An Efficient Method for Counting Large-Scale Plantings of Transplanted Crops in UAV Remote Sensing Images

Counting the number of transplanted crops is a crucial link in agricultural production, serving as a key method to promptly obtain information on crop growth conditions and ensure the yield and quality. The existing counting methods primarily rely on manual counting or estimation, which are inefficient, costly, and difficult to evaluate statistically. Additionally, some deep-learning-based algorithms can only crop large-scale remote sensing images obtained by Unmanned Aerial Vehicles (UAVs) into smaller sub-images for counting. However, this fragmentation often leads to incomplete crop contours of some transplanted crops, issues such as over-segmentation, repeated counting, low statistical efficiency, and also requires a significant amount of data annotation and model training work. To address the aforementioned challenges, this paper first proposes an effective framework for farmland segmentation, named MED-Net, based on DeepLabV3+, integrating MobileNetV2 and Efficient Channel Attention Net (ECA-Net), enabling precise plot segmentation. Secondly, color masking for transplanted crops is established in the HSV color space to further remove background information. After filtering and denoising, the contours of transplanted crops are extracted. An efficient contour filtering strategy is then applied to enable accurate counting. This paper conducted experiments on tobacco counting, and the experimental results demonstrated that the proposed MED-Net framework could accurately segment farmland in UAV large-scale remote sensing images with high similarity and complex backgrounds. The contour extraction and filtering strategy can effectively and accurately identify the contours of transplanted crops, meeting the requirements for rapid and accurate survival counting in the early stage of transplantation.