Cassava Plant Leaf Disease Detection

Abstract

There are various machine learning algorithms being implemented across the agricultural domain as well as other computer vision domains for the image classification problems as well as object detection problems. These algorithms work on feature extraction from the images. One of the most used algorithms is Convolutional Neural Network (CNN), which helps in feature extraction. Another method which is currently ruling the realm of machine learning is transfer learning, where the knowledge gained by machine while learning to solve one problem is applied for solving another problem. This paper demonstrates how various CNN architectures and transfer learning techniques can be applied for the disease detection in cassava plant.

Plant phenotyping relevance

カッサバ葉の画像から植物の病害を検出するCNN・転移学習手法が研究の中心であり、植物の病態を直接推定するため対象範囲に含める。

Code and data availability

The paper uses the publicly available Kaggle cassava leaf disease dataset (Makerere AI lab / iCassava 2019), but this is a third-party dataset cited as prior work, not an authors' paper-specific asset. No author analysis code, trained model checkpoints, or data deposit with an authors' public URL is mentioned, and no k

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