Biosystems Engineering最新文献_第9页

Evaluating a hybrid process of anaerobic digestion, aerobic degradation, and electrochemical separation for swine wastewater treatment with methane and nutrient recovery 评估采用厌氧消化、好氧降解和电化学分离混合工艺处理猪废水并回收甲烷和营养物质的效果

IF 4.4 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2024-10-04 DOI: 10.1016/j.biosystemseng.2024.09.022

Run-Feng Chen , Chun-Hai Wei , Hai-Tao Zhong , Xiu-Feng Ye , Jun-Jie Ye , Kai Liu , Quan-Bao Zhao , Huu Hao Ngo

A hybrid process of anaerobic digestion (AD), aerobic degradation, and electrochemical separation was evaluated for treating real swine wastewater that is rich in organic and nutrient to achieve methane and nutrient recovery and industry standard discharge quality. Fe anode electrocoagulation and Mg anode struvite electrochemical precipitation (SEP) were evaluated as AD pretreatments. Both removed partial chemical oxygen demand (COD) from raw swine wastewater, but only SEP slightly enhanced the methane yield of pretreated swine wastewater. The SEP efficiency of the AD effluent was significantly better than raw swine wastewater. A further coupled micro/ultra-filtration produced high-purity (96%) struvite. SEP and struvite chemical precipitation (SCP) were evaluated for AD effluent treatment. This showed that compared with SCP following first-order reaction kinetics (reaction rate constant of 0.791 and 0.854 h⁻¹ for NH₄⁺-N and PO₄^3--P), SEP not only achieved better removal of COD, NH₄⁺-N and PO₄^3--P, but was also shown to follow zero-order reaction kinetics (reaction rate constant of 5.72 and 5.78 mmol L⁻¹ h⁻¹ for NH₄⁺-N and PO₄^3--P). The SEP and SCP treated AD effluent was evaluated by conventional activated sludge (CAS), showing faster COD removal (first-order reaction rate constant of 0. 213 and 0.163 h⁻¹) and lower residual COD (150 and 248 mg L⁻¹) from SEP than SCP treated AD effluent, making the final effluent well below Chinese livestock wastewater discharge standards. Therefore, an emerging hybrid anaerobic membrane bioreactor (AnMBR)-SEP-CAS is proposed for swine wastewater treatment and proved to be more economically viable than the conventional hybrid AD-SCP-CAS process via cost-benefit analysis.

对厌氧消化（AD）、好氧降解和电化学分离的混合工艺进行了评估，以处理富含有机物和营养物的真实猪场废水，实现甲烷和营养物的回收，并达到工业标准的排放质量。铁阳极电凝和镁阳极石英砂电化学沉淀 (SEP) 作为厌氧消化（AD）预处理方法进行了评估。这两种方法都能去除生猪废水中的部分化学需氧量（COD），但只有 SEP 能稍微提高预处理生猪废水的甲烷产量。厌氧消化（AD）出水的 SEP 效率明显优于生猪废水。进一步的耦合微滤/超滤产生了高纯度（96%）的硬石膏。针对厌氧消化（AD）废水处理，对 SEP 和硬石膏化学沉淀（SCP）进行了评估。结果表明，与采用一阶反应动力学（NH4+-N 和 PO43--P 的反应速率常数分别为 0.791 和 0.854 h-1）的 SCP 相比，SEP 不仅能更好地去除 COD、NH4+-N 和 PO43--P，而且采用零阶反应动力学（NH4+-N 和 PO43--P 的反应速率常数分别为 5.72 和 5.78 mmol L-1 h-1）。传统活性污泥法（CAS）对 SEP 和 SCP 处理后的厌氧消化污水进行了评估，结果表明，与 SCP 处理后的厌氧消化污水相比，SEP 的 COD 去除速度更快（一阶反应速率常数分别为 0.213 和 0.163 h-1），残留 COD 更低（分别为 150 和 248 mg L-1），最终出水远远低于中国畜禽养殖废水排放标准。因此，我们提出了一种新兴的厌氧膜生物反应器（AnMBR）-SEP-CAS 混合工艺用于猪废水处理，并通过成本效益分析证明该工艺比传统的 AD-SCP-CAS 混合工艺更具经济可行性。

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引用次数: 0

PointStack based 3D automatic body measurement for goat phenotypic information acquisition 基于 PointStack 的三维自动体型测量，用于采集山羊表型信息

