University of Science & Technology Chittagong (USTC) (Bengali: বিজ্ঞান ও প্রযুক্তি বিশ্ববিদ্যালয়, চট্টগ্রাম) is a private university located in Chittagong, Bangladesh. At first, it was established with the sponsorship of a private charity on May 13, 1989. Later it was upgraded to USTC as a full phased university under the Private University Act of 1992.
The rapid expansion of digital agriculture driven by sensors, drones, IoT devices, and remote sensing technologies has generated unprecedented volumes of farm-level data, intensifying concerns surrounding privacy, ownership, and secure data sharing. Federated Learning (FL) has emerged as a promising paradigm that enables distributed model training without exposing raw data, making it well suited for precision agriculture. This systematic review examines 90 peer-reviewed studies published between 2022 and 2025 across major academic databases to evaluate how FL is being applied to enhance agricultural intelligence while reducing raw-data sharing and, in fewer studies, using explicit privacy mechanisms. This review distinguishes between (i) baseline FL, where privacy is primarily achieved by keeping raw data local and (ii) FL augmented with explicit privacy-preserving mechanisms such as differential privacy, homomorphic encryption and secure multi-party computation. Besides, it highlights that horizontal and cross-silo FL architectures dominate current research, particularly in crop disease detection, yield prediction, smart irrigation, and IoT-based farm monitoring. A key finding is that most agricultural FL studies still rely on baseline FL without formal privacy guarantees, and the adoption of DP/HE/SMPC remains limited highlighting a major research gap. Although FL often matches centralized deep-learning accuracy, real-world adoption is limited by non-IID data, communication costs, and constrained compute/connectivity in rural settings. This review summarizes FL architectures, agricultural applications, and the limited use of explicit privacy mechanisms, highlighting key gaps and research directions (personalized/hierarchical FL, federated reinforcement learning, explainable and energy-efficient models, and quantum-enhanced FL).
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.
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.
Traditionally, the Heliotropium indicum plant has been utilized in indigenous medicine to manage different health issues including asthma, coughs, bronchitis, skin disorders, wounds, and rheumatic pains. The purpose is to measure the comparative characteristics of H. indicum (MEHI) in terms of its pain-relieving properties by in vivo testing on mice and to conduct a quantitative phytochemicals analysis using mass spectrometer to screen out the most active compound through computational investigation. The pain-relieving properties of MEHI were analyzed through the nociceptive model, revealing a 78.80
The Malvaceae (formerly Tiliaceae) family's Grewia serrulata DC has remarkable medicinal characteristics and its many parts are utilized in traditional medicine. However, scientific evidence supporting its pharmacological activities remains limited, particularly regarding the bioactivity of its leave extracts and solvent fractions. Therefore, the present study aimed to evaluate the phytochemical profile and antioxidant, anti-inflammatory, and antidiarrheal activities of the methanol extract of G. serrulata leaves and solvent fractions. To investigate the antioxidant effect, a 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging assay was conducted in vitro, while in vivo anti-inflammatory and antidiarrheal activities were evaluated using the carrageenan-induced paw edema model and the castor oil-induced diarrhea model in mice, respectively. G. serrulata methanol extract (GSME) and its n-Hexane fraction (GSNH) showed significant potential in DPPH scavenging at higher doses with showing IC50 values of 11.7886μg/mL and 89.86μg/mL, respectively. Also, GSME, GSNH & Dichloromethane fractions (GSDM) displayed significant (p <0.001) anti-inflammatory and antidiarrheal effects compared to control. GSME, GSNH & GSDM reduced inflammation at a rate of 88.48%, 66.03% & 58.46% during 4th hour of post-injection. GSME & GSNH demonstrated a highly significant (p <0.001) reduction (71.21% & 62.12% inhibition, respectively) in diarrhea, while Loperamide showed an inhibition of 72.73%. The findings indicate that G. serrulata leaves exhibit promising antioxidant, anti-inflammatory and antidiarrheal activities. The results provide preliminary pharmacological support for the traditional use of this plant and highlight its potential as a source of bioactive compounds for future drug discovery, though further investigations including bioassay-guided isolation, mechanistic investigations, and safety evaluations are required.
