An advanced artificial intelligence framework integrating ensembled convolutional neural networks and Vision Transformers for precise soil classification with adaptive fuzzy logic-based crop recommendations

Farhan Sheth, Priya Mathur, Amit Kumar Gupta, Sandeep Chaurasia

Abstract: This study introduces an advanced Artificial Intelligence (AI) framework for soil classification and crop recommendation, combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in an ensemble approach, alongside an adaptive fuzzy logic-based decision system for crop suggestions. While existing research typically addresses soil classification or crop recommendation in isolation, this work integrates cutting-edge deep learning models and fuzzy logic to enhance both tasks. The methodology is divided into two phases: Phase 1 covers data collection, preprocessing, and augmentation using Cycle Generative Adversarial Networks (CycleGAN) to expand the curated dataset of 1189 soil images to 8,413, while Phase 2 focuses on training CNN and ViT models, ensembling these models, and developing a fuzzy logic system that considers soil type, nutrients, potential of hydrogen (pH), and climatic conditions for crop recommendations. Experimental results indicate models achieve classification accuracies of up to 89.32 % on the original dataset, improving to 91.01 % with augmented data. On the CycleGAN-augmented (CyAUG) dataset, EfficientNet v2 Large and ViT-Large/16 attain accuracies of 99.60 % and 99.73 %, respectively. Furthermore, an ensemble of these architectures achieves a perfect accuracy of 100 %. The results are also validated by K-fold cross-validation. The research also presents 'Agro Companion,' an AI-powered tool that assists farmers in soil identification and crop selection based on geological and environmental data. This framework addresses key agricultural challenges in India, offering a high-accuracy, practical solution for improving both soil classification and crop recommendation. This research delivers state-of-the-art soil classification performance and a robust AI-based crop recommendation tool to support sustainable agricultural practices.