IF 4.4 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2024-10-01 DOI: 10.1016/j.biosystemseng.2024.09.008

Bo Jin , Guorui Wang , Jingze Feng , Yongliang Qiao , Zhifeng Yao , Mei Li , Meili Wang

The body size of livestock is an essential phenotypic trait in genetic breeding, gene improvement, health screening, and animal welfare. To develop a non-contact automatic system for measuring goat body traits, we propose a point-cloud segmentation model based on an improved PointStack, which segments the automatically acquired three-dimensional (3D) point-cloud data of goats into different parts, including the head, front legs, hind legs, chest, abdomen, hip, and tail. The segmented point cloud, along with the physiological features of the goat, is then used to locate the corresponding key points for body size measurement. A novel method for key point localisation is proposed that includes coordinate normalisation, retrieval of key clusters, key point adjustment, optimisation of the traveling salesman problem, and edge detection. These methods were designed to reduce discrepancies at crucial points of body features, thereby facilitating the precise computation of the body size parameter in goats. In this work, 326 point clouds representing the upright posture of 55 goats were used for segmentation and body size measurement testing. The proposed segmentation model achieved a mean intersection over union of 89.21% and accuracy of 94.54%, outperforming comparative models. In the body traits measurement experiment, mean absolute percentage errors for body length, body height, chest width, chest girth, hip height, and hip width were recorded as 3.24%, 2.54%, 5.43%, 3.08%, 2.16%, and 4.59%, respectively. In summary, the proposed automated measurement method demonstrates high accuracy, strong robustness, and holds significant potential for widespread application.

牲畜的体型是遗传育种、基因改良、健康检查和动物福利方面的重要表型特征。为了开发一种用于测量山羊体型特征的非接触式自动系统，我们提出了一种基于改进型 PointStack 的点云分割模型，该模型可将自动获取的山羊三维（3D）点云数据分割成不同的部分，包括头部、前腿、后腿、胸部、腹部、臀部和尾部。然后利用分割后的点云以及山羊的生理特征来定位相应的关键点，以便进行体型测量。我们提出了一种新颖的关键点定位方法，包括坐标归一化、关键集群检索、关键点调整、优化旅行推销员问题和边缘检测。这些方法旨在减少身体特征关键点的差异，从而促进山羊体型参数的精确计算。在这项工作中，使用了代表 55 只山羊直立姿势的 326 个点云进行分割和体型测量测试。所提出的分割模型的平均相交率为 89.21%，准确率为 94.54%，优于比较模型。在体型测量实验中，体长、体高、胸宽、胸围、臀高和臀宽的平均绝对百分比误差分别为 3.24%、2.54%、5.43%、3.08%、2.16% 和 4.59%。总之，所提出的自动测量方法准确度高、稳健性强，具有广泛应用的巨大潜力。

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引用次数: 0

Semantics-aware next-best-view planning for efficient search and detection of task-relevant plant parts 语义感知的下一个最佳视图规划，用于高效搜索和检测任务相关的植物部分

IF 4.4 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2024-09-30 DOI: 10.1016/j.biosystemseng.2024.09.018

Akshay K. Burusa, Joost Scholten, Xin Wang, David Rapado-Rincón, Eldert J. van Henten, Gert Kootstra

Searching and detecting the task-relevant parts of plants is important to automate harvesting and de-leafing of tomato plants using robots. This is challenging due to high levels of occlusion in tomato plants. Active vision is a promising approach in which the robot strategically plans its camera viewpoints to overcome occlusion and improve perception accuracy. However, current active-vision algorithms cannot differentiate between relevant and irrelevant plant parts and spend time on perceiving irrelevant plant parts. This work proposed a semantics-aware active-vision strategy that uses semantic information to identify the relevant plant parts and prioritise them during view planning. The proposed strategy was evaluated on the task of searching and detecting the relevant plant parts using simulation and real-world experiments. In simulation experiments, the semantics-aware strategy proposed could search and detect 81.8% of the relevant plant parts using nine viewpoints. It was significantly faster and detected more plant parts than predefined, random, and volumetric active-vision strategies that do not use semantic information. The strategy proposed was also robust to uncertainty in plant and plant-part positions, plant complexity, and different viewpoint-sampling strategies. In real-world experiments, the semantics-aware strategy could search and detect 82.7% of the relevant plant parts using seven viewpoints, under complex greenhouse conditions with natural variation and occlusion, natural illumination, sensor noise, and uncertainty in camera poses. The results of this work clearly indicate the advantage of using semantics-aware active vision for targeted perception of plant parts and its applicability in the real world. It can significantly improve the efficiency of automated harvesting and de-leafing in tomato crop production.

