Abstract
<span>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.</span>
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