Journal of Food Measurement and Characterization
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2025
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IF 3.3
Liu Wangyu,Tan Zhenhua,Xie Weigui
With the continuous growth of tomato yields and the need to address the cumbersome task of manual harvesting, the development of harvesting robots presents a promising solution for tomato harvesting. A key challenge lies in improving the success rate of these robots for reliable tomato harvesting without causing damage. This study aims to propose a rigid-flexible coupled gripper to ensure higher success rates in tomato harvesting, while also incorporating an advanced method for detecting fruit ripeness. The paper introduces a flexible hydrogel pressure sensor, featuring a wide detection range, high sensitivity, and excellent stability, integrated into the gripper design. A signal acquisition system based on a sensor array is developed, enabling the accurate capture of force signals during the tomato grasping process. The sensor array collects tactile sequence data in real-time, which, when combined with compressive deformation data from the fruit, forms a comprehensive dataset. To detect tomato ripeness, four classification models—Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Multi-Layer Perceptron (MLP), and Fully Convolutional Networks (FCN)—are implemented. Among the four models, the LSTM network achieves the highest classification performance. The overall recognition accuracy for tomatoes of different ripeness levels, within the same variety and at room temperature, is determined to be 99%. The results demonstrate that the combination of a flexible gripper with hydrogel sensors not only preserves the integrity of the tomatoes but also ensures accurate detection of fruit ripeness. This innovation has the potential to significantly enhance the performance of tomato harvesting robots, contributing to more efficient and automated agricultural practices.
研究领域:
农业工程
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植物生理学
✓ 已验证
产品关联
DOI
高影响因子
INDUSTRIAL CROPS AND PRODUCTS
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2024
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IF 5.6
Hao Jia,Xiong Jing,Wenhui Su,Huimin Yang,Shaohui Fan,Yutong Sun
Bamboo is a sustainable and environment-friendly polymer. In order to investigate the effect of photoaging on the service-life of bamboo, ultraviolet aging (UV-aging), light-avoidance −90 %RH (AL-90 %RH), light-avoidance (AL) and control were continuously monitored for 26 weeks under four kinds of light conditions. The surface color, micro-morphology and surface interfacial composition of comparing various bamboo samples during storage. The results showed that the total color difference for the cortex (9.758 ± 0.712) and pith (1.325 ± 0.075) under AL conditions were lower than the other three sets of storage conditions, and L* values were reduced by 56.42 % and 53.73 %, respectively. In addition, microflora and grooves were formed on the surface of bamboo stored under AL-90 %RH conditions. Meanwhile, catheters were the main channels for the propagation of mycelium in bamboo. Moreover, the Cox/unox value (an oxygenated to unoxygenated carbon ratio of 0.964) increased sharply under these conditions. UV-aging destroyed the texture and structure of bamboo tissue cells and surfaces, degraded the surface lignin content by 18.89 %, and exposed more observable hemicellulose and cellulose functional groups, thus increasing the carbon binding energy and becoming more difficult to degrade. Finally, the Long Short-Term Memory (LSTM) neural network model was used to predict the fading of the surface color and the volatile compounds after aging, and different photoaging color indication systems were established. Exploring the aging mechanism of bamboo during storage is important for promoting the development of bamboo industry and replacing plastic products, and helping in determine the practical applications of the proposed strategies, their economic benefits and limiting of bamboo photodegradation under optimal warehousing conditions. This meet the current requirements of sustainable development by preserving bamboo resources and use instead renewable resources for both environmental and economic benefit.
研究领域:
材料科学
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高分子化学
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环境友好材料
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可持续发展
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表面化学
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生物降解
✓ 已验证
DOI
高影响因子
Science China-Materials
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2025
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IF 7.4
Zhao Xinyi,Lin Xiangsheng,Yao Zhao,Li Yuanyue,Li Yang,Gong Ningji
Smart agriculture utilizes sensors and software to control agricultural production through mobile or computer platforms, enabling unmanned, automated, and intelligent management. Recently, research and development in plant growth monitoring technologies have garnered significant attention. The challenge lies in achieving long-term monitoring, phased predictions, and plant self-regulation without harming the plants. The present study demonstrates the fabrication of plant-compatible and breathable tensile and bending strain sensors using composite nanofiber membranes (CNMs) composed of Ti 2 C 2 T x (MXene), carbon nanotubes (CNTs), and thermoplastic polyurethanes (TPU) through electrospinning and ultrasonic immersion techniques. The MXene and CNTs synergistically form a dual-network conductive structure on the TPU nanofiber membrane, thereby imparting the composite membrane with remarkable tensile sensitivity (5.41, 7.39, and 3.39 within the ranges of 0%–20%, 20%–50%, and 50%–70%, respectively) as well as exceptional bending sensitivity (1.79, 0.89, and 0.46 within the ranges of 0°–30°, 30°–90°, and 90°–120°, respectively). The tensile strain sensor, combined with a deep learning Long Short-Term Memory (LSTM) model, establishes a platform for plant growth monitoring and prediction. The bending strain sensor, integrated with a shape memory alloy (SMA)-based soft actuator, forms a plant sensing-actuating system to assist in plant leaf growth. This work leverages MXene/CNTs/TPU CNMs to flexibly prepare strain sensors for specific applications, combining deep learning and soft actuators to achieve plant growth prediction and self-regulation. This research holds significant importance in advancing the development of smart agriculture.
研究领域:
农业工程
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智能农业
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传感器技术
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植物生理学
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纳米材料
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生物机电一体化
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）