搜索和检测植物的任务相关部分对于使用机器人自动收获和摘除番茄植株的叶子非常重要。由于番茄植株的遮挡程度较高，因此这项工作极具挑战性。主动视觉是一种很有前途的方法，在这种方法中，机器人会战略性地规划其摄像头视点，以克服遮挡并提高感知精度。然而，目前的主动视觉算法无法区分相关和不相关的植物部分，因此会在感知不相关的植物部分上花费时间。这项研究提出了一种语义感知主动视觉策略，利用语义信息识别相关植物部分，并在视图规划过程中对其进行优先排序。通过模拟实验和真实世界实验，在搜索和检测相关植物部分的任务中对所提出的策略进行了评估。在模拟实验中，所提出的语义感知策略可以使用九个视角搜索和检测 81.8% 的相关植物部分。与不使用语义信息的预定义策略、随机策略和体积主动视觉策略相比，该策略的速度明显更快，检测到的植物部分也更多。所提出的策略对植物和植物部分位置的不确定性、植物的复杂性以及不同的视点采样策略也很稳健。在真实世界的实验中，在复杂的温室条件下，包括自然变化和遮挡、自然光照、传感器噪声以及相机姿势的不确定性，语义感知策略可以使用七个视点搜索并检测到 82.7% 的相关植物部分。这项工作的结果清楚地表明了使用语义感知主动视觉来有针对性地感知植物部分的优势及其在现实世界中的适用性。它能显著提高番茄作物生产中自动收获和去叶的效率。

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引用次数: 0

Predicting wheat scab levels based on rotation detector and Swin classifier 基于旋转检测器和 Swin 分类器预测小麦赤霉病程度

IF 4.4 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2024-09-30 DOI: 10.1016/j.biosystemseng.2024.09.016

Dongyan Zhang , Zhipeng Chen , Hansen Luo , Gensheng Hu , Xin-Gen Zhou , Chunyan Gu , Liping Li , Wei Guo

Wheat scab is a highly destructive disease that adversely impact wheat crops throughout their growth cycle. It is crucial to promptly evaluate the levels of wheat scab in the field to prevent its spread. Manual observation, however, is inefficient and time-consuming. Recent research has indicated that computer vision-based methods can enhance efficiency in this regard. This study proposed a method for predicting wheat scab levels using a rotation detector and Swin classifier.

To minimise background interference, the study incorporated the rotation wheat detector (RWD) network for detecting wheat heads. The RWD network employed the Kalman filter Intersection over Union (KFIoU) to predict the angle, thereby improving accuracy. The Swin wheat classifier (SWC) network was employed to classify healthy and diseased wheat heads. The SWC network benefited from the shifted window self-attention module (SW-MSA), which enhanced feature extraction by establishing connections with other windows. The proposed method was evaluated using wheat field images collected over 3 years. The results demonstrate promising performance, achieving a 96% accuracy in predicting wheat scab levels. Furthermore, the R² and RMSE values for diseased wheat count were 97.62% and 3.61, respectively. This method offers an accurate means of predicting wheat scab levels through the analysis of wheat field images. Additionally, the introduction of the rotation detector presents a novel contribution to research on wheat scab detection.

小麦赤霉病是一种破坏性很强的病害，对小麦作物的整个生长周期都有不利影响。及时评估田间小麦赤霉病的程度以防止其蔓延至关重要。然而，人工观察效率低且耗时。最近的研究表明，基于计算机视觉的方法可以提高这方面的效率。为了最大限度地减少背景干扰，该研究采用了旋转小麦检测器（RWD）网络来检测小麦头。RWD 网络采用卡尔曼滤波器交叉联合（KFIoU）来预测角度，从而提高了准确性。斯温小麦分类器（SWC）网络用于对健康麦头和病害麦头进行分类。SWC 网络得益于移位窗口自注意模块（SW-MSA），该模块通过与其他窗口建立连接来增强特征提取。利用 3 年来收集的麦田图像对所提出的方法进行了评估。结果表明，该方法性能良好，预测小麦赤霉病水平的准确率达到 96%。此外，病麦计数的 R2 和 RMSE 值分别为 97.62% 和 3.61。该方法通过分析麦田图像，为预测小麦赤霉病程度提供了一种准确的方法。此外，旋转检测器的引入也为小麦赤霉病检测研究做出了新的贡献。

