The agricultural transformation in the last decade using artificial intelligence has led to
significant gains in productivity and profitability. The traditional machine learning
approaches present inherent limitations in extracting features and information from image
data. Deep learning techniques, particularly CNN's, help to overcome these limitations due
to their multi-level architecture. Various deep learning applications in agriculture include
crop disease identification, fruit classification, and germination rate monitoring. Seed image …