Early detection of plant and crop diseases is vital for achieving sustainable agriculture and global food security. Traditional inspection methods are often slow and subjective, whereas Artificial Intelligence (AI) techniques offer fast, scalable, and objective alternatives. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, this systematic review synthesizes 145 studies published between 2023 and 2025 that employ Machine Learning (ML), Deep Learning (DL), Explainable AI (XAI), and Federated Learning (FL) for plant and crop disease classification. The studies are organized by crop species, imaging modality, and model architecture to evaluate performance in terms of accuracy, robustness, interpretability, and privacy preservation. Results reveal that XAI techniques such as Grad-CAM, LIME, and SHAP enhance transparency and trust, while FL enables decentralized and privacy-aware collaboration across distributed agricultural datasets with only minimal reductions in model accuracy compared to centralized training. Despite strong results in controlled conditions, many models struggle to generalize under real-field variability due to data imbalance and environmental factors. Emerging directions include lightweight edge architectures, domain adaptation, and unified explainable-federated frameworks. Overall, this review identifies FL and XAI as complementary drivers of transparent, privacy-preserving, and scalable AI systems for sustainable precision agriculture.

Sustainable agriculture in arid regions faces critical challenges due to water scarcity, high temperatures, and inefficient traditional farming practices. This study presents an AI-enabled smart farming framework for optimizing date palm (Phoenix dactylifera) cultivation through the integration of Machine Learning (ML) and Internet of Things (IoT) technologies. A structured multimodal dataset comprising biometric features palm height, trunk diameter, and leaf number, environmental parameters soil moisture, temperature, and humidity, and categorical attributes variety and health status was analyzed to classify palm health and support data-driven irrigation management. Four ML algorithms Random Forest (RF), Gradient Boosting Machine (GBM), Artificial Neural Network (ANN), and Support Vector Machine (SVM) were developed and optimized using grid search with five-fold cross-validation. Among them, the Random Forest model achieved the highest classification accuracy of 95.3%, demonstrating strong robustness for heterogeneous agricultural data. Feature importance analysis highlighted soil moisture, humidity, trunk diameter, and leaf number as key contributors to palm health prediction. The proposed AI–IoT framework enables real-time monitoring, predictive diagnostics, and automated decision support for sustainable water use and crop management, aligning with Saudi Vision 2030 objectives for technology-driven and resource-efficient agriculture.

The rapid expansion of Bangladesh’s power and industrial sectors has led to a significant increase in demand for electrical equipment, a large portion of which is currently fulfilled through imports, resulting in sustained foreign currency outflows and supply chain vulnerabilities. This study investigates the feasibility, opportunities, and constraints associated with developing local assembly and manufacturing of electrical equipment, with particular focus on transformers and low- and medium-voltage panels. The analysis is based on a mixed-method approach that integrates foreign trade data from FY 2021–22 to FY 2023–24, industry-level information, and policy evaluation to assess import dependency, domestic manufacturing capability, and export potential. The findings reveal that the sector is characterized by high reliance on imported finished products and raw materials, indicating structural limitations in local value addition and technological capability. However, the study demonstrates that strengthening domestic manufacturing can significantly reduce import dependency, shorten procurement lead times, and enhance supply chain resilience while generating employment and supporting industrial skill development. Furthermore, the analysis identifies key fiscal and procurement policy reforms as critical enablers for improving cost competitiveness, facilitating import substitution, and unlocking export opportunities. The study highlights the strategic importance of developing a robust local manufacturing ecosystem to support long-term industrial sustainability and economic resilience in Bangladesh.