Search Results (4)

Citrus fruits and related processed products represent a major agricultural sector worldwide, contributing to food supply chains and to regional economies, particularly in Mediterranean and subtropical areas. Citrus processing generates significant amounts of post-processing waste, and their sustainable management is a critical challenge, driving growing scientific interest in exploring environmentally sustainable and profitable valorisation strategies. This study aimed at mapping the sustainability of post-processing citrus valorisation strategies documented in the scientific literature, through a scoping literature review based on the PRISMA-ScR model. Only peer-reviewed studies in English were selected from Scopus and Web of Science; in detail, 29 life cycle assessment studies (LCAs) focusing on the valorisation of citrus by-products have been analysed. Most of the studies were focused on essential oil extraction and energy production. Most of the biorefinery systems and valorisation aims proposed were found to be better than the business-as-usual solution. However, results are strongly influenced by the functional unit and allocation method. Economic allocation to the main product resulted in better environmental performances. The major environmental hotspot is the agrochemicals required for crop management. The analysis of LCAs facilitated the identification of valorisation strategies that deserve greater interest from the scientific community to propose sustainable bio-circular solutions in the agro-industrial and agricultural sectors. Full article

With its unique climate and geographical advantages, Yunnan Province in China has become one of the country’s most important citrus-growing regions. However, the dense foliage and large fruit size of citrus trees often result in significant occlusion, and the fluctuating light intensity further complicates accurate assessment of fruit maturity. To address these challenges, this study proposes an improved model based on YOLOv8, named ORD-YOLO, for citrus fruit maturity detection. To enhance the model’s robustness in complex environments, several key improvements have been introduced. First, the standard convolution operations are replaced with Omni-Dimensional Dynamic Convolution (ODConv) to improve feature extraction capabilities. Second, the feature fusion process is optimized and inference speed is increased by integrating a Re-parameterizable Generalized Feature Pyramid Network (RepGFPN). Third, the detection head is redesigned using a Dynamic Head structure that leverages dynamic attention mechanisms to enhance key feature perception. Additionally, the loss function is optimized using InnerDIoU to improve object localization accuracy. Experimental results demonstrate that the enhanced ORD-YOLO model achieves a precision of 93.83%, a recall of 91.62%, and a mean Average Precision (mAP) of 96.92%, representing improvements of 4.66%, 3.3%, and 3%, respectively, over the original YOLOv8 model. ORD-YOLO not only maintains stable and accurate citrus fruit maturity recognition under complex backgrounds, but also significantly reduces misjudgment caused by manual assessments. Furthermore, the model enables real-time, non-destructive detection. When deployed on harvesting robots, it can substantially increase picking efficiency and reduce post-maturity fruit rot due to delayed harvesting. These advancements contribute meaningfully to the quality improvement, efficiency enhancement, and digital transformation of the citrus industry. Full article

The citrus picking-point localization is critical for automatic citrus harvesting. Due to the complex citrus growing environment and the limitations of devices, the efficient citrus picking-point localization method becomes a hot research topic. This study designs a novel and efficient workflow for citrus picking-point localization, named as CPPL. The CPPL is achieved based on two stages, namely the detection stage and the segmentation stage. For the detection stage, we define the KD-YOLOP to accurately detect citrus fruits to quickly localize the initial picking region. The KD-YOLOP is defined based on a knowledge distillation learning and a model pruning to reduce the computational cost while having a competitive accuracy. For the segmentation stage, we define the RG-YOLO-seg to efficiently segment the citrus branches to compute the picking points. The RG-YOLO-seg is proposed by introducing the RGNet to extract efficient features and using the GSNeck to fuse multi-scale features. Therefore, by using knowledge distillation, model pruning, and a lightweight model for branch segmentation, the proposed CPPL achieves accurate real-time localization of citrus picking points. We conduct extensive experiments to evaluate our method; many results show that the proposed CPPL outperforms the current methods and achieves adequate accuracy. It provides an efficient and robust novel method for real-time citrus harvesting in practical agricultural applications. Full article

Citrus is an important commercial crop in many areas. The management and planning of citrus growing can be supported by timely and efficient monitoring of citrus-growing regions. Their complex planting structure and the weather are likely to cause problems for extracting citrus-growing regions from remote sensing images. To accurately extract citrus-growing regions, deep learning is employed, because it has a strong feature representation ability and can obtain rich semantic information. A novel model for extracting citrus-growing regions by UNet that incorporates an image pyramid structure is proposed on the basis of the Sentinel-2 satellite imagery. A pyramid-structured encoder, a decoder, and multiscale skip connections are the three main components of the model. Additionally, atrous spatial pyramid pooling is used to prevent information loss and improve the ability to learn spatial features. The experimental results show that the proposed model has the best performance, with the precision, the intersection over union, the recall, and the F1-score reaching 88.96%, 73.22%, 80.55%, and 84.54%, respectively. The extracted citrus-growing regions have regular boundaries and complete parcels. Furthermore, the proposed model has greater overall accuracy, kappa, producer accuracy, and user accuracy than the object-oriented random forest algorithm that is widely applied in various fields. Overall, the proposed method shows a better generalization ability, higher robustness, greater accuracy, and less fragmented extraction results. This research can support the rapid and accurate mapping of large-scale citrus-growing regions. Full article