Spatial Distribution and Driving Forces of the Vegetable Industry in China

Wang, Hongru; Jun, He; Aziz, Noshaba; Wang, Yue

doi:10.3390/land11070981

Cited by 15 publications

(7 citation statements)

References 36 publications

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“…Increase income, increase the strength of policies to benefit farmers. According to relevant statistics, China's mustard planting area is about 100hm2 [5].The output of mustard has reached 4500t, and the value of mustard has reached 200 billion RMB [6]. From this, we can see that mustard has a more important position in our people's daily life, at the same time, mustard because of its own characteristics, has become a powerful assistant for the development of agricultural innovation.…”

Section: Introductionmentioning

confidence: 99%

Mustard Agricultural Insurance Product Design in the Context of Common Wealth: Taking Zhejiang Province as An Example

Hong,

Lu,

2024

Int. J. Res. Publ. Rev.

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Capsicum agricultural insurance is a new type of product to protect farmers' income in recent years, food security is vital to the stability of national security, farmers' economy is also a factor that can not be ignored in the sustainable development of China's economy. At this stage, all kinds of agricultural insurance emerges, the heat is rising, but its system, mode, mechanism is still to be improved, only in the embryonic stage. This paper is based on literature research, through the analysis of the existing agricultural insurance law, to further explore its problems, and take mustard as an example, to build a new type of mustard agricultural insurance products. Finally, in order to explore the development path of crop income insurance suitable for China's national conditions, this paper puts forward suggestions from the following seven aspects: clarifying the positioning of agricultural products, grasping the boundaries of agricultural products' adaptation, strengthening the policy support, formulating the standard model, strengthening the construction of infrastructure, perfecting the reinsurance system, and doing a good job in the assessment standard. This study hopes to provide a certain reference for the study of agricultural insurance product design.

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Section: Introductionmentioning

confidence: 99%

Mustard Agricultural Insurance Product Design in the Context of Common Wealth: Taking Zhejiang Province as An Example

Hong,

Lu,

2024

Int. J. Res. Publ. Rev.

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“…Vegetables are essential in daily diets. Data from the United Nations' Food and Agriculture Organization show that China leads globally, with 52.25% of the world's vegetable planting area and 58.31% of its total production [1]. With the increasing adoption of smart agricultural technologies, traditional methods of vegetable production are evolving.…”

Section: Introductionmentioning

confidence: 99%

Seedling-YOLO: High-Efficiency Target Detection Algorithm for Field Broccoli Seedling Transplanting Quality Based on YOLOv7-Tiny

Zhang,

Zhou,

Liu

et al. 2024

Agronomy

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The rapid and accurate detection of broccoli seedling planting quality is crucial for the implementation of robotic intelligent field management. However, existing algorithms often face issues of false detections and missed detections when identifying the categories of broccoli planting quality. For instance, the similarity between the features of broccoli root balls and soil, along with the potential for being obscured by leaves, leads to false detections of “exposed seedlings”. Additionally, features left by the end effector resemble the background, making the detection of the “missed hills” category challenging. Moreover, existing algorithms require substantial computational resources and memory. To address these challenges, we developed Seedling-YOLO, a deep-learning model dedicated to the visual detection of broccoli planting quality. Initially, we designed a new module, the Efficient Layer Aggregation Networks-Pconv (ELAN_P), utilizing partial convolution (Pconv). This module serves as the backbone feature extraction network, effectively reducing redundant calculations. Furthermore, the model incorporates the Content-aware ReAssembly of Features (CARAFE) and Coordinate Attention (CA), enhancing its focus on the long-range spatial information of challenging-to-detect samples. Experimental results demonstrate that our Seedling-YOLO model outperforms YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv7 in terms of speed and precision, particularly in detecting ‘exposed seedlings’ and ‘missed hills’-key categories impacting yield, with Average Precision (AP) values of 94.2% and 92.2%, respectively. The model achieved a mean Average Precision of 0.5 (mAP@0.5) of 94.3% and a frame rate of 29.7 frames per second (FPS). In field tests conducted with double-row vegetable ridges at a plant spacing of 0.4 m and robot speed of 0.6 m/s, Seedling-YOLO exhibited optimal efficiency and precision. It achieved an actual detection precision of 93% and a detection efficiency of 180 plants/min, meeting the requirements for real-time and precise detection. This model can be deployed on seedling replenishment robots, providing a visual solution for robots, thereby enhancing vegetable yield.

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“…China is recognized as a large vegetable producer and vegetable consumer. China's vegetable sown area and production account for 52.25% and 58.31% of the world's total planted area and production, respectively, ranking first in the world [1]. With the development of modern facility agriculture, the scale of intensive vegetable production has expanded.…”

Section: Introductionmentioning

confidence: 99%

Early Identification and Localization Algorithm for Weak Seedlings Based on Phenotype Detection and Machine Learning

Zhang

Dong

et al. 2023

Agriculture

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It is important to propose the correct decision for culling and replenishing seedlings in factory seedling nurseries to improve the quality of seedlings and save resources. To solve the problems of inefficiency and subjectivity of the existing traditional manual culling and replenishment of seeds, this paper proposes an automatic method to discriminate the early growth condition of seedlings. Taking watermelon plug seedlings as an example, Azure Kinect was used to collect data of its top view three times a day, at 9:00, 14:00, and 19:00. The data were collected from the time of germination to the time of main leaf growth, and the seedlings were manually determined to be strong or weak on the last day of collection. Pre-processing, image segmentation, and point cloud processing methods were performed on the collected data to obtain the plant height and leaf area of each seedling. The plant height and leaf area on the sixth day were predicted using an LSTM recurrent neural network for the first three days. The R squared for plant height and leaf area prediction were 0.932 and 0.901, respectively. The dichotomous classification of normal and abnormal seedlings was performed using six machine learning classification methods, such as random forest, SVM, and XGBoost, for day six data. The experimental results proved that random forest had the highest classification accuracy of 84%. Finally, the appropriate culling and replenishment decisions are given based on the classification results. This method can provide some technical support and a theoretical basis for factory seedling nurseries and transplanting robots.

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Spatial Distribution and Driving Forces of the Vegetable Industry in China

Cited by 15 publications

References 36 publications

Mustard Agricultural Insurance Product Design in the Context of Common Wealth: Taking Zhejiang Province as An Example

Mustard Agricultural Insurance Product Design in the Context of Common Wealth: Taking Zhejiang Province as An Example

Seedling-YOLO: High-Efficiency Target Detection Algorithm for Field Broccoli Seedling Transplanting Quality Based on YOLOv7-Tiny

Early Identification and Localization Algorithm for Weak Seedlings Based on Phenotype Detection and Machine Learning

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