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引用次数: 0

Impacts of crate design, number of heat lamps and lying posture on the occurrence of shoulder lesions in sows 板条箱设计、保温灯数量和躺卧姿势对母猪肩部病变发生的影响

IF 4.4 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2024-09-24 DOI: 10.1016/j.biosystemseng.2024.09.017

Shubham Bery , Tami M. Brown-Brandl , Gary A. Rohrer , Sudhendu Raj Sharma , Suzanne M. Leonard

This study investigated the interaction of sow and engineering factors on shoulder lesion formation. Sows were randomly assigned to three farrowing crate designs: Traditional Stall Layout, Expanded Creep Stall Layout, and Expanded Sow & Creep Stall Layout. Each crate configuration was further differentiated by the inclusion of either one (1HL) or two (2HL) heat lamps. Digital and depth images were collected from an overhead time of flight depth camera (Kinect V2) every 5 s. Computer vision techniques were employed to analyze top-down digital images from the 21st to the 24th day of farrowing to detect and estimate lesion size. Additionally, the study incorporated an analysis of sow lying behaviors on the occurrence and size of lesions using depth images. Sow's environmental and phenotypic data - weight, parity, body condition score, total lying time and number of lying transitions in a day were investigated for impact on shoulder lesion. The results indicated that the interaction of smaller crate sizes and increased heat lamp usage significantly impacted lesion occurrence (p < 0.05). Also, higher parity and lighter weight sows showed higher lesion occurrence (p < 0.05). However, other factors, such as the number of heat lamps alone and detailed metrics of lying postures, did not show a significant impact on lesion occurrence. In contrast, none of the studied factors showed a significant impact on the size of shoulder lesions. This highlights the importance of allocating crate space with respect to heat lamp placement to the sows.

Science4Impact Statement (S4IS): This manuscript evaluates shoulder lesions' presence and size in lactating sows housed within farrowing stalls. Shoulder lesions are one of the main causes of premature culling in sows and are a major concern for animal well-being. Understanding the impact of crate design and the number of heat lamps is important for the engineering design of the farrowing environment.

本研究调查了母猪和工程因素对肩部病变形成的相互影响。母猪被随机分配到三种产仔箱设计中：传统栏位布局、扩大的匍匐栏位布局和扩大的母猪&匍匐栏位布局。每种产仔箱配置都有一个（1HL）或两个（2HL）加热灯。采用计算机视觉技术分析产仔第 21 天至第 24 天的自上而下的数字图像，以检测和估计病变大小。此外，该研究还利用深度图像分析了母猪躺卧行为对病变发生和大小的影响。研究还调查了母猪的环境和表型数据--体重、胎次、体况评分、总躺卧时间和一天中的躺卧转换次数--对肩部病变的影响。结果表明，较小的围栏尺寸和增加保温灯的使用对肩部病变的发生有显著影响（p < 0.05）。此外，胎次较高和体重较轻的母猪肩部病变发生率较高（p < 0.05）。然而，其他因素，如单独使用热灯的数量和卧姿的详细指标，对病变发生率没有显著影响。相比之下，所研究的因素都没有对肩部病变的大小产生明显影响。这凸显了在给母猪放置加热灯时分配笼舍空间的重要性：本手稿评估了饲养在产房内的哺乳母猪肩部病变的存在和大小。肩部病变是造成母猪过早淘汰的主要原因之一，也是动物健康的一个主要问题。了解产房设计和保温灯数量的影响对于产房环境的工程设计非常重要。

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引用次数: 0

Characterising equivalent droplet indicators of sprinkler irrigation from a kinetic energy perspective 从动能角度确定喷灌的等效水滴指标

IF 4.4 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2024-09-24 DOI: 10.1016/j.biosystemseng.2024.09.019

Rui Zhang , Yichuan Liu , Delan Zhu , Pute Wu , Changjuan Zheng , Xiaomin Zhang , Nazarov Khudayberdi , Changxin Liu

