Update Benchmark/Classification-Wheat Disease/readme.txt

Benchmark/Classification-Wheat Disease/readme.txt

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-Wheat Disease Classification. 
-The second task is wheat disease classification. We collected images from five public datasets of CerealConv, WFD, AWDD, MSWDD, and LWDCD, to construct a comprehensive dataset of healthy wheat and other eight disease types including Brownrust, Mildew, Septoria, Yellowrust, Stemrust, Healthy, Wheatscab, and Yellowdwarf, with 4000 images in total. These images were obtained from multiple countries in Europe, North America, and Asia between 2019 and 2024, reflecting the sensitivity and resistance of different wheat varieties to diseases and covering images of different varieties, periods, and environmental conditions. Based on this dataset, we established the Wheat Disease Classification benchmark, partitioned into 320 training samples and 3,680 testing samples. The identical random five-fold sampling trials were conducted to ensure statistical soundness.
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-Long, M., Hartley, M., Morris, R. J. & Brown, J. K. M. Classification of wheat diseases using deep learning networks with field and glasshouse images. Plant Pathol. 72, 536–547 (2023).
-Genaev, M. A. et al. Image-based wheat fungi diseases identification by deep learning. Plants 10, 1500 (2021).
-Safarijalal, B., Alborzi, Y. & Najafi, E. Automated Wheat Disease Detection using a ROS-based Autonomous Guided UAV. Preprint at http://arxiv.org/abs/2206.15042 (2022).
-Mao, R. et al. DAE-Mask: a novel deep-learning-based automatic detection model for in-field wheat diseases. Precis. Agric. 25, 785–810 (2024).
-Goyal, L., Sharma, C. M., Singh, A. & Singh, P. K. Leaf and spike wheat disease detection & classification using an improved deep convolutional architecture. Inform. Med. Unlocked 25, 100642 (2021).
+Wheat Disease Classification. 
+We collected images from five public datasets of CerealConv, WFD, AWDD, MSWDD, and LWDCD, to construct a comprehensive dataset of healthy wheat and other eight disease types including Brownrust, Mildew, Septoria, Yellowrust, Stemrust, Healthy, Wheatscab, and Yellowdwarf, with 4000 images in total. These images were obtained from multiple countries in Europe, North America, and Asia between 2019 and 2024, reflecting the sensitivity and resistance of different wheat varieties to diseases and covering images of different varieties, periods, and environmental conditions. Based on this dataset, we established the Wheat Disease Classification benchmark, partitioned into 320 training samples and 3,680 testing samples. The identical random five-fold sampling trials were conducted to ensure statistical soundness.
+
+Long, M., Hartley, M., Morris, R. J. & Brown, J. K. M. Classification of wheat diseases using deep learning networks with field and glasshouse images. Plant Pathol. 72, 536–547 (2023).
+Genaev, M. A. et al. Image-based wheat fungi diseases identification by deep learning. Plants 10, 1500 (2021).
+Safarijalal, B., Alborzi, Y. & Najafi, E. Automated Wheat Disease Detection using a ROS-based Autonomous Guided UAV. Preprint at http://arxiv.org/abs/2206.15042 (2022).
+Mao, R. et al. DAE-Mask: a novel deep-learning-based automatic detection model for in-field wheat diseases. Precis. Agric. 25, 785–810 (2024).
+Goyal, L., Sharma, C. M., Singh, A. & Singh, P. K. Leaf and spike wheat disease detection & classification using an improved deep convolutional architecture. Inform. Med. Unlocked 25, 100642 (2021).