| 1. |
- A. Bateki, Christian, et al.
(author)
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Of milk and mobiles: Assessing the potential of cellphone applications to reduce cattle milk yield gaps in Africa using a case study
- 2021
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In: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699. ; 191
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Journal article (peer-reviewed)abstract
- There are growing expectations that Information and Communication Technology (ICT) applications could help improve on-farm yields amongst smallholder farmers in developing countries, and consequently, food and nutrition security. However, few studies have quantified the actual contribution of ICT applications on farmers’ yields, and these studies predominantly focused on crop production. We assessed the potential of ICT applications to close milk yield gaps among small- and medium scale dairy cattle farmers in Africa. First, we developed a theoretical framework summarizing biophysical and socio-economic constraints that foster milk yield gaps and discussed which constraints can be addressed using ICT applications. Second, using a case study of a feeding advice application for dairy cattle pre-tested with farmers in rural Kenya, we analyzed how much stand-alone the application could contribute to close dairy cattle milk yield gaps. Our findings suggest that ICT applications could help address some existing biophysical and socio-economic constraints fostering milk yield gaps, including data collection for breeding programs, feeding management advice, and facilitating access to markets and capital. Our stand-alone ICT application closed yield gaps by 2 % to 6 % on representative farms. Several factors may explain the limited actual contribution of selected ICT applications to reduce existing milk yield gaps, including the quality of the input data and models used in ICT applications, and more structural constraints that cannot be addressed by digital tools. Therefore, although ICT applications could help address constraints to achieving higher milk yields on dairy farms, a significant contribution to improve yields may only be achieved when conditions surrounding their use are adequate.
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| 2. |
- Araujo Sandroni, Murilo, et al.
(author)
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In-field classification of the asymptomatic biotrophic phase of potato late blight based on deep learning and proximal hyperspectral imaging
- 2023
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In: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 205
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Journal article (peer-reviewed)abstract
- Effective detection of potato late blight (PLB) is an essential aspect of potato cultivation. However, it is a challenge to detect late blight in asymptomatic biotrophic phase in fields with conventional imaging approaches because of the lack of visual symptoms in the canopy. Hyperspectral imaging can capture spectral signals from a wide range of wavelengths also outside the visual wavelengths. Here, we propose a deep learning classification architecture for hyperspectral images by combining 2D convolutional neural network (2D-CNN) and 3D-CNN with deep cooperative attention networks (PLB-2D-3D-A). First, 2D-CNN and 3D-CNN are used to extract rich spectral space features, and then the attention mechanism AttentionBlock and SE-ResNet are used to emphasize the salient features in the feature maps and increase the generalization ability of the model. The dataset is built with 15,360 images (64x64x204), cropped from 240 raw images captured in an experimental field with over 20 potato genotypes. The accuracy in the test dataset of 2000 images reached 0.739 in the full band and 0.790 in the specific bands (492 nm, 519 nm, 560 nm, 592 nm, 717 nm and 765 nm). This study shows an encouraging result for classification of the asymptomatic biotrophic phase of PLB disease with deep learning and proximal hyperspectral imaging.
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| 3. |
- Barros, T., et al.
(author)
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Multispectral vineyard segmentation : A deep learning comparison study
- 2022
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In: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 195
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Journal article (peer-reviewed)abstract
- Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this work, a study is presented of semantic segmentation for vine detection in real-world vineyards by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data have been collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-definition RGB camera and a five-band multispectral and thermal camera. Extensive experiments using deep-segmentation networks and unsupervised methods have been performed on multimodal datasets representing four distinct vineyards located in the central region of Portugal. The reported results indicate that SegNet, U-Net, and ModSegNet have equivalent overall performance in vine segmentation. The results also show that multimodality slightly improves the performance of vine segmentation, but the NIR spectrum alone generally is sufficient on most of the datasets. Furthermore, results suggest that high-definition RGB images produce equivalent or higher performance than any lower resolution multispectral band combination. Lastly, Deep Learning (DL) networks have higher overall performance than classical methods. The code and dataset are publicly available on https://github.com/Cybonic/DL_vineyard_segmentation_study.git.
