This paper provides a comprehensive review of deep learning-based image segmentation techniques in agricultural applications. Image segmentation is crucial for various tasks in agriculture, including crop and soil monitoring, predicting optimal sowing, fertilizing, and harvesting times, estimating crop yield, and detecting plant diseases. However, challenges such as disease staging recognition, labeling inconsistency, and environmental changes pose significant difficulties. The authors categorize deep learning-based image segmentation solutions into eight main groups: encoder-decoder structures, multi-scale and pyramid-based methods, dilated convolutional networks, visual attention models, generative adversarial networks (GANs), graph neural networks (GNNs), instance segmentation networks, and transformer-based models. They also discuss the applications of these methods in agriculture, such as plant disease detection, weed identification, crop growth monitoring, yield estimation, and counting. Additionally, the paper reviews publicly available plant image segmentation datasets and evaluates the performance of image segmentation algorithms on benchmark datasets. Finally, it outlines future directions and challenges in image segmentation for agricultural applications.This paper provides a comprehensive review of deep learning-based image segmentation techniques in agricultural applications. Image segmentation is crucial for various tasks in agriculture, including crop and soil monitoring, predicting optimal sowing, fertilizing, and harvesting times, estimating crop yield, and detecting plant diseases. However, challenges such as disease staging recognition, labeling inconsistency, and environmental changes pose significant difficulties. The authors categorize deep learning-based image segmentation solutions into eight main groups: encoder-decoder structures, multi-scale and pyramid-based methods, dilated convolutional networks, visual attention models, generative adversarial networks (GANs), graph neural networks (GNNs), instance segmentation networks, and transformer-based models. They also discuss the applications of these methods in agriculture, such as plant disease detection, weed identification, crop growth monitoring, yield estimation, and counting. Additionally, the paper reviews publicly available plant image segmentation datasets and evaluates the performance of image segmentation algorithms on benchmark datasets. Finally, it outlines future directions and challenges in image segmentation for agricultural applications.