Published Paper Details:

Agriculture plays a critical role in global food security and the economy. However, one of the major challenges in agriculture is the early detection and control of plant diseases, which, if left untreated, can lead to significant yield losses and reduced crop quality. Traditionally, plant disease detection relies on manual inspection by agricultural experts, which is often time-consuming, subjective, and not always accessible, especially in rural or underdeveloped regions. With the advancement of artificial intelligence (AI), particularly machine learning (ML), there has been a growing interest in applying these technologies to automate the process of plant disease detection. Machine learning techniques allow systems to learn from large datasets of plant images and accurately identify disease symptoms, often even before they become visible to the naked eye. In recent years, image-based plant disease detection using machine learning has gained popularity due to the availability of large datasets, advancements in computing power, and the development of powerful algorithms such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests. These models can be trained to recognize complex patterns and classify different types of diseases based on visual symptoms like color, texture, and shape of the affected leaves or fruits.