Equivalent droplet velocity and diameter are important parameters for measuring the effectiveness of sprinkler spraying; however, non-optical test methods (paper stain, flour pellet, and oil immersion methods) can only obtain the droplet number and diameter. With the widespread use of optical instruments in sprinkler testing, droplet velocity can also be measured, therefore, it has become possible to calculate the average droplet characteristics from an energy perspective. This paper proposes an energy-weighted method for calculating droplet equivalence indicators. Statistical analyses were performed based on five types of sprinkler irrigation droplet distribution data to compare the characteristics and differences between the energy-weighted method and the calculation results of the other methods. The results showed that 1) the velocity outcomes of the energy-weighted droplet equivalent method, empirical formula I, and empirical formula II consistently increase and decrease; 2) the equivalent droplet diameter based on the energy-weighted method is the largest, followed by the equivalent method related to droplet volume, and the smallest is the equivalent method related to droplet quantity; and 3) the equivalent droplet velocity and diameter calculated by the energy-weighted equivalent method can characterise droplets with a high energy contribution. The energy-weighted equivalent droplet velocity and diameter indicators derived in this study provide new ideas for characterising droplet averaging.

等效液滴速度和直径是测量喷灌机喷洒效果的重要参数；然而，非光学测试方法（纸张染色法、面粉颗粒法和油浸法）只能获得液滴数量和直径。随着光学仪器在洒水测试中的广泛应用，水滴速度也可以测量，因此从能量角度计算平均水滴特性成为可能。本文提出了一种计算液滴等效指标的能量加权法。根据五种喷灌水滴分布数据进行统计分析，比较能量加权法与其他方法计算结果的特点和差异。结果表明：1）能量加权等效水滴法、经验公式 I 和经验公式 II 的速度结果一致地增大和减小；2）基于能量加权法的等效水滴直径最大，其次是与水滴体积相关的等效法，最小的是与水滴数量相关的等效法；3）能量加权等效法计算的等效水滴速度和直径可以表征高能量贡献的水滴。本研究得出的能量加权等效液滴速度和直径指标为表征液滴平均值提供了新思路。

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引用次数: 0

Disturbance analysis and seeding performance evaluation of a pneumatic-seed spoon interactive precision maize seed-metering device for plot planting 用于小区播种的气动播种勺交互式玉米种子精确计量装置的扰动分析和播种性能评估

IF 4.4 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2024-09-23 DOI: 10.1016/j.biosystemseng.2024.09.007

Shidong Deng, Yamei Feng, Xiupei Cheng, Xianliang Wang, Xiangcai Zhang, Zhongcai Wei

In response to the serious issue of missed seeding of the seed-metering device caused by the small and gradually decreasing number of maize seeds in plot planting conditions, a precision seed-metering device for maize plots with pneumatic-seed spoon interactive was designed. The seed-metering device utilises the coupling interaction between the seed spoons and airflow to adjust the maize posture in the filling zone, achieving stable filling of the seed-metering device with a low population of seeds in the seed chamber. EDEM software is used to simulate and analyse the disturbance caused by three types of seed-metering discs and the average kinetic energy in the filling zone as the evaluation criterion. A test platform for seed-metering device of maize plot was constructed, with the qualified index, multiple index, and missing index as evaluation criteria. A full-factorial experiment was conducted with rotation speed of the seed-metering disc, air pressure, and types of seed-metering discs as factors, determining the optimal seed-metering disc for seeding performance. The results indicated that under conditions of low seed population in the seeding chamber, with air pressures ranging from −1.5 to −2.5 kPa and seed-metering disc speeds between 1.16 and 3.49 rad s⁻¹, the seed-metering device with linear disturbance exhibited a multiple index of <6.25% and a missing index of <3.46%. Additionally, the qualified index consistently reached 90.29%. These evaluation criteria meet the standards, demonstrating effective seeding capabilities.

针对小区播种条件下玉米种子数量少且逐渐减少，导致测种装置漏播的严重问题，设计了一种气动播种勺交互式玉米小区精密测种装置。该种子计量装置利用种子勺与气流之间的耦合互动来调整充种区内的玉米姿态，从而实现了在种子腔内种子数量较少的情况下稳定充种的种子计量装置。EDEM 软件用于模拟和分析三种类型的种子计量盘造成的干扰，并以充填区的平均动能作为评估标准。构建了玉米小区种子计量装置测试平台，以合格指数、多重指数和缺失指数作为评价标准。以测种盘转速、气压和测种盘类型为因素，进行了全因子试验，确定了播种性能最优的测种盘。结果表明，在播种室内种子数量较少的条件下，气压在 -1.5 至 -2.5 kPa 之间，排种盘转速在 1.16 至 3.49 rad s-1 之间，线性扰动排种器的多重指数为 <6.25%，缺失指数为 <3.46%。此外，合格指数始终达到 90.29%。这些评估标准均符合标准，显示了有效的播种能力。

