Detection of cotton crops diseases using customized deep learning model.

Abstract

The agricultural industry is experiencing revolutionary changes through the latest advances in artificial intelligence and deep learning-based technologies. These powerful tools are being used for a variety of tasks including crop yield estimation, crop maturity assessment, and disease detection. The cotton crop is an essential source of revenue for many countries highlighting the need to protect it from deadly diseases that can drastically reduce yields. Early and accurate disease detection is quite crucial for preventing economic losses in the agricultural sector. Thanks to deep learning algorithms, researchers have developed innovative disease detection approaches that can help safeguard the cotton crop and promote economic growth. This study presents dissimilar state-of-the-art deep learning models for disease recognition including VGG16, DenseNet, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet models. For this purpose, real cotton disease data is collected from fields and preprocessed using different well-known techniques before using as input to deep learning models. Experimental analysis reveals that the ResNet152 model outperforms all other deep learning models, making it a practical and efficient approach for cotton disease recognition. By harnessing the power of deep learning and artificial intelligence, we can help protect the cotton crop and ensure a prosperous future for the agricultural sector.

Plant phenotyping relevance

綿花の病害症状を画像から認識する深層学習手法の比較・評価が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採用。

Code and data availability

The paper describes a self-collected cotton disease image dataset (5,600 images from South Punjab, Pakistan) and training of multiple CNN models, but no supplied block contains any public deposit, repository URL, code availability statement, or data availability statement for the dataset, images, code, or trained model

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