Information Processing in Agriculture最新文献

英文中文

Mask R-CNN aided fruit surface temperature monitoring algorithm with edge compute enabled internet of things system for automated apple heat stress management 利用支持边缘计算的物联网系统的假面 R-CNN 辅助水果表面温度监测算法，实现苹果热应力自动管理

Information Processing in Agriculture

Pub Date : 2023-12-01 DOI: 10.1016/j.inpa.2023.12.001

Basavaraj R. Amogi, Rakesh Ranjan, L. Khot

引用次数: 0

Supervised and unsupervised machine learning approaches for prediction and geographical discrimination of Iranian saffron ecotypes based on flower-related and phytochemical attributes 基于花卉相关属性和植物化学属性的伊朗藏红花生态型预测和地理鉴别的有监督和无监督机器学习方法

Information Processing in Agriculture

Pub Date : 2023-12-01 DOI: 10.1016/j.inpa.2023.12.002

Seid Mohammad Alavi-Siney, Jalal Saba, Alireza Fotuhi Siahpirani, Jaber Nasiri

引用次数: 0

Front Matter 1 - Full Title Page (regular issues)/Special Issue Title page (special issues) 封面1 -完整的扉页(每期)/特刊扉页(每期)

Information Processing in Agriculture

Pub Date : 2023-11-15 DOI: 10.1016/S2214-3173(23)00083-5

引用次数: 0

A review on beef cattle supplementation technologies 肉牛补充饲料技术研究进展

Information Processing in Agriculture

Pub Date : 2023-11-01 DOI: 10.1016/j.inpa.2023.10.003

Guilherme Defalque, Ricardo Santos, Marcio Pache, Cristiane Defalque

The increase in the worldwide population reflects the expansion of beef cattle production and exportation. Although pasture is the world’s primary feed source of cattle food, failures in pasture management can endanger the productivity of beef cattle. An option for reducing the issues brought on by a shortage of nutritional resources and maintaining the fodder pasture is to perform the supplementation process on the livestock, even being one of the most costly activities in animal management. To decrease expenses and the need for labor to supplement the herd and improve animal performance, many parameters directly associated with supplementation must be monitored, such as environmental climate, soil and pasture characteristics, animal welfare, weight, and health. With so many parameters that impacts the decision on the quality and quantity of supplement to be supplied to the herd, sensors, remote sensing, and agricultural machinery are essential. The joint usage of these technologies in the supplementation process is complex, and there is a gap in decision-making systems for dynamic supplementation. Therefore, this work aims to carry out a comprehensive literature review that characterizes the main technologies related to the bovine supplementation process, mapping the main processes that involve the use of technological tools in the most diverse application domains. Finally, we propose a new Internet of Things architecture focused on the cattle supplementation process that combines technologies to compose a dynamic supplementation decision-making system capable of estimating the quantity and quality of the supplement that the herd needs in the presence of changes in the environment, pasture, and animals’ conditions parameters to reach production targets.

世界人口的增加反映了肉牛生产和出口的扩大。虽然牧场是世界上牛的主要饲料来源，但牧场管理的失败可能危及肉牛的生产力。减少营养资源短缺和维持饲料牧场所带来的问题的一个选择是对牲畜进行补充过程，即使是动物管理中最昂贵的活动之一。为了减少费用和对劳动力的需求，以补充畜群和提高动物的生产性能，必须监测许多与补充直接相关的参数，如环境气候、土壤和牧场特征、动物福利、体重和健康。由于有如此多的参数影响着决定向畜群提供补品的质量和数量，传感器、遥感和农业机械是必不可少的。这些技术在补充过程中的联合使用是复杂的，并且在动态补充的决策系统方面存在空白。因此，这项工作旨在进行全面的文献综述，描述与牛补充过程相关的主要技术，绘制涉及在最多样化的应用领域使用技术工具的主要过程。最后，我们提出了一种新的物联网架构，以牛的补充过程为重点，结合技术组成一个动态补充决策系统，能够在环境、牧场和动物条件参数发生变化的情况下，估计牛群所需的补充数量和质量，以达到生产目标。

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引用次数: 0

Automated tree ring detection of common Indiana hardwood species through deep learning: Introducing a new dataset of annotated images 通过深度学习自动检测印第安纳州常见硬木树种的年轮:引入一个新的带注释的图像数据集