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引用次数: 0

Positioning of mango picking point using an improved YOLOv8 architecture with object detection and instance segmentation 利用改进的 YOLOv8 架构进行芒果采摘点定位，并进行对象检测和实例分割

IF 4.4 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2024-09-23 DOI: 10.1016/j.biosystemseng.2024.09.015

Hongwei Li , Jianzhi Huang , Zenan Gu , Deqiang He , Junduan Huang , Chenglin Wang

Positioning of mango picking points is a crucial technology for the realisation of automated robotic mango harvesting. Herein, this study reported a visualised end-to-end system for mango picking point positioning using improved YOLOv8 architecture with object detection and instance segmentation, as well as an algorithm of picking point positioning. At first, the improved YOLOv8n model, incorporating the BiFPN structure and the SPD-Conv module, was utilised to enhance the detection performance of mango fruits and stems. This model achieved a detection precision of 98.9% in fruits and 97.1% in stems, with recall of 99.5% and 94.6% respectively. Then, the YOLOv8n-seg model was used for segment the stem ROI (Region of interest), leading to 81.85% in MIoU and 88.69% in mPA. Finally, a skeleton line of the stem region was obtained on the basis of the segmentation image, and a picking point positioning algorithm was developed to determine the coordinates of the optimal picking point. Subsequently, the positioning success rate of coordinates, absolute errors, and relative errors were calculated by comparing the automatic positioned coordinates with the manually positioned stem region. Experimental results indicated that this study achieved an average positioning success rate of 92.01%, with an average absolute error of 4.93 pixels and an average relative error of 13.11%. Additionally, the average processing time for processing 640 images using the picking point positioning system is 72.75 ms. This study demonstrates the reliability and effectiveness of positioning mango picking points, laying the technological basis for the automated harvesting of mango fruits.

芒果采摘点的定位是实现芒果自动机器人采摘的关键技术。在此，本研究报告了一个可视化端到端芒果采摘点定位系统，该系统采用改进的 YOLOv8 架构，具有对象检测和实例分割功能，以及采摘点定位算法。首先，利用改进的 YOLOv8n 模型，结合 BiFPN 结构和 SPD-Conv 模块，提高了芒果果实和茎的检测性能。该模型对水果和茎的检测精度分别达到了 98.9% 和 97.1%，召回率分别为 99.5% 和 94.6%。然后，使用 YOLOv8n-seg 模型对茎的 ROI（感兴趣区域）进行分割，MIoU 和 mPA 的分割结果分别为 81.85% 和 88.69%。最后，在分割图像的基础上获得了茎干区域的骨架线，并开发了取点定位算法，以确定最佳取点的坐标。随后，通过比较自动定位的坐标和人工定位的茎干区域，计算出坐标的定位成功率、绝对误差和相对误差。实验结果表明，该研究的平均定位成功率为 92.01%，平均绝对误差为 4.93 像素，平均相对误差为 13.11%。此外，使用拾取点定位系统处理 640 幅图像的平均处理时间为 72.75 毫秒。这项研究证明了芒果采摘点定位的可靠性和有效性，为芒果果实的自动采摘奠定了技术基础。

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引用次数: 0

Laser Doppler vibrometer enables in-situ monitoring of peach firmness 激光多普勒测振仪实现了对桃子硬度的现场监测

IF 4.4 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2024-09-22 DOI: 10.1016/j.biosystemseng.2024.09.013

Dachen Wang , Yilei Hu , Jiaqi Xiong , Yibin Ying , Ce Yang , Di Cui

Fruit firmness is a measure of the edible quality and maturity of peaches. In-situ monitoring of peach firmness can aid in fruit quality control and determining the optimal harvest time according to market demand. In this study, a non-contact acoustic vibration-based method was proposed for in-situ monitoring of fruit firmness of on-tree peaches. A new design of a compressed air excitation unit was constructed to impact the peach on the tree and a laser Doppler vibrometer was adopted to measure the acoustic vibration response (AVR) of the peach. To isolate the vibration information characterising fruit firmness, the AVR was firstly pre-processed by the wavelet threshold denoising method and then analysed by the autoregressive method to acquire the power spectral density (PSD) of the peach. For effectively extracting vibration features from the PSD to predict peach firmness, a novel one-dimensional convolutional neural network (CNN_m) with multiscale perceptual fields was constructed. The performance of CNN_m was compared with those of partial least squares regression, support vector regression models, and a single-branch 1D-CNN model with the mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and residual prediction deviation (RPD). The results indicated that the proposed method enabled in-situ monitoring of peach firmness and the established CNN_m model performed better than other models in predicting peach firmness (

R_{P}^{2}

= 0.813, MAEP = 1.636 N mm⁻¹, RMSEP = 2.501 N mm⁻¹, and

{R P D}_{P}

= 2.334).

