Detection of Cassava Plant Disease using Deep Transfer Learning Approach

Abstract

Small-scale farmers use cassava as an essential crop for food and nutrition security, owing to its capacity to flourish under adverse situations. In numerous African countries, it serves as a significant source of carbohydrates. Leaf diseases can sometimes damage cassava crops, constraining overall output and farmers' revenue. The ongoing research on cassava disease is fraught with challenges, including a low detection rate, extended processing time, and inadequate precision. This research employs deep transfer learning with Visual Geometry Group (VGG16) models for the diagnosis of Cassava leaf diseases. An experimental study is conducted on a dataset of 5,656 images of cassava, categorized into four distinct disease classifications. Two of the most sophisticated predictions were generated: one identified the healthy leaf, while the other detected the diseases present on the unhealthy leaf. Our suggested deep transfer learning model attains a promising accuracy of 88% and an F1-score of 82% on the public plant disease dataset from the Kaggle repository, achieved through effective hyperparameter fine-tuning. The results of this study strongly advocate for additional research and practical application of deep learning models in plant disease diagnostics.

Plant phenotyping relevance

カッサバ葉の画像から健全・罹病状態を分類する深層転移学習手法が研究の中心であり、植物病害状態の画像ベース表現型推定に該当する。

Code and data availability

The paper states its cassava leaf disease image dataset (5,656 images) came from a Kaggle public repository, but the 'Availability of Data and Material' section links to a healthcare stroke dataset, which is unrelated to the paper's plant-phenotyping data. No authors' code, models, or correct dataset URL are provided,故

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