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crop yield and quality, especially powdery mildew, which has a high incidence in various plants. Traditional manual detection methods are inefficient, easily affected by human factors, and cannot meet the needs of large-scale farm real-time monitoring. Existing machine learning algorithms lack accuracy in detecting powdery mildew and cannot effectively distinguish disease characteristics at different stages. This dataset aims to provide high-quality image samples for agricultural intelligence, with a focus on improving the precision and speed of powdery mildew detection.Data collection was done using high-resolution cameras, selecting samples from multiple plants collected under different times and lighting conditions, ensuring diversity and representativeness of the samples. Quality control of the data is ensured through multiple rounds of labeling and consistency checks, with the labeling team consisting of agricultural experts with a pathological background, totaling over 20 people. Data preprocessing includes image enhancement, noise reduction, and color correction steps to ensure the stability and accuracy of the data. All data is stored in JPG format, organized in a two-tier directory structure based on plant species and disease location.The core advantages of the dataset are manifested in several aspects. Firstly, the labeling accuracy is above 95%, and the labeling consistency assured by expert review exceeds 90%. In terms of technological innovation, new image enhancement techniques have been introduced to improve model robustness under different lighting conditions. The application value lies in being able to improve the accuracy of powdery mildew detection models by 15%, significantly enhancing the performance of agricultural disease automatic detection systems. Compared to similar datasets, this dataset has outstanding advantages in diversity and labeling accuracy and covers rare early symptoms of powdery mildew, making it extremely scarce. The dataset is well structured to support the expansion of different diseases and general applications.","2026-02-04 16:41:39","2026-10-19 17:19:54","0.00",581,500,0,"powdery mildew identification, agricultural disease detection dataset, plant health monitoring","High-quality image dataset for identifying and detecting plant powdery mildew, assisting in agricultural intelligence.","JPG","1.6G","pest and disease detection, agricultural production management, plant health monitoring","image classification, object detection, disease identification","V1.0","sample\u002F897\u002Fe120652a14c07e551fcc961b6c45ddc4.zip","records","https:\u002F\u002Fstatic.mobiusi.com\u002Fmanual_sorting_data\u002F20260202\u002Fe0a14fc690422e80cb38bbfe76bac270.jpg?Expires=1792484992&OSSAccessKeyId=LTAI5tH8MQqhgeiAtrzCHrxM&Signature=di%2BOU9dPIAItmmJPE86nf1Naf9U%3D&x-oss-process=image%2Fresize%2Cm_fill%2Cw_500%2Ch_250",[586,591,594,599,605,611,616,621,626],{"id":574,"name":587,"field":588,"intro":589,"type":590},"File Name","file_name","File name","string",{"id":574,"name":592,"field":593,"intro":592,"type":590},"Resolution","quality",{"id":595,"name":596,"field":597,"intro":598,"type":590},6412,"Disease Severity","disease_severity","Describes the severity of the plant's infection with powdery mildew.",{"id":600,"name":601,"field":602,"intro":603,"type":604},6413,"Leaf Affected Area","leaf_affected_area","Shows the percentage of the leaf area affected by powdery mildew.","float",{"id":606,"name":607,"field":608,"intro":609,"type":610},6414,"Infected Leaf Count","infected_leaf_count","Number of leaves infected with powdery mildew in the image.","int",{"id":612,"name":613,"field":614,"intro":615,"type":590},6415,"Symptom Color","symptom_color","The predominant color of the powdery mildew spots in the image.",{"id":617,"name":618,"field":619,"intro":620,"type":590},6416,"Host Species","host_species","The species of the host plant infected with powdery mildew.",{"id":622,"name":623,"field":624,"intro":625,"type":604},6417,"Image Brightness","image_brightness","Overall brightness value of the image.",{"id":627,"name":628,"field":629,"intro":630,"type":590},6418,"Leaf Texture","leaf_texture","Description of the leaf's texture features in the image.",[632,636,640,644,648,652],{"id":633,"question":634,"answer":635},4619,"Which plants is the Garden Powdery Mildew Identification Image Dataset suitable for?","This dataset is mainly suitable for the identification of powdery mildew in various horticultural plants.",{"id":637,"question":638,"answer":639},4620,"How to use the Garden Powdery Mildew Identification Image Dataset to improve model accuracy?","By training and validating machine learning models to identify powdery mildew spots, the accuracy of the recognition models can be improved.",{"id":641,"question":642,"answer":643},4621,"How many images are there in the Garden Powdery Mildew Identification Image Dataset?","The dataset contains thousands of high-quality images for powdery mildew identification.",{"id":645,"question":646,"answer":647},4622,"What is the data format of the Garden Powdery Mildew Identification Image Dataset?","The data is usually stored in JPEG or PNG format, which is convenient for various image processing and model training applications.",{"id":649,"question":650,"answer":651},4623,"What are the main application areas of the Garden Powdery Mildew Identification Image Dataset?","This dataset is mainly used for crop disease detection systems and pest management in horticulture.",{"id":653,"question":654,"answer":655},4624,"What is the importance of this dataset for the agriculture, forestry, and fishery industries?","By providing accurate disease identification, it helps to improve crop yields and ensure the healthy development of agriculture, forestry, and fishery 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