Smart agriculture is a concept that refers to a revolution in the agriculture industry that promotes the monitoring of activities necessary to transform agricultural methods to ensure food security in an ever-changing environment. These days, the role of technology is increasing rapidly in every sector. Smart agriculture is one of these sectors, where technology is playing a significant role. The key aim of smart farming is to use the technologies to increase the quality and quantity of agricultural products. IOT and digital image processing are two commonly utilized technologies, which have a wide range of applications in agriculture. IOT is an abbreviation for the Internet of things, i.e., devices to execute different functions. Image processing offers various types of imaging sensors and processing that could lead to numerous kinds of IOT-ready applications. In this work, an integrated application of IOT and digital image processing for weed plant detection is explored using the Weed-ConvNet model to provide a detailed architecture of these technologies in the agriculture domain. Additionally, the regularized Weed-ConvNet is designed for classification with grayscale and color segmented weed images. The accuracy of the Weed-ConvNet model with color segmented weed images is 0.978, which is better than 0.942 of the Weed-ConvNet model with grayscale segmented weed images.

The fundamental significance of agriculture in preserving human life and providing necessary resources has led to frequent references to it as the “backbone of human civilization.” In human cultures all over the world, the agricultural sector has played a significant role. In today's world, emerging technologies play a very important role in the agriculture sector. Machine learning is one of them; machine learning significantly contributes to the transformation of agriculture by facilitating data-driven decision-making, enhancing productivity, and optimizing numerous processes. There are so many areas where machine learning can be applied in agriculture processes like crop and soil monitoring, yield prediction, pest and disease management, etc. Pests and diseases are major obstacles to achieving higher productivity. The creation of effective techniques for the automatic detection and forecasting of pests and illnesses in crops is very much important. In this chapter, the authors use different machine-learning algorithms to detect early signs of pests, diseases, and weed infestations in crops.

The current research endeavors to evaluate the efficacy of regression-based machine learning algorithms through an assessment of their performance using diverse metrics. The focus of our study involves the implementation of the breast cancer Wisconsin (Diagnostic) dataset, employing both the random forest and gradient-boosting regression algorithms. In our comprehensive performance analysis, we utilized key metrics such as Mean Squared Error (MSE), R-squared, Mean Absolute Error (MAE), and Coefficient of Determination (COD), supplemented by additional metrics. The evaluation aimed to gauge the algorithms' accuracy and predictive capabilities. Notably, for continuous target variables, the gradient-boosting regression model emerged as particularly noteworthy in terms of performance when compared to other models. The gradient-boosting regression model exhibited remarkable results, highlighting its superiority in handling the breast cancer dataset. The model achieved an impressively low MSE value of 0.05, indicating minimal prediction errors. Furthermore, the R-squared value of 0.89 highlighted the model's ability to explain the variance in the data, affirming its robust predictive power. The Mean Absolute Error (MAE) of 0.14 reinforced the model's accuracy in predicting continuous outcomes. Beyond these core metrics, the study incorporated additional measures to provide a comprehensive understanding of the algorithms' performance. The findings underscore the potential of gradient-boosting regression in enhancing predictive accuracy for datasets with continuous target variables, particularly evident in the context of breast cancer diagnosis. This research contributes valuable insights to the ongoing exploration of machine learning algorithms, providing a basis for informed decision-making in medical and predictive analytics domains.

AI is transforming student assessment in higher education, offering solutions to limitations of traditional methods. It enables personalized, efficient, and data-driven evaluation through automated grading, adaptive learning platforms, and analytics tools. While case studies show improved outcomes, ethical concerns like bias must be addressed. Responsible AI implementation can enhance assessment practices, promoting educational equity and excellence. Institutions adopting AI should set clear goals, involve stakeholders, and maintain ethical safeguards.

The rapid growth of e-commerce and urbanization has intensified the complexity of last-mile logistics, creating significant environmental and operational challenges. This chapter examines the transformative role of technology innovations and digital solutions in achieving sustainable last-mile delivery systems. It explores cutting-edge technologies including autonomous vehicles, drones, electric fleets, blockchain for supply chain transparency, artificial intelligence for route optimization, Internet of Things (IoT) sensors, and crowdsourcing platforms. The chapter analyzes how these innovations address traditional challenges such as traffic congestion, carbon emissions, delivery failures, and cost inefficiencies.