果实坚实度是衡量桃子可食用质量和成熟度的标准。对桃子果实坚实度的现场监测有助于果实质量控制和根据市场需求确定最佳采收时间。本研究提出了一种基于声学振动的非接触式方法，用于现场监测树上桃子的果实坚实度。研究人员建造了一个新设计的压缩空气激励装置来冲击树上的桃子，并采用激光多普勒测振仪来测量桃子的声学振动响应（AVR）。为了分离出表征果实坚硬程度的振动信息，首先用小波阈值去噪方法对声学振动响应进行预处理，然后用自回归方法对其进行分析，以获得桃子的功率谱密度（PSD）。为了有效地从 PSD 中提取振动特征来预测桃子的坚硬程度，我们构建了一个具有多尺度感知场的新型一维卷积神经网络（CNNm）。用平均绝对误差（MAE）、均方根误差（RMSE）、判定系数（R2）和残差预测偏差（RPD）比较了 CNNm 与偏最小二乘回归、支持向量回归模型和单分支一维卷积神经网络模型的性能。结果表明，所提出的方法可对桃子的坚实度进行现场监测，所建立的 CNNm 模型在预测桃子坚实度方面的表现优于其他模型（RP2 = 0.813、MAEP = 1.636 N mm-1、RMSEP = 2.501 N mm-1、RPDP = 2.334）。

{"title":"Laser Doppler vibrometer enables in-situ monitoring of peach firmness","authors":"Dachen Wang , Yilei Hu , Jiaqi Xiong , Yibin Ying , Ce Yang , Di Cui","doi":"10.1016/j.biosystemseng.2024.09.013","DOIUrl":"10.1016/j.biosystemseng.2024.09.013","url":null,"abstract":"<div><div>Fruit firmness is a measure of the edible quality and maturity of peaches. In-situ monitoring of peach firmness can aid in fruit quality control and determining the optimal harvest time according to market demand. In this study, a non-contact acoustic vibration-based method was proposed for in-situ monitoring of fruit firmness of on-tree peaches. A new design of a compressed air excitation unit was constructed to impact the peach on the tree and a laser Doppler vibrometer was adopted to measure the acoustic vibration response (AVR) of the peach. To isolate the vibration information characterising fruit firmness, the AVR was firstly pre-processed by the wavelet threshold denoising method and then analysed by the autoregressive method to acquire the power spectral density (PSD) of the peach. For effectively extracting vibration features from the PSD to predict peach firmness, a novel one-dimensional convolutional neural network (CNN<sub>m</sub>) with multiscale perceptual fields was constructed. The performance of CNN<sub>m</sub> was compared with those of partial least squares regression, support vector regression models, and a single-branch 1D-CNN model with the mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (<em>R</em><sup><em>2</em></sup>), and residual prediction deviation (<em>RPD</em>). The results indicated that the proposed method enabled in-situ monitoring of peach firmness and the established CNN<sub>m</sub> model performed better than other models in predicting peach firmness (<span><math><mrow><msubsup><mi>R</mi><mi>P</mi><mn>2</mn></msubsup></mrow></math></span> = 0.813, MAEP = 1.636 N mm<sup>−1</sup>, RMSEP = 2.501 N mm<sup>−1</sup>, and <span><math><mrow><msub><mrow><mi>R</mi><mi>P</mi><mi>D</mi></mrow><mi>P</mi></msub></mrow></math></span> = 2.334).</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 191-201"},"PeriodicalIF":4.4,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new removal method of yellow-rotten leaf for hydroponic lettuce with the flipping-tearing-twisting trajectory and its parameters optimisation 翻转-撕裂-扭转轨迹去除水培生菜黄腐叶的新方法及其参数优化