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| 4. |
- Bonestroo, John, et al.
(author)
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Forecasting chronic mastitis using automatic milking system sensor data and gradient-boosting classifiers
- 2022
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In: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 198
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Journal article (peer-reviewed)abstract
- Although most of the losses due to mastitis per case in dairy production are estimated to be caused by clinical cases, subclinical cases, especially chronic, can also be problematic due to milk production losses and the risk of transmission of pathogens. Knowing which subclinical mastitis cases will become chronic at an early stage would be helpful in intervening in these cases. Automatic milking systems (AMS) can collect data on mastitis indicators such as conductivity, Somatic cell count (SCC), and blood in the milk for each milking. The aim of this study was to develop a sensor-based prediction model using SCC, conductivity, blood in the milk, parity, milk diversion, time interval between milkings, milk yield and DIM that forecasts the chronicity in subclinical mastitis cases after an initial increase in SCC. We used sensor data from 14 European and North American dairy farms (with herd sizes of lactating cows ranging from 55 to 638 cows and herd mean parities between 2.00 and 3.19) with an AMS and an online cell counter, measuring SCC. Typically, a threshold of 200,000 SCC/ml has been used to distinguish cows with subclinical mastitis from healthy cows. We used gradient-boosting trees and sensor data to forecast whether the SCC would decrease structurally below 200,000 SCC/ml in 50 days after the day at which the prediction was performed. Data from 30 and 15 days prior to the day where the forecast was made, was used. The model was trained on data from seven randomly selected dairy farms from the dataset and the data of the remaining seven dairy farms were used to estimate the predictive performance. These results were compared with two approaches that simulate how farmers would diagnose chronic mastitis with a simple prediction rule based on close-to-daily SCC (frequent sampling approach), and on less frequent monthly SCC (monthly sampling approach). We used accuracy, Matthew's correlation coefficient (MCC), and Area under the Curve (AUC) as metrics to assess the forecasting performance of the chronic mastitis prediction model. On average, the forecast model, using 30 days of sensor data prior to the day of prediction, outperformed the approaches according to the accuracy (chronic mastitis prediction model: 0.888, frequent sampling approach: 0.848, and monthly sampling approach: 0.865), MCC (chronic mastitis prediction model: 0.712, frequent sampling approach: 0.630, and monthly sampling approach: 0.552), and AUC metrics (chronic mastitis prediction model: 0.964 and frequent sampling approach: 0.941) metrics. The results also indicate that shortening the input requirement from 30 days of prior sensor data to 15 days has a limited effect on the performance of the model. Overall, this study shows that it is possible with a high accuracy to predict the future chronic mastitis status using past sensor data and machine learning models.
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| 5. |
- Bosona, Techane, et al.
(author)
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Multipurpose simulation model for pasture-based mobile Automated Milking and Marketing System, Part-I: Pasture, milk yield, and milk marketing characteristics
- 2021
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In: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 190
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Journal article (peer-reviewed)abstract
- It is essential to promote sustainable dairy farming which could lead to improved animal welfare, economic benefits, biodiversity and environmental benefits, milk quality, and customer satisfaction. In this regard, a mobile automated milking system (AMS) could contribute a lot. However, mobile AMS is a new innovative system which is not investigated well. Therefore, a simplified and integrated management approach should be introduced. The main objective of this study was to develop a multipurpose simulation model (DigiMilk model) specific to pasture-based mobile AMS. The model comprises five major subsystems: Pasture yield as dry matter (DM) and grazing characteristics; AMS Milking and milk yield characteristics; Milk handling and marketing; Resource consumption; and Economic assessment. This paper (Part-I) focuses on the first three components while the remaining two subsystems would be addressed in Part-II of this paper. DigiMilk model was built in MATLAB-Simulink environment. It was tested and evaluated using mainly secondary data and limited primary information acquired from a dairy farm in central Sweden. In this initial analysis, a continuous stocking system on pasture was assumed to be implemented from May 15 till September 15. Multiple sensitivity analyses were successfully conducted to get more insights. The results indicated that, considering maximum pasture growth rate of 77 kgDM day−1ha−1, the accumulated average pasture yield, over the grazing season, was estimated to be 6928 kgDM ha−1. For cows with average grazing rate of 16–18 kgDM day−1cow−1, the stocking rate of 3 cow ha−1 could lead to good performance of grazing management. When stocking rate and grazing rate of 3 cow ha−1 and 16 kgDM day−1cow−1 were considered respectively, the cumulative milk yield values (excluding amount consumed by calves) over the grazing season were estimated to be 2101 L cow−1 and 6303 L ha−1. Out of this 6303 L ha−1, 2952 L ha−1was estimated to be sold on-site, using milk vending machine (MVM), while 3351 L ha−1 was to be delivered to super market. The accuracy of results from the the simulation model could be improved with future work with more real data from actual demonstration of mobile AMS over the entire grazing season. In addition to its capacity to serve as an integrated decision making tool, DigiMilk model enables to have organized digital data that could be useful for future researches to evaluate the environmental and/or economic performances of pasture-based dairy systems with mobile AMS.
