Generating captions in English and Marathi language for describing health of cotton plant

Abstract

Humans’ basic needs include food, shelter, and clothing. Cotton is the foundation of the textile industry. It is also one of the most profitable non-food crops for farmers around the world. Different diseases have a significant impact on cotton yield. Cotton plant leaves are adversely affected by aphids, army worms, bacterial blight, powdery mildew, and target spots. This paper proposes an encoder decoder model for generating captions in English and Marathi language to describe health of cotton plant from aerial images. The cotton disease captions dataset (CDCD) was developed to assess the effectiveness of the proposed approach. Experiments were conducted using various convolutional neural network (CNN) models, such as VGG-19, InceptionResNetV2, and EfficientNetV2L. The quality of generated caption is evaluated on BiLingual evaluation understudy (BLEU) metrics and using subjective criteria. The results obtained for captions generated in English and Marathi language are comparable. The network combination of EfficientNetV2L and long short-term memory (LSTM) has outperformed the other combinations.

Plant phenotyping relevance

綿花の航空画像から植物の健康状態・病害を推定して説明文を生成する画像解析手法とデータセットを開発・評価しており、フェノタイピング手法が中心である。

Code and data availability

The paper's own cotton disease captions dataset (CDCD) is described in detail but no public deposit, availability statement, or authors' URL is provided for it. The two Kaggle datasets cited (Dhamodharan [23], Bhoi [24]) are prior-work source datasets contributing only portions of CDCD, not the paper's bilingual (Mar/英

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