Pesticide Spraying Safety Detection Dataset

The agricultural field faces environmental pollution and food safety issues due to excessive pesticide use, posing serious threats to ecology and human health. Existing monitoring methods for pesticide spraying are mostly manual inspections, which are inefficient and prone to errors, failing to meet the needs of modern agriculture. This dataset aims to aid the development of automated spraying monitoring systems by providing high-quality target detection data, improving spraying safety and efficiency. The dataset includes images of various pesticide spraying scenes captured with high-resolution cameras under different lighting and weather conditions. To ensure data quality, multiple rounds of labeling and consistency checks were implemented, and each image was reviewed by experts. The data is uniformly stored in JPEG format and organized in a directory structure to facilitate subsequent processing and use. The core advantage of the dataset lies in its high labeling accuracy and consistency, with a labeling accuracy rate of over 95%, and the data samples cover various pesticides and spraying conditions, enhancing the model's generalization ability. An innovative labeling method combining deep learning technology and human-machine collaboration improved labeling efficiency by 30%. This dataset will effectively enhance the performance metrics of relevant detection systems and promote the development of agricultural safety monitoring technology.