IF 4.4 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering

Pub Date : 2024-09-21 DOI: 10.1016/j.biosystemseng.2024.09.010

Yidong Ma , Chong Qi , Liming Zhou , Xin Jin , Bo Zhao , Xinping Li

To intelligently remove the yellow-rotten leaf of hydroponic lettuce, a new leaf removal method was proposed. The lettuce position was adjusted for yellow-rotten leaf removal according to the visual recognition and localisation, and then the adsorbed yellow-rotten leaf was lifted by air pipes. Finally, the leaf was clamped and removed along the flipping-tearing-twisting trajectory. The adsorbing pressure, adsorbing position, and clamping position for yellow-rotten leaf removal were confirmed by the adsorbing and stretching tests. To improve the leaf removal success rate, the tearing angle, flipping angle, and torsional time radio were optimised by Box-Behnken tests. A quadratic model for the three factors and leaf removal success rate was established to analyse the orders of significance, and the order of significance for single factor was (i) the tearing angle, (ii) the flipping angle, and (iii) the torsional time radio. The order of significance for interaction terms was (i) the flipping angle and tearing angle, and (ii) the flipping angle and torsional time ratio. The solved optimal combination of factors was a flipping angle of 100.5°, a tearing angle of 131.0°, and a torsional time ratio of 0.68, which gave the maximum leaf removal success rate. The optimal combination of factors was verified, and the leaf removal process was shot by high speed camera. The verification tests showed that the maximum leaf removal success rate was 82.8%, and the leaf removal process took 6.58 s, meeting the requirements of yellow-rotten leaf removal for hydroponic lettuce.

为智能去除水培生菜的黄腐叶，提出了一种新的去叶方法。根据视觉识别和定位，调整莴苣位置以去除黄腐叶，然后用气管提升吸附的黄腐叶。最后，沿着翻转-撕扯-扭转的轨迹夹住并摘除叶片。通过吸附和拉伸试验，确认了去除黄腐叶的吸附压力、吸附位置和夹持位置。为提高摘叶成功率，通过盒-贝肯（Box-Behnken）试验对撕裂角、翻转角和扭转时间无线电进行了优化。建立了三个因素与摘叶成功率的二次模型，分析了显著性顺序，单因素的显著性顺序为(i) 撕裂角、(ii) 翻转角和(iii) 扭转时间收音机。交互项的显著性顺序为：(i) 翻转角和撕裂角；(ii) 翻转角和扭转时间比。求解出的最佳因素组合为翻转角 100.5°、撕裂角 131.0°、扭转时间比 0.68，该组合可获得最大的摘叶成功率。对各因素的最佳组合进行了验证，并用高速摄像机拍摄了摘叶过程。验证测试表明，最大摘叶成功率为 82.8%，摘叶过程耗时 6.58 秒，满足水培生菜摘除黄腐叶的要求。

{"title":"A new removal method of yellow-rotten leaf for hydroponic lettuce with the flipping-tearing-twisting trajectory and its parameters optimisation","authors":"Yidong Ma , Chong Qi , Liming Zhou , Xin Jin , Bo Zhao , Xinping Li","doi":"10.1016/j.biosystemseng.2024.09.010","DOIUrl":"10.1016/j.biosystemseng.2024.09.010","url":null,"abstract":"<div><p>To intelligently remove the yellow-rotten leaf of hydroponic lettuce, a new leaf removal method was proposed. The lettuce position was adjusted for yellow-rotten leaf removal according to the visual recognition and localisation, and then the adsorbed yellow-rotten leaf was lifted by air pipes. Finally, the leaf was clamped and removed along the flipping-tearing-twisting trajectory. The adsorbing pressure, adsorbing position, and clamping position for yellow-rotten leaf removal were confirmed by the adsorbing and stretching tests. To improve the leaf removal success rate, the tearing angle, flipping angle, and torsional time radio were optimised by Box-Behnken tests. A quadratic model for the three factors and leaf removal success rate was established to analyse the orders of significance, and the order of significance for single factor was (i) the tearing angle, (ii) the flipping angle, and (iii) the torsional time radio. The order of significance for interaction terms was (i) the flipping angle and tearing angle, and (ii) the flipping angle and torsional time ratio. The solved optimal combination of factors was a flipping angle of 100.5°, a tearing angle of 131.0°, and a torsional time ratio of 0.68, which gave the maximum leaf removal success rate. The optimal combination of factors was verified, and the leaf removal process was shot by high speed camera. The verification tests showed that the maximum leaf removal success rate was 82.8%, and the leaf removal process took 6.58 s, meeting the requirements of yellow-rotten leaf removal for hydroponic lettuce.</p></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 175-190"},"PeriodicalIF":4.4,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0