Segmentation and Object Detection in Crop and Food Images

The fusion of computer vision techniques, comprising image segmentation and object detection, has really catalyzed the advancement in agriculture and food processing. Such methods allow the precise identification, classification, and quantification of crops, weeds, fruits, diseases, and food items in complex visual settings, thereby extending an automation and real-time monitoring angle. In agriculture, segmentation is used to distinguish crops from weeds or soil, while object detection helps with disease diagnosis, fruit counting, yield prediction, crop growth analysis, and so forth. Likewise, in the food system, these methods help in product grading, estimating ripeness, contaminant detection, and food classification, thereby improving safety and quality. Newer deep-learning models such as U-Net, Mask R-CNN, YOLO, and Vision Transformers (ViTs) fare better under difficult conditions with variable lighting, occlusion, and so on. Public datasets, including PlantVillage, DeepWeed, and Fruits 360, together with TensorFlow, PyTorch, and OpenCV, facilitate rapid development and deployment. Problems remain, with several attained, namely, data scarcity, generalization across two regions, explainability, and inference energy efficiency for edge devices. These issues must be addressed if deployment is to be offered on a large scale in rural areas and other resource-constrained locations. All agronomic and food automation-engineered basics are addressed for students, researchers, and practitioners.