A COMPREHENSIVE REVIEW ON SUITABLE IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUE FOR DISEASE IDENTIFICATION OF TOMATO AND POTATO PLANTS

Abstract

Agriculture plays a vital role in the Sri Lankan economy. Cultivation of crops like tomatoes and potatoes which is being used as a fruit and vegetable will contribute significantly to farmer’s earnings. However, tomato and potato crop faces numerous challenges, such as disease infection can significantly reduce the yield. Early identification of these diseases is crucial for implementing timely interventions and minimizing the potential damage. The current study aims to analyze existing methodologies and identify the most effective approaches for disease detection in tomato and potato crops. Image processing techniques enable the extraction of relevant features from digital images of infected plants, aiding in the identification of diseases accurately. Additionally, machine learning algorithms have proven to be valuable tools for analyzing complex datasets and distinguishing between healthy and diseased plants. The review explores various image processing techniques, including image segmentation, feature extraction, and classification algorithms (support vector machines, random forests, and convolutional neural networks). Suitability of these techniques assured that the disease identification in tomato plants based on their accuracy, efficiency, and robustness. The findings of this review will serve as a foundation for the development of a software application to identify tomato leaf diseases accurately. By enabling accurate disease identification and management, this study seeks to enhance the resilience and productivity of tomato and potato cultivation in Sri Lanka, contributing to the sustainable growth of the agricultural sector.

Keywords

• agricultural automation,
• image processing,
• machine learning