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| 6. |
- Forghani, Kamran, et al.
(author)
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Maximizing value yield in wood industry through flexible sawing and product grading based on wane and log shape
- 2024
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In: Computers and Electronics in Agriculture. - : Elsevier. - 0168-1699 .- 1872-7107. ; 216
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Journal article (peer-reviewed)abstract
- The optimization of sawing processes in the wood industry is critical for maximizing efficiency and profitability. The introduction of computerized tomography scanners provides sawmill operators with three-dimensional internal models of logs, which can be used to assess value and yield more accurately. We present a methodology for solving the sawing optimization problem employing a flexible sawing scheme that allows greater flexibility in cutting logs into products while considering product quality classes influenced by wane defects. The methodology has two phases: preprocessing and optimization. In the preprocessing phase, two alternative algorithms are given that generate and evaluate the potential sawing positions of products by considering the 3D surface of the log, product size requirements, and product quality classes. In the optimization phase, a maximum set-packing problem is solved for the preprocessed data using mixed-integer programming (MIP), aiming to obtain a feasible cut pattern that maximizes value yield. This is implemented in a system named FlexSaw, which takes advantage of parallel computation during the preprocessing phase and utilizes a MIP solver during the optimization phase. The proposed sawing methods are evaluated on the Swedish Pine Stem Bank. Additionally, FlexSaw is compared with an existing tool that utilizes cant sawing. Results demonstrate the superiority of flexible sawing. While the practical feasibility of implementing a flexible way of sawing logs is constrained by the limitations of current sawmill machinery, the potential increase in yield promotes the exploration of alternative machinery in the wood industry.
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| 7. |
- Guo, Zhiming, et al.
(author)
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Comparative study of Vis/NIR reflectance and transmittance method for on-line detection of strawberry SSC
- 2024
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In: Computers and Electronics in Agriculture. - : Elsevier. - 0168-1699 .- 1872-7107. ; 218
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Journal article (peer-reviewed)abstract
- Strawberry, as a fragile and vulnerable fruit, the realization of automatic sorting is conducive to improve the intelligent level of strawberry industry and improve the ability of product quality management. An on-line soluble solids content (SSC) detection prototype which can protect the strawberry from mechanical damage was researched and developed. The reflectance and transmittance of visible and near infrared (Vis/NIR) spectra were acquired by the prototype respectively, and the performances of the two spectra on the SSC detection performance of strawberry were compared. Four feature selection algorithms like competitive adaptive reweighted sampling (CARS) ware used for reflectance and transmittance spectra to reduce the spectra complexity, improve the strawberry SSC detection accuracy and optimize the running time of the prototype. The comparison showed that the transmittance spectra can reflect the internal SSC information of strawberry better. Then the results of feature variable selection showed that strawberry transmittance spectra combined with CARS algorithm achieved the best result of SSC prediction, and the prediction correlation coefficient (Rp) was 0.928, the root mean square error of prediction (RMSEP) was 0.412 Brix, and the residual predictive deviation (RPD) value was 2.670. The CARS-PLS model for reflectance spectra also obtained the optimization result in the reflectance group, but its Rp, RMSEP and RPD value was 0.812, 0.587 Brix and 1.670 respectively, which still did not meet the reliability of application. The results demonstrated that the Vis/NIR transmittance spectra have great application potential in strawberry on-line internal quality detection.
