Fennel Crop Growth Monitoring Dataset

Currently, the agricultural sector faces challenges such as the lack of high-precision data and real-time monitoring in crop growth monitoring. Traditional monitoring methods often rely on manual observation, which is inefficient and prone to errors. Existing solutions are limited in terms of data update speed and accuracy, failing to meet the precision and timeliness demands of modern agriculture. The construction of this dataset aims to solve the problems of image recognition and classification in object detection by providing high-quality fennel crop growth monitoring data, meeting the data needs in agricultural production. Data collection uses drone aerial photography techniques with high-resolution cameras, capturing images at different growth stages and under different environmental conditions. To ensure data quality, multiple rounds of annotation and consistency checks were implemented, and the data was reviewed by agricultural experts. The data is stored in JPG format, organized by time and growth stage, to facilitate subsequent analysis and model training.