Diseases Identification Using ConvNet In Sugarcane Crops
Apsana M. Kotekan, Vidhyashri S. Kakaraddi, Anil R Jamakhandi
Abstract
Sugarcane is an essential crop worldwide, contributing significantly to the global economy. However, various diseases threaten sugarcane production, leading to substantial yield losses. Early detection and accurate identification of these diseases are crucial for effective disease management. This paper presents the automated recognition of sugarcane diseases using a deep learning method ConvNet (CNNs). The proposed model leverages the power of deep learning to analyze images of sugarcane leaves and classify them into different disease categories. The results demonstrate the efficiency of the deep learning model in accurately identifying sugarcane diseases, paving the way for improved disease management practices.