Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture. Therefore, this paper provides a comprehensive review of CNNs in computer vision for grain crop phenotyping. It is meaningful to provide a review as a roadmap for future research in such a thriving research area. The CNN models (e.g., VGG, YOLO, and Faster R-CNN) used CV tasks including image classification, object detection, semantic segmentation, and instance segmentation, and the main results of recent studies on crop phenotype detection are discussed and summarized. Additionally, the challenges and future trends of the phenotyping techniques in grain crops are presented.

Fusarium has become a major impediment to stable wheat production in many regions worldwide. Infected wheat plants not only experience reduced yield and quality but their spikes generate toxins that pose a significant threat to human and animal health. Currently, there are two primary methods for effectively controlling Fusarium head blight (FHB): spraying quantitative chemical agents and breeding disease-resistant wheat varieties. The premise of both methods is to accurately diagnosis the severity of wheat FHB in real time. In this study, a deep learning-based multi-model fusion system was developed for integrated detection of FHB severity. Combination schemes of network frameworks and backbones for wheat spike and spot segmentation were investigated. The training results demonstrated that Mobilev3-Deeplabv3+ exhibits strong multi-scale feature refinement capabilities and achieved a high segmentation accuracy of 97.6% for high-throughput wheat spike images. By implementing parallel feature fusion from high- to low-resolution inputs, w48-Hrnet excelled at recognizing fine and complex FHB spots, resulting in up to 99.8% accuracy. Refinement of wheat FHB grading classification from the perspectives of epidemic control (zero to five levels) and breeding (zero to 14 levels) has been accomplished. In addition, the effectiveness of introducing HSV color feature as a weighting factor into the evaluation model for grading of wheat spikes was verified. The multi-model fusion algorithm, developed specifically for the all-in-one process, successfully accomplished the tasks of segmentation, extraction, and classification, with an overall accuracy of 92.6% for FHB severity grades. The integrated system, combining deep learning and image analysis, provides a reliable and nondestructive diagnosis of wheat FHB, enabling real-time monitoring for farmers and researchers.