AgroSegNet: Semantic Segmentation Guided Crop Image Extraction using Enhanced Mask RCNN

Abstract

Traditional crop image segmentation methods often struggle to accurately extract crops due to the complex interplay of agricultural factors and environmental conditions. In this study, AgroSegNet, an innovative approach that integrates agronomic knowledge into the segmentation process to improve crop extraction accuracy, is proposed. Leveraging the Mask RCNN framework, the method dynamically adapts region proposals based on crop growth stages, phenological information, and agronomic principles. Initializing the Fruits 360 Dataset labels using LabelMe and preprocessing the dataset, we then divide it into training and test sets. Constructing an enhanced Mask RCNN model using the PyTorch 2.0 deep learning framework, our model incorporates path aggregation, feature augmentation, and an optimized region extraction network, bolstered by a feature pyramid network. Spatial information preservation is achieved using bilinear interpolation in ROIAlign. A multi-scale feature fusion mechanism is introduced to capture fine-grained details essential for precise segmentation. Additionally, domain adaptation techniques are employed to enhance model generalization across diverse agricultural environments. Through extensive experimentation and comparative analysis, AgroSegNet demonstrates superior performance in terms of precision, recall, average precision, mean average precision, and F1 scores for crop image extraction compared to traditional methods. The approach not only advances the state-of-the-art in crop segmentation but also offers valuable insights for leveraging agronomic knowledge in computer vision applications for agriculture