Search Results (4)

Precisely identifying and delineating food regions automatically from images, a task known as food image segmentation, is crucial for enabling applications in food science such as automated dietary logging, accurate nutritional analysis, and food safety monitoring. However, accurately segmenting food images, particularly delineating food edges with precision, remains challenging due to the wide variety and diverse forms of food items, frequent inter-food occlusion, and ambiguous boundaries between food and backgrounds or containers. To overcome these challenges, we proposed a novel method called the Multi-view Edge Attention Network (MVEANet), which focuses on enhancing the fine-grained segmentation of food edges. The core idea behind this method is to integrate information obtained from observing food from different perspectives to achieve a more comprehensive understanding of its shape and specifically to strengthen the processing capability for food contour details. Rigorous testing on two large public food image datasets, FoodSeg103 and UEC-FoodPIX Complete, demonstrates that MVEANet surpasses existing state-of-the-art methods in segmentation accuracy, performing exceptionally well in depicting clear and precise food boundaries. This work provides the field of food science with a more accurate and reliable tool for automated food image segmentation, offering strong technical support for the development of more intelligent dietary assessment, nutritional research, and health management systems. Full article

Winter wheat is one of the major crops for global food security. Accurate statistics of its planting area play a crucial role in agricultural policy formulation and resource management. However, the existing semantic segmentation methods for remote sensing images are subjected to limitations in dealing with noise, ambiguity, and intra-class heterogeneity, posing a negative impact on the segmentation performance of the spatial distribution and area of winter wheat fields in practical applications. In response to the above challenges, we proposed an improved HRNet-based semantic segmentation model in this paper. First, this model incorporates a semantic domain module (SDM), which improves the model’s precision of pixel-level semantic parsing and reduces the interference from noise through multi-confidence scale class representation. Second, a nested attention module (NAM) is embedded, which enhances the model’s capability of recognizing correct correlations in pixel classes. The experimental results show that the proposed model achieved a mean intersection over union (mIoU) of 80.51%, a precision of 88.64%, a recall of 89.14%, an overall accuracy (OA) of 90.12%, and an F1-score of 88.89% on the testing set. Compared to traditional methods, our model demonstrated better segmentation performance in winter wheat semantic segmentation tasks. The achievements of this study not only provide an effective tool and technical support for accurately measuring the area of winter wheat fields, but also have important practical value and profound strategic significance for optimizing agricultural resource allocation and achieving precision agriculture. Full article

As a prominent topic in food computing, cross-modal recipe retrieval has garnered substantial attention. However, the semantic alignment across food images and recipes cannot be further enhanced due to the lack of intra-modal alignment in existing solutions. Additionally, a critical issue named food image ambiguity is overlooked, which disrupts the convergence of models. To these ends, we propose a novel Multi-Modal Alignment Method for Cross-Modal Recipe Retrieval (MMACMR). To consider inter-modal and intra-modal alignment together, this method measures the ambiguous food image similarity under the guidance of their corresponding recipes. Additionally, we enhance recipe semantic representation learning by involving a cross-attention module between ingredients and instructions, which is effective in supporting food image similarity measurement. We conduct experiments on the challenging public dataset Recipe1M; as a result, our method outperforms several state-of-the-art methods in commonly used evaluation criteria. Full article

This paper on family farms is in the form of an historical review complemented by current and future perspectives from North America, China, Brazil and Europe. The literature review demonstrates the multiple discourses, concepts and methodologies which underpin contemporary understandings of the family farm. The authors argue that family-based farming units are ubiquitous in most agricultural systems and take on many different forms and functions, conditioned by the structure of agriculture in different locations and political systems. Our review accepts this diversity and seeks to identify some key elements that inform our understanding of the sustainability of family farming, now and in the future. The term ‘family’ is the differentiating variable and behooves a sociological approach. However, economists can view the family farm as an economic unit, a business and even a firm. Geographers see family farms consigned to the margins of good land areas, and political scientists have seen family farms as a class. What emerges is a semantic enigma. As an imaginary term, ‘family farming’ is useful as a positive, universally valued ideal; as a definable entity on the ground, however, it is difficult to classify and measure for comparative policy and research purposes. This ambiguity is utilized by governments to manage the increasing capitalization of farm units while projecting the image of wholesome production of food. The case studies demonstrate the diversity of ways in which family farming ideologies are being mobilized in contemporary agrarian change processes. The notion of ‘land to the tiller’ is resonant with historic injustices in Scotland and Brazil, where family-based agriculture is understood as the ‘natural’ order of agricultural production and actively supported as an historic ideal. In contrast, in the Chinese context, ‘land to the tiller’ is a political means of increasing capital penetration and economic sustainability. Evidence from China, Brazil and Scotland demonstrates the active role of governments, coupled with symbolic ideologies of farming, which suggest that the longevity (i.e., sustainability) of family farming will continue. Full article