This paper provides a comprehensive review of deep learning-based image segmentation techniques in agricultural applications. Image segmentation is a critical task in computer vision that divides images into multiple segments and objects. In agriculture, it is used for crop and soil monitoring, predicting optimal sowing, fertilizing, and harvesting times, estimating crop yields, and detecting plant diseases. However, image segmentation in agriculture faces challenges such as disease stage recognition, labeling inconsistency, and changes in plant morphology due to environmental factors. The paper categorizes 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, graph neural networks, instance segmentation networks, and transformer-based models. It also discusses the applications of image segmentation in agriculture, such as plant disease detection, weed identification, crop growth monitoring, and yield estimation. The paper reviews publicly available plant image segmentation datasets and evaluates the performance of image segmentation algorithms on benchmark datasets. It also discusses the challenges and future prospects of image segmentation in agriculture. The paper highlights the importance of deep learning in improving the accuracy and robustness of image segmentation in agriculture, and discusses various deep learning network architectures, including convolutional neural networks, generative adversarial networks, graph neural networks, and transformers. It also discusses instance segmentation networks, generative models, and adversarial training. The paper concludes that deep learning-based image segmentation has significant potential in agriculture, but challenges such as model generalization and dataset-specific problems remain. The paper also discusses attention-based models and their role in improving image segmentation in agriculture.This paper provides a comprehensive review of deep learning-based image segmentation techniques in agricultural applications. Image segmentation is a critical task in computer vision that divides images into multiple segments and objects. In agriculture, it is used for crop and soil monitoring, predicting optimal sowing, fertilizing, and harvesting times, estimating crop yields, and detecting plant diseases. However, image segmentation in agriculture faces challenges such as disease stage recognition, labeling inconsistency, and changes in plant morphology due to environmental factors. The paper categorizes 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, graph neural networks, instance segmentation networks, and transformer-based models. It also discusses the applications of image segmentation in agriculture, such as plant disease detection, weed identification, crop growth monitoring, and yield estimation. The paper reviews publicly available plant image segmentation datasets and evaluates the performance of image segmentation algorithms on benchmark datasets. It also discusses the challenges and future prospects of image segmentation in agriculture. The paper highlights the importance of deep learning in improving the accuracy and robustness of image segmentation in agriculture, and discusses various deep learning network architectures, including convolutional neural networks, generative adversarial networks, graph neural networks, and transformers. It also discusses instance segmentation networks, generative models, and adversarial training. The paper concludes that deep learning-based image segmentation has significant potential in agriculture, but challenges such as model generalization and dataset-specific problems remain. The paper also discusses attention-based models and their role in improving image segmentation in agriculture.