This paper delves into the realm of artificial intelligence, where an array of deep learning
techniques has proven effective in automating crop leaf disease identification and …

Defect detection is a critical component in ensuring the highest quality of printed circuit
board manufacture. The work investigates the application of transfer learning to identify …

Climate change, including rising temperatures, droughts, and floods, has recently become a
global concern. In the agricultural sector, it is anticipated that climate change will …

Zero-day attacks pose a paramount challenge to contemporary cybersecurity experts. These
exploits capitalize on undisclosed security vulnerabilities within software or operating …

This study investigates the possibility of segmenting modeled images of optical vortices
using neural networks. An original dataset was collected for the training and testing of neural …

The agriculture sectors, which account for approximately 50% of the worldwide economic
production, are the fundamental cornerstone of each nation. The significance of precision …

Abstract Deep Neural Networks (DNNs) have become a popular instrument for solving
various real-world problems. DNNs' sophisticated structure allows them to learn complex …

The integration of Deep Reinforcement Learning (DRL) techniques in the context of smart
irrigation systems is examined in this chapter. Smart irrigation has become a viable solution …

This research aims to develop a disease and pest detection system in chili plants using
computer vision techniques. In this study, deep learning methods, especially Convolutional …

Weed is a major problem faced by the agriculture and farming sector. Advanced imaging
and deep learning (DL) techniques have the potential to automate various tasks involved in …