A Review of Plant Disease Identification using Computational Techniques

In India, the imperative to bolster agricultural productivity in the face of population growth and heightened food demands underscores the significance of disease detection in crops. This study delves into advanced plant disease detection methodologies, specifically emphasizing the integration of ML and DL techniques. Machine learning, with its capacity for autonomous information processing, proves instrumental in precise disease identification. However, recent strides in deep learning, particularly in computer vision, reveal superior performance in detecting diverse plant diseases across various crops compared to traditional machine learning. The research advocates for the proactive use of deep learning for early disease detection through image analysis, offering a sentinel approach against bacterial, viral, and fungal infections. This not only fortifies plant disease detection but also charts a course towards sustainable agriculture. The study contributes actionable insights, envisioning a future where technological interventions harmonize with traditional farming practices, fostering resilience and optimizing crop yields to fulfill the evolving requirements of a growing populace.

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