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| 8. |
- Guo, Zhiming, et al.
(author)
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Detection model transfer of apple soluble solids content based on NIR spectroscopy and deep learning
- 2023
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In: Computers and Electronics in Agriculture. - : Elsevier. - 0168-1699 .- 1872-7107. ; 212
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Journal article (peer-reviewed)abstract
- Transfer and updating of near infrared (NIR) spectroscopy model of fruit internal quality has become the focus of the industrial application. Internet of Things (IoT) and deep learning (DL) were proposed to perform soluble solids content (SSC) model transfer of apple by NIR. A model transfer platform including low-power handheld internal quality terminal and interacting cloud data system had been constructed. An autoencoder (AE) neural network model was developed for the spectral correction and model transfer. The average time for transmitting detection results to the detection terminal was 1.5 to 2.0 s, with a 100% effective transmission rate. After 5000 iterations of training, the correlation coefficient of different detection terminals improved by 55%, and the root mean square error was reduced by 94%. Selected samples from the second batch of apples detected by the No. 1 detection terminal were added to the original neural network for training. After adding 30 samples, the correlation coefficient increased by 13% and the root mean square error decreased by 90%. The results demonstrated that the AE neural network for spectral correction was effective in eliminating differences between devices and significantly reducing the impact of different detection terminals on the accuracy of NIR detection of SSC in apples. Therefore, the NIR detection model transfer technique could be practically exploited for fruit quality control assessment using different detection terminals.
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| 9. |
- Hosseini, S. Ahmad, et al.
(author)
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A scenario-based metaheuristic and optimization framework for cost-effective machine-trail network design in forestry
- 2023
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In: Computers and Electronics in Agriculture. - : Elsevier. - 0168-1699 .- 1872-7107. ; 212
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Journal article (peer-reviewed)abstract
- Designing an optimal machine trail network is a complex locational problem that requires an understanding of different machines’ operations and terrain features as well as the trade-offs between various objectives. With the overall goal to minimize the operational costs of the logging operation, this paper proposes a mathematical optimization model for the trail network design problem and a greedy heuristic method based on different randomized search scenarios aiming to find the optimal location of machine trails —with potential to reduce negative environmental impact. The network is designed so that all trees can be reached and adapted to how the machines can maneuver while considering the terrain elevation’s influence. To examine the effectiveness and practical performance of the heuristic and the optimization model, it was applied in a case study on four harvest units with different topologies and shapes. The computational experiments show that the heuristic can generate solutions that outperform the solutions corresponding to conventional, manual designs within practical time limits for operational planning. Moreover, to highlight certain features of the heuristic and the parameter settings’ effect on its performance, we present an extensive computational sensitivity analysis.
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| 10. |
- Kurtser, Polina, 1990-, et al.
(author)
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RGB-D datasets for robotic perception in site-specific agricultural operations : a survey
- 2023
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In: Computers and Electronics in Agriculture. - : Elsevier. - 0168-1699 .- 1872-7107. ; 212
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Journal article (peer-reviewed)abstract
- Fusing color (RGB) images and range or depth (D) data in the form of RGB-D or multi-sensory setups is a relatively new but rapidly growing modality for many agricultural tasks. RGB-D data have potential to provide valuable information for many agricultural tasks that rely on perception, but collection of appropriate data and suitable ground truth information can be challenging and labor-intensive, and high-quality publicly available datasets are rare. This paper presents a survey of the existing RGB-D datasets available for agricultural robotics, and summarizes key trends and challenges in this research field. It evaluates the relative advantages of the commonly used sensors, and how the hardware can affect the characteristics of the data collected. It also analyzes the role of RGB-D data in the most common vision-based machine learning tasks applied to agricultural robotic operations: visual recognition, object detection, and semantic segmentation, and compares and contrasts methods that utilize 2-D and 3-D perceptual data.
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