International Research Journal of Education and Technology

Agriculture is fundamental to global food security, and addressing crop diseases is essential for sustainable farming. This study explores the application of machine learning for leaf disease prediction, leveraging advanced image processing and classification techniques. Features such as leaf texture, shape, and color variations are analyzed to identify disease patterns. Machine learning models, including Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), are implemented to classify healthy and diseased leaves with high accuracy. These models enhance traditional farming methods by providing data-driven insights and reducing dependency on manual inspections.The application of machine learning (ML) in agriculture has transformed traditional farming practices, offering innovative solutions to critical challenges such as plant disease detection and management. Among these, leaf diseases are a significant concern as they often go unnoticed until substantial damage occurs, leading to reduced crop yields and economic losses. This paper presents a machine learning-based leaf disease predictor designed to assist farmers and agricultural professionals in diagnosing plant health with high accuracy and efficiency. The proposed solution leverages advanced image recognition algorithms, particularly convolutional neural networks (CNNs), to analyze visual symptoms on plant leaves, such as discoloration, texture changes, and structural deformations. A comprehensive dataset of labeled leaf images, encompassing various plant species and common diseases, is utilized to train and validate the model, ensuring robust performance across diverse scenarios. By integrating scalable architectures, the predictor supports real- time disease diagnosis through user-friendly mobile and web applications, making it accessible even in remote and resource-limited environments. This innovative approach not only enables early disease detection but also reduces dependency on chemical pesticides, facilitates timely interventions, and minimizes crop losses. Moreover, it promotes sustainable agricultural practices by enhancing the efficiency of resource usage, improving crop productivity, and supporting environmental conservation. The study highlights the significant impact of machine learning in addressing global agricultural challenges, emphasizing its role in ensuring food security and economic sustainability. The integration of ML into farming practices paves the way for precision agriculture, where tailored solutions  are  provided  for  specific  crop  needs,

thereby optimizing yields. Experimental results demonstrate the model's effectiveness in accurately classifying and predicting diseases, underscoring its potential as a reliable tool for modern agriculture. By bridging the gap between cutting-edge technology and practical farming applications, this research showcases how ML-driven tools can revolutionize agriculture, making it more resilient and adaptive to future challenges. The study ultimately advocates for the adoption of technology-driven strategies in agriculture to meet growing food demands while mitigating the impacts of climate change and resource constraints, positioning machine learning as a cornerstone of next-generation farming innovations.

KEYWORDS

Machine learning, leaf disease prediction, agriculture, image processing, CNN, SVM, sustainable farming.