Sugarcane Crop Disease Detection

Abstract

Sugarcane is the crucial crop in the world, and the many diseases are impacted on this crop. Early disease detection of the crop is the important for the preventing losses of the yield. This research proposes a deep learning based approach for the detecting diseases of the sugarcane using the DenseNet and Sequential models. This pre-trained model uses the convolutional neural networks (CNNs) to extract features from the sugarcane images and classify them into the different diseases based on their features. The Sequential model achieves the high accuracy i.e. 94% while the DenseNet achieves the 75% accuracy. These result shows that this models can effectively detect the diseases of the sugarcane crop which is helpful for the preventing the disease spread and the reduce the yield losses. Sugarcane is a vital crop worldwide, and its production is severely impacted by various diseases. Early detection of these diseases is crucial for preventing significant yield losses. This research proposes a deep learning-based approach for detecting sugarcane crop diseases using DenseNet and Sequential models. The proposed models utilize convolutional neural networks (CNNs) to extract features from sugarcane images and classify them into different disease categories. The DenseNet model achieves a high accuracy of 75%, while the Sequential model attains an accuracy of 94%. The results demonstrate that the proposed models can effectively detect sugarcane crop diseases, enabling farmers and agricultural experts to take timely measures to prevent disease spread and reduce yield losses. This research contributes to the development of precision agriculture techniques, promoting sustainable and efficient sugarcane production.

Plant phenotyping relevance

サトウキビ画像から病害状態を推定する深層学習手法が研究の中心であり、植物病害フェノタイピングに該当する。

Code and data availability

The paper describes a privately collected sugarcane disease image dataset (Panhala Taluka, Kolhapur) and DenseNet/Sequential models, but provides no public deposit, repository, or availability statement for the dataset, images, code, or trained models. The 'Sugarcane Leaf Dataset' mentioned is cited prior work (Thite 4

No evidence-backed public reproduction asset is currently recorded.