Information Processing in Agriculture

Pub Date : 2023-11-01 DOI: 10.1016/j.inpa.2023.10.002

Fanyou Wu, Yunmei Huang, Bedrich Benes, Charles C. Warner, Rado Gazo

Tree-ring dating enables gathering necessary knowledge about trees, and it is essential in many areas, including forest management and the timber industry. Tree-ring dating can be conducted on either wood’s clean cross-sections or tree trunks’ rough end cross-sections. However, the measurement process is still time-consuming and frequently requires experts who use special devices, such as stereoscopes. Modern approaches based on image processing using deep learning have been successfully applied in many areas, and they can succeed in recognizing tree rings. While supervised deep learning-based methods often produce excellent results, they also depend on extensive datasets of tediously annotated data. To our knowledge, there are only a few publicly available ring image datasets with annotations. We introduce a new carefully captured dataset of images of hardwood species automatically annotated for tree ring detection. We capture each wood cookie twice, once in the rough form, similar to industrial settings, and then after careful cleaning, that reveals all growth rings. We carefully overlap the images and use them for an automatic ring annotation in the rough data. We then use the Feature Pyramid Network with Resnet encoder that obtains an overall pixel-level area under the curve score of 85.72% and ring level F1 score of 0.7348. The data and code are available at https://github.com/wufanyou/growth-ring-detection.

树木年轮测年可以收集有关树木的必要知识，它在许多领域都是必不可少的，包括森林管理和木材工业。树木年轮测年既可以在木材的干净横截面上进行，也可以在树干的粗端横截面上进行。然而，测量过程仍然很耗时，并且经常需要使用特殊设备的专家，例如立体镜。基于深度学习的图像处理的现代方法已经成功地应用于许多领域，它们可以成功地识别树木年轮。虽然基于监督的深度学习方法通常会产生出色的结果，但它们也依赖于大量带有冗长注释的数据集。据我们所知，只有少数公开可用的带有注释的环状图像数据集。我们引入了一个新的精心捕获的硬木物种图像数据集，该数据集自动注释用于树木年轮检测。我们将每个木质饼干捕获两次，一次是粗糙的形式，类似于工业环境，然后经过仔细的清洁，显示所有的生长环。我们仔细地重叠图像，并将它们用于粗糙数据中的自动环形注释。然后，我们使用带有Resnet编码器的特征金字塔网络，得到曲线下的总体像素级面积为85.72%，环级F1得分为0.7348。数据和代码可在https://github.com/wufanyou/growth-ring-detection上获得。

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引用次数: 0

Recognition and statistical method of cows rumination and eating behaviors based on Tensorflow.js 基于Tensorflow.js的奶牛反刍进食行为识别与统计方法

Information Processing in Agriculture

Pub Date : 2023-11-01 DOI: 10.1016/j.inpa.2023.11.002

Yu Zhang, Xiangting Li, Zhiqing Yang, Shaopeng Hu, Xiao Fu, Weizheng Shen

Information about dairy cow ruminating is closely associated with the health status of dairy cows. Therefore, it is of great significance to recognize and make statistics of dairy cows’ ruminating and feeding behavior. Concerning conventional recognition methods which are dependent on contact type devices, they have some defects of poor instantaneity and strong stress responses. As for recognition based on machine vision, it needs to transmit masses of data and raises high requirements for the cloud server and network performance. According to principles of edge computing, the model is deployed via Tensorflow.js in an edge device in the present study, constructing a recognition and statistical system for ruminating and feeding behavior of dairy cows. Through the application programming interface (API) of the browser, an edge device is able to invoke a camera and acquire dairy cow images. Then, the images can be inputted in the SSD MobileNet V2 model, which is followed by inference based on browser hashrate. Moreover, the edge device merely uploads recognition results to the cloud server for statistics, which features high instantaneity and compatibility. In terms of recognizing ruminating and feeding behavior of dairy cows, the proposed system has a precision ratio of 96.50%, a recall rate of 91.77%, an F1-score of 94.08%, specificity of 91.36%, and accuracy of 91.66%. This suggests that the proposed method is effective in recognizing dairy cow behavior.

