Exploring Machine Learning Frameworks for Precision Agriculture: Focus on Leaf Diseases

Abstract

Leaf diseases pose a significant challenge for farmers, as they can reduce crop yields and threaten food security. In the past,manual inspection was used to detect these illnesses, which may be a time-consuming and human error-prone procedure.New methods for recognizing and classifying leaf diseases have surfaced with the development of machine learning (ML)and deep learning (DL), providing more accurate and effective treatments.This research explores the use of a variety of machine learning and deep learning techniques, such as neural networks, random forests, and support vector machines (SVM). Additionally, it highlights advanced techniques such as convolutional neural networks (CNNs) and transfer learning.A key resource in this field is the PlantVillage dataset, which has been instrumental in developing and testing these technologies. While these methods offer promising results, they come with challenges. For example, the limited diversity of available datasets can hinder model performance, and applying these techniques in real-world, real-time settings remains difficult. Scalability is another issue, as models designed for small datasets often struggle to handle larger or more complex ones. The paper identifies potential directions for future research, including designing more efficient models, enhancing the interpretability of deep learning systems, and adapting models for various environmental conditions. By providing an overview of current research, this review aims to support the development of better tools for automated leaf disease detection, ultimately benefiting both farmers and the broader agricultural community