AI-SCAN: Advancing plant leaf disease detection with Transcendental Residual Convolutional Swin Transformer with hybrid optimizer

Abstract

Abstract Detecting diseases in plant leaves is very crucial for making sure that there is enough food worldwide and that we can continue with sustainable farming. Plant diseases are big threats to the output of crops, safety in food and economic security worldwide. The importance of finding and controlling these diseases quickly is very high to lessen their effect and maintain a good balance in farming methods. The main reason why this study started was because of how important it is to have precise and dependable methods for detecting diseases in plant leaves within the field of agriculture. The development of AI-driven Smart Detection for Plant Leaves (AI-SCAN) is a pliable and robust technique that will raise the reliability and accuracy of plant leaf disease diagnosis. The construction procedure of the recommended model highlights the fundamental technique employed in our proposed study. Here, the Transcendental Residual Convolutional Swin Transformer (TRCST) technique – which was invented lately and is exclusive, has been used to accurately identify the type of plant disease from the given images. Furthermore, Nomadic People’s most recent hybrid optimization model incorporates photon optimizer (NP2O) to fine-tune TRCST parameters, significantly improving AI-SCAN’s overall disease detection performance. The merging of TRCST with NP2O into AI-SCAN gives an original way that can be used for finding plant leaf disease, providing better trustworthiness, precision and speed when diagnosing sicknesses on plants. This method deals with the main issues linked to agriculture’s sustained capacity for producing food and defending crops against destructive diseases. The Plant Village, Cassava, and Jasmine datasets are three different publicly available, large-scale plant leaf datasets that were used to examine the results and implications of the proposed AI-SCAN.

Plant phenotyping relevance

植物葉画像から病害状態を推定するAI手法の開発と公開データセットでの評価が中心であり、植物フェノタイピング手法に該当する。

Code and data availability

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