奶牛反刍信息与奶牛的健康状况密切相关。因此，对奶牛的反刍和摄食行为进行识别和统计具有重要意义。传统的基于接触式器件的识别方法存在实时性差、应力响应强等缺陷。基于机器视觉的识别需要传输大量的数据，对云服务器和网络性能提出了很高的要求。根据边缘计算原理，本研究通过Tensorflow.js将该模型部署在边缘设备上，构建奶牛反刍和摄食行为的识别与统计系统。通过浏览器的应用程序编程接口(API)，边缘设备可以调用摄像头获取奶牛图像。然后，将图像输入到SSD MobileNet V2模型中，然后根据浏览器的哈希值进行推理。边缘设备仅将识别结果上传到云服务器进行统计，实时性和兼容性高。在奶牛反刍和采食行为识别方面，系统准确率为96.50%，召回率为91.77%，f1评分为94.08%，特异性为91.36%，准确率为91.66%。这表明该方法在奶牛行为识别方面是有效的。

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引用次数: 0

A hybrid genetic slime mould algorithm for parameter optimization of field-road trajectory segmentation models 用于田间道路轨迹分割模型参数优化的混合遗传粘菌算法

Information Processing in Agriculture

Pub Date : 2023-11-01 DOI: 10.1016/j.inpa.2023.11.003

Jiawen Pan, Caicong Wu, Weixin Zhai

引用次数: 0

Evaluation of the applicability of a metal oxide semiconductor gas sensor for methane emissions from agriculture 金属氧化物半导体气体传感器对农业甲烷排放的适用性评估

Information Processing in Agriculture

Pub Date : 2023-11-01 DOI: 10.1016/j.inpa.2023.11.001

Bastiaan Molleman, Enrico Alessi, Fabio Passaniti, Karen Daly

This work investigated the potential of metal oxide semiconductor (MOS) gas sensors for environmental monitoring of methane. Calibrations were performed under controlled conditions in the lab, and under semi-controlled conditions in the field, using a modified head space chamber set-up. Concentrations up to ±300 ppm methane were tested. The relationship between sensor conductance and methane concentrations could be very well described using principles from adsorption theory. The adjustable parameters were background conductance G0, a sensitivity constant S and a non-ideality coefficient n, where n has a non-rational value between 0 and 1. Sensor behaviour was very different in dry air than in humid air, with the background conductance increasing approximately tenfold and sensitivity decreasing between 20 fold and 80 fold, while the non-ideality coefficient increased from ±0.4 to ±0.6. Nevertheless, at high methane concentrations comparable conductance values were recorded in dry and humid air. The standard deviation of predicted values was 1.6 μS.for the least well described dataset. Using the corresponding calibration curve, a detection limit of 11 ppm is calculated for humid ambient air. This values suggests that MOS sensor are adequately sensitive to be used for methane detection in an agricultural context.

本文研究了金属氧化物半导体(MOS)气体传感器用于甲烷环境监测的潜力。校准在实验室的受控条件下进行，在现场的半受控条件下进行，使用改进的头部空间室设置。测试的甲烷浓度高达±300 ppm。传感器电导与甲烷浓度之间的关系可以用吸附理论的原理很好地描述。可调参数为背景电导G0、灵敏度常数S和非理想系数n，其中n为0 ~ 1之间的无理数。在干燥空气中，传感器的行为与潮湿空气中有很大的不同，背景电导增加了大约10倍，灵敏度下降了20到80倍，而非理想系数从±0.4增加到±0.6。然而，在高甲烷浓度下，在干燥和潮湿空气中记录的电导值相当。预测值的标准差为1.6 μS。对于描述最少的数据集。使用相应的校准曲线，计算出潮湿环境空气的检测限为11ppm。这一数值表明，MOS传感器具有足够的灵敏度，可用于农业环境中的甲烷检测。

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引用次数: 0

Attention-based generative adversarial networks for aquaponics environment time series data imputation 基于注意力的鱼菜共生环境时间序列数据输入生成对抗网络

Information Processing in Agriculture

Pub Date : 2023-10-01 DOI: 10.1016/j.inpa.2023.10.001

Keyang Zhong, Xueqian Sun, Gedi Liu, Yifeng Jiang, Yi Ouyang, Yang Wang

Environmental parameter data collected by sensors for monitoring the environment of agricultural facility operations are usually incomplete due to external environmental disturbances and device failures. And the missing of collected data is completely at random. In practice, missing data could create biased estimations and make multivariate time series predictions of environmental parameters difficult, leading to imprecise environmental control. A multivariate time series imputation model based on generative adversarial networks and multi-head attention (ATTN-GAN) is proposed in this work to reducing the negative consequence of missing data. ATTN-GAN can capture the temporal and spatial correlation of time series, and has a good capacity to learn data distribution. In the downstream experiments, we used ATTN-GAN and baseline models for data imputation, and predicted the imputed data, respectively. For the imputation of missing data, over the 20%, 50% and 80% missing rate, ATTN-GAN had the lowest RMSE, 0.1593, 0.2012 and 0.2688 respectively. For water temperature prediction, data processed with ATTN-GAN over MLP, LSTM, DA-RNN prediction methods had the lowest MSE, 0.6816, 0.8375 and 0.3736 respectively. Those results revealed that ATTN-GAN outperformed all baseline models in terms of data imputation accuracy. The data processed by ATTN-GAN is the best for time series prediction.

