The economy, unemployment, and job creation of South Africa heavily depend on the growth of the agricultural sector. With a growing population of 60 million, there are approximately 4 million small-scale farmers (SSF) number, and about 36,000 commercial farmers which serve South Africa. The agricultural sector in South Africa faces challenges such as climate change, lack of access to infrastructure and training, high labour costs, limited access to modern technology, and resource constraints. Precision agriculture (PA) using AI can address many of these issues for small-scale farmers by improving access to technology, reducing production costs, enhancing skills and training, improving data management, and providing better irrigation infrastructure and transport access. However, there is a dearth of research on the application of precision agriculture using artificial intelligence (AI) by small scale farmers (SSF) in South Africa and Africa at large. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Bibliometric analysis guidelines were used to investigate the adoption of precision agriculture and its socio-economic implications for small-scale farmers in South Africa or the systematic literature review (SLR) compared various challenges and the use of PA and AI for small-scale farmers. The incorporation of AI-driven PA offers a significant increase in productivity and efficiency. Through a detailed systematic review of existing literature from inception to date, this study examines 182 articles synthesized from two major databases (Scopus and Web of Science). The systematic review was conducted using the machine learning tool R Studio. The study analyzed the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.

The Agriculture Trading Platform (ATP) represents a significant innovation in the realm of agricultural trade in Malaysia. This web-based platform is designed to address the prevalent inefficiencies and lack of transparency in the current agricultural trading environment. By centralizing real-time data on agricultural production, consumption, and pricing, ATP provides a comprehensive dashboard that facilitates data-driven decision-making for all stakeholders in the agricultural supply chain. The platform employs advanced deep learning algorithms, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to forecast market trends and consumption patterns. These predictive capabilities enable producers to optimize their market strategies, negotiate better prices, and access broader markets, thereby enhancing the overall efficiency and transparency of agricultural trading in Malaysia. The ATP’s user-friendly interface and robust analytical tools have the potential to revolutionize the agricultural sector by empowering farmers, reducing reliance on intermediaries, and fostering a more equitable trading environment.