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This breakthrough technology, based on Transformer architecture, offers a solution to challenges in identifying irregular disease patterns and complex backgrounds in rice cultivation. With its promise of higher precision, AISOA-SSformer could become a valuable tool for farmers and agricultural experts, contributing to better crop health and management.

Rice is one of the world's most essential food crops, but its production is constantly threatened by leaf diseases caused by pathogens like fungi, bacteria, and viruses. These diseases, which manifest as spots or blotches on leaves, can severely impact crop health and yield. Traditional manual identification of such diseases is labor-intensive and error-prone. The advent of deep learning-based segmentation technologies has brought improvements, but current methods often struggle with irregular disease features, complex backgrounds, and blurred boundaries in leaf images.

A published in  on 5 August 2024, helps farmers make better decisions, resulting in healthier crops and higher yields while reducing environmental impact.

The AISOA-SSformer model introduces several new components to enhance performance in rice leaf disease segmentation. The team implemented the model using PyTorch 1.10.0 to ensure consistency across experiments. The Sparse Global-Update Perceptron (SGUP) is employed to stabilize the learning process, effectively capturing the irregular features of leaf diseases. Additionally, a Salient Feature Attention Mechanism (SFAM) is incorporated to help the model filter out background noise while focusing on important features. This is achieved through two key modules: the Spatial Reconstruction Module (SRM) and the Channel Reconstruction Module (CRM), which work together to separate and optimize disease features.

To further enhance accuracy, the model uses the Annealing-Integrated Sparrow Optimization Algorithm (AISOA), which adjusts the training process to avoid being trapped in local optima and improve the recognition of fuzzy leaf edges. Compared to existing models, AISOA-SSformer achieved an impressive 83.1% mean intersection over union (MIoU), an 80.3% Dice coefficient, and a recall of 76.5%, making it one of the most accurate methods for segmenting rice leaf diseases. Ablation studies confirmed the combined impact of SGUP, SFAM, and AISOA, boosting the MIoU and Dice coefficients significantly. Comparative analyses with established models, including CNN-based and Transformer architectures, highlighted AISOA-SSformer’s superior ability to segment rice leaf diseases, even in complex environments, emphasizing its potential for practical agricultural applications.

The AISOA-SSformer model represents a breakthrough in precision agriculture, providing an advanced tool for identifying rice leaf diseases. By enhancing segmentation accuracy and addressing complex backgrounds and irregular disease patterns, this model has the potential to revolutionize crop disease management. In the future, it could be applied to other crops and agricultural challenges, making significant contributions to sustainable farming and food security.

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Funding information

This work was supported by the Changsha Municipal Natural Science Foundation (grant no. kq2014160); in part by the National Natural Science Foundation in China (grant no. 61703441); in part by the Key Projects of the Department of Education, Hunan Province (grant no. 19A511); in part by the Hunan Key Laboratory of Intelligent Logistics Technology (grant no. 2019TP1015); and in part by the National Natural Science Foundation of China (grant no. 61902436).

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Science Partner Journal  is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.