由于外部环境干扰和设备故障，用于监测农业设施运行环境的传感器采集的环境参数数据通常是不完整的。收集数据的缺失完全是随机的。在实践中，缺失的数据可能会产生有偏差的估计，并使环境参数的多元时间序列预测变得困难，从而导致不精确的环境控制。本文提出了一种基于生成对抗网络和多头注意(ATTN-GAN)的多元时间序列输入模型，以减少数据缺失的负面影响。ATTN-GAN能够捕捉时间序列的时空相关性，具有良好的数据分布学习能力。在下游实验中，我们使用ATTN-GAN和基线模型进行数据输入，并分别对输入数据进行预测。对于缺失数据的imputation，在20%、50%和80%缺失率下，ATTN-GAN的RMSE最低，分别为0.1593、0.2012和0.2688。在水温预测中，采用ATTN-GAN处理的数据的MSE分别为0.6816、0.8375和0.3736，低于MLP、LSTM和DA-RNN方法。这些结果表明，ATTN-GAN在数据输入精度方面优于所有基线模型。ATTN-GAN处理的数据对时间序列预测效果最好。

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引用次数: 0

An investigation on the best-fit models for sugarcane biomass estimation by linear mixed-effect modelling on unmanned aerial vehicle-based multispectral images: A case study of Australia 基于无人机的多光谱图像线性混合效应模型估算甘蔗生物量的最佳拟合模型研究——以澳大利亚为例

Information Processing in Agriculture

Pub Date : 2023-09-01 DOI: 10.1016/j.inpa.2022.03.005

Sharareh Akbarian , Chengyuan Xu , Weijin Wang , Stephen Ginns , Samsung Lim

Due to the worldwide population growth and the increasing needs for sugar-based products, accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth. This research aims to find the imperative predictors correspond to the random and fixed effects to improve the accuracy of wet and dry sugarcane biomass estimations by integrating ground data and multi-temporal images from Unmanned Aerial Vehicles (UAVs). The multispectral images and biomass measurements were obtained at different sugarcane growth stages from 12 plots with three nitrogen fertilizer treatments. Individual spectral bands and different combinations of the plots, growth stages, and nitrogen fertilizer treatments were investigated to address the issue of selecting the correct fixed and random effects for the modelling. A model selection strategy was applied to obtain the optimum fixed effects and their proportional contribution. The results showed that utilizing Green, Blue, and Near Infrared spectral bands on models rather than all bands improved model performance for wet and dry biomass estimates. Additionally, the combination of plots and growth stages outperformed all the candidates of random effects. The proposed model outperformed the Multiple Linear Regression (MLR), Generalized Linear Model (GLM), and Generalized Additive Model (GAM) for wet and dry sugarcane biomass, with coefficients of determination (R²) of 0.93 and 0.97, and Root Mean Square Error (RMSE) of 12.78 and 2.57 t/ha, respectively. This study indicates that the proposed model can accurately estimate sugarcane biomasses without relying on nitrogen fertilizers or the saturation/senescence problem of Vegetation Indices (VIs) in mature growth stages.

由于世界人口的增长和对糖基产品需求的增加，准确估计甘蔗生物量对于精确监测甘蔗生长至关重要。本研究旨在通过整合地面数据和无人机多时相影像，寻找与随机效应和固定效应相对应的命令式预测因子，以提高甘蔗干湿生物量估算的精度。对12个施氮地块进行了不同生育期的多光谱成像和生物量测定。研究了不同的光谱波段和不同的地块、生长阶段和氮肥处理组合，以解决为模型选择正确的固定和随机效应的问题。采用模型选择策略获得最优固定效应及其比例贡献。结果表明，在模型上使用绿色、蓝色和近红外光谱波段，而不是所有波段，可以提高模型对干湿生物量估算的性能。此外，地块和生长阶段的组合优于随机效应的所有候选。该模型对湿甘蔗和干甘蔗生物量的影响优于多元线性回归(MLR)、广义线性模型(GLM)和广义加性模型(GAM)，决定系数(R2)分别为0.93和0.97，均方根误差(RMSE)分别为12.78和2.57 t/ha。本研究表明，该模型可以在不依赖于氮肥的情况下准确估算甘蔗生物量，也不依赖于成熟生长阶段植被指数(VIs)的饱和/衰老问题。

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