Published: 11 August 2021| Version 1 | DOI: 10.17632/g8fhf7fbds.1


Thailand’s Economic Crops Aerial Image Dataset We introduce the novel economic crops aerial image dataset, namely the EcoCropsAID dataset. This dataset was collected in Thailand from five economic crops that were cultivated in different provinces and regions between 2014 and 2018. The aerial images of economic crops were gathered based on Agri-Map Online provided by the Ministry of Agriculture and Cooperatives and the National Electronics and Computer Technology Center (NECTEC). The Agri-Map Online is an agriculture map that all departments under the Ministry of Agriculture and Cooperatives use as an agriculture management tool. Subsequent agricultural information is accurate and up-to-date. Then, the Google Earth application was employed to capture aerial images after we selected the economic crops areas in which images were to be collected. It is quite a complex dataset because the Google Earth program used several remote imaging sensors to record the aerial images. The EcoCropsAID dataset includes five categories (rice, sugarcane, cassava, rubber, and longan) and contains 5,400 images. Each class has around 1,000 images. To prepare the aerial images of the economic crops, we recorded the image with 600 × 600 pixels and stored it in the RGB color format. The challenges of classification on the EcoCropsAID dataset are 1) many different image resolutions and colors are contained in the EcoCropsAID dataset due to the various remote imaging sensors, 2) the similarity of patterns amongst each class, for example, longan and rubber, and 3) the difference of pattern inside the same class, for example, cassava and rice.



Mahasarakham University


Computer Vision, Image Classification, Convolutional Neural Network