This paper presents a comprehensive framework for integrating Explainable Artificial Intelligence (XAI) into smart agriculture to address challenges in transparency, interpretability, and trust associated with AI-driven decision-making. Leveraging techniques such as SHAP, LIME, and Grad-CAM, the framework provides actionable insights into predictive maintenance, crop health monitoring, and resource optimization. A hybrid methodology combines IoT-based data acquisition, Federated Learning (FL), and multimodal feature analysis to ensure scalability and privacy preservation. Additionally, the study introduces a multi-context agricultural dataset and a novel interpretability-accuracy metric to evaluate XAI models’ adaptability across diverse agricultural settings. Experimental results demonstrate the proposed framework’s superiority in achieving an optimal balance between accuracy and interpretability, resource efficiency, and robust decision-making in precision agriculture. This approach fosters sustainable practices while addressing ethical and practical challenges in democratizing AI in agriculture.

The rapid advancement in self-driving and autonomous vehicles (AVs) integrated with artificial intelligence (AI) technology demands not only precision but also output transparency. In this paper, we propose a novel hybrid explainable AI (XAI) framework that combines local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP). Our framework combines the precision and globality of SHAP and low computational requirements of LIME, creating a balanced approach for onboard deployment with enhanced transparency. We evaluate the proposed framework on three different state-of-the-art models: ResNet-18, ResNet-50, and SegNet-50 on the KITTI dataset. The results demonstrate that our hybrid approach consistently outperforms traditional approaches by achieving a fidelity rate of more than 85%, interpretability factor of more than 80%, and consistency of more than 70%, surpassing the conventional methods. Furthermore, the inference time of our proposed framework with ResNet-18 was 0.28 s; for ResNet-50, it was 0.571 s; and that for SegNet was 3.889 s with XAI layers. This is optimal for onboard computations and deployment. This research establishes a strong foundation for the deployment of XAI in safety-critical AV with balanced tradeoffs for real-time decision-making.

Machine learning (ML) is a branch of artificial intelligence (AI) that has been successfully applied in a variety of remote sensing applications, including geophysical information retrieval such as soil moisture content (SMC). Deep learning (DL) is a subfield of ML that uses models with complex structures to solve prediction problems with higher performance than traditional ML. In this study, a framework based on DL was developed for SMC retrieval. For this purpose, a sample dataset was built, which included synthetic aperture radar (SAR) backscattering, radar incidence angle, and ground truth data. Herein, the performance of five optimized ML prediction models was evaluated in terms of soil moisture prediction. However, to boost the prediction performance of these models, a DL-based data augmentation technique was implemented to create a reconstructed version of the available dataset. This includes building a sparse autoencoder DL network for data reconstruction. The Bayesian optimization strategy was employed for fine-tuning the hyperparameters of the ML models in order to improve their prediction performance. The results of our study highlighted the improved performance of the five ML prediction models with augmented data. The Gaussian process regression (GPR) showed the best prediction performance with 4.05% RMSE and 0.81 R² on a 10% independent test subset.

The rapid advancement of precision farming, automated irrigation systems, and predictive analytics has revolutionized agriculture, but these innovations also introduce new challenges, particularly in data integrity and security. With the increased usage of IoT devices, data integration of vast amounts of data becomes critical. This paper proposes a comprehensive framework, AgriChainSynch, which integrates IoT, blockchain, and artificial intelligence systems to address these challenges. The proposed framework shows a dynamic data storage algorithm that optimizes storage decisions based on data characteristics and features. Furthermore, the Blockchain Integration Layer (BIL) ensures security and scalability with a Feedback and Adaptation Module (FAM) that continuously improves systems performance. The proposed hybrid blockchain structure balances privacy and scalability with homomorphic encryption. Extensive experimentation and validation demonstrate that the proposed framework improves storage efficiency with reduced processing time and enhances network performance, offering improved solutions for modern agricultural systems. The findings underscore the potential of integrating IoT, blockchain, and AI under one umbrella, supporting large-scale farming operations.

Current improvements in quantum computing present a substantial challenge to classical cryptographic systems, which typically rely on problems that can be solved in polynomial time using quantum algorithms. Consequently, post-quantum cryptography (PQC) has emerged as a promising solution to emerging quantum-based cryptographic challenges. The greatest threat is public-key cryptosystems, which are primarily responsible for key exchanges. In PQC, key encapsulation mechanisms (KEMs) are crucial for securing key exchange protocols, particularly in Internet communication, virtual private networks (VPNs), and secure messaging applications. CRYSTALS-Kyber and NTRU are two well-known PQC KEMs offering robust security in the quantum world. However, even when quantum computers are functional, they are not easily accessible. IoT devices will not be able to utilize them directly, so there will still be a requirement to protect IoT devices from quantum attacks. Concerns such as limited computational power, energy efficiency, and memory constraints in devices such as those used in IoTs, embedded systems, and smart cards limit the use of these techniques in constrained environments. These concerns always arise there. To address this issue, this study conducts a broad comparative analysis of Kyber and NTRU, with special focus on their security, performance, and implementation efficiency in such environments (IOT/constrained environments). In addition, a case study was conducted by applying KEMs to a low-power embedded device to analyze their performance in real-world scenarios. These results offer an important comparison for cyber security engineers and cryptographers who are involved in integrating post-quantum cryptography into resource-constrained devices.