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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mondhe, Parag Jayant
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Computer Science & IT Engineering

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Encoder-decoder approach for describing health of cauliflower plant in multiple languages Mondhe, Parag Jayant; Satone, Manisha P.; Wasatkar, Namrata N.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2971-2977

Abstract

Physically examining each plant to determine its state of health and determining the disease if plant is affected due to it, is challenging. The encoder - decoder approach is proposed for describing health of cauliflower plant in English, Hindi and Marathi languages from aerial images. Experiments are performed with different CNN models and LSTM combinations. The Multilanguage Cauliflower Captions Dataset (MCCD) is developed to evaluate the performance of the model. The dataset contains 1213 images where each image is described in 3 different languages. The dataset contains images of cauliflower plant affected due to bacterial spot rot, black rot and downy mildew diseases. It also contains images of healthy plant. The objective metrics such as BLEU scores and subjective criteria are used to decide the quality of the generated description.
Co-Authors Satone, Manisha P. Wasatkar, Namrata N.