| 1. |
- Ahlman, Linnéa, 1987, et al.
(författare)
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Using chlorophyll a fluorescence gains to optimize LED light spectrum for short term photosynthesis
- 2017
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Ingår i: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699. ; 142, s. 224-234
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Tidskriftsartikel (refereegranskat)abstract
- When changing from the traditional high pressure sodium (HPS) lamps to light emitting diode (LED) lamps there is a quite unexplored energy saving potential in the fact that they are far better suited for control, since both spectrum and light intensity can be adjusted. This work aims at finding a way to automatically adjust the spectrum of a LED lamp, equipped with several different types of LEDs, to maximize plant growth by feedback of a remote online measure correlated with growth.A series of experiments were conducted on basil plants in order to examine whether remotely sensed steady-state chlorophyll fluorescence (F740) can be used for this purpose, and if its derivatives (fluorescence gains) w.r.t. applied powers change relative to each other for different light intensities and spectraA strong correlation between F740 and photosynthetic rate was indeed found. However, the order (w.r.t. LED type) of the fluorescence gains was only moderately affected by the light intensities and spectra investigated. The gain was highest w.r.t. red light (630 nm), though, when taking the electrical efficiencies of individual LED types into consideration, blue LEDs (450 nm) were equally, or even more efficient than the red onesAn online controller to regulate optimal spectrum for basil appears to be unnecessary. However, the fluorescence gains could be used to adapt to changes in the efficiencies when crops and operating conditions change, or when the diodes degrade. The method also shows promise as a tool to find optimal light intensity levels as well as identifying plant stress.
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| 2. |
- Awad, Ali Ismail
(författare)
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From classical methods to animal biometrics: A review on cattle identification and tracking
- 2016
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Ingår i: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 123, s. 423-435
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Tidskriftsartikel (refereegranskat)abstract
- Cattle, buffalo and cow, identification has recently played an influential role towards understanding disease trajectory, vaccination and production management, animal traceability, and animal ownership assignment. Cattle identification and tracking refers to the process of accurately recognizing individual cattle and their products via a unique identifier or marker. Classical cattle identification and tracking methods such as ear tags, branding, tattooing, and electrical methods have long been in use; however, their performance is limited due to their vulnerability to losses, duplications, fraud, and security challenges. Owing to their uniqueness, immutability, and low costs, biometric traits mapped into animal identification systems have emerged as a promising trend. Biometric identifiers for beef animals include muzzle print images, iris patterns, and retinal vascular patterns. Although using biometric identifiers has replaced human experts with computerized systems, it raises additional challenges in terms of identifier capturing, identification accuracy, processing time, and overall system operability. This article reviews the evolution in cattle identification and tracking from classical methods to animal biometrics. It reports on traditional animal identification methods and their advