NEH region was utilized to generate an accurate suitability map (Area Under Curve (AUC) for test data = 0.88). Probabilities and corresponding locations extracted from the suitability map were used as training data for AI models including Random Forest Regression (RFR), Support Vector Regression (SVR), and Artificial Neural Network (ANN). The ANN model, architecture being optimized using a genetic algorithm, achieved the best performance (R² = 0.81). All models show good predictive ability (R² > 0.75) in predicting actual habitat suitability (Residual Prediction Deviation (RPD) > 2.10), and a high degree of relationship between predicted and actual probabilities (AUC > 94.0 %) was also observed. The mobile application integrated with the ANN model achieved high AUC (>97.0 %) and R² (0.82) values for testing locations and predicted known perilla locations with an accuracy of 76.0 %. This shows the practical utility of AI-based mobile application for species distribution modeling and emphasizes its potential for perilla cultivators. The developed user-friendly mobile application may help farmers of NEH region to predict optimal locations for perilla cultivation in real-time with a single click, thereby enhancing sustainable production efficiency and biodiversity conservation efforts in their locales.