The development of methodology and techniques for crop disease identification

Abstract

In India, an estimated 15-25% of potential crop production is lost due pest and diseases (Roy and Bezbaruah, 2002). The country needs not only to raise production but also ensure food security for its growing consumption needs while curbing excessive pesticide usage. Detection of pests and diseases at an early stage plays a significant role in addressing the above-mentioned concerns and image classification offers a cost-effective and scalable solution to the disease detection problem (A. Ramcharan et al. 2017). Here, the principles of transfer learning are implemented with pretrained model – Resnet34 (K. He et al. 2015), and test its effectiveness in image classification using a dataset of tea leaves. The novelty of this work is that the images used are not curated, individual leaves with controlled backgrounds but of plants in-situ. The effect of the level of zoom and background is examined and class activation maps are used to validate that the basis of classification is indeed the disease and not an artificial bias from factors such as background, lighting etc.

Plant phenotyping relevance

茶葉の病害を植物画像から分類する方法を開発・検証しており、背景やズームの影響、クラス活性化マップによる妥当性確認も扱うため、病害状態のフェノタイピング手法が中心です。

Code and data availability

The supplied blocks describe a tea-leaf disease image dataset (~500 images/class) collected in-situ and a Resnet34 transfer-learning analysis, but contain no public deposit, availability statement, or URL for the images, trained model, or analysis code. The only mentioned dataset (Plant Village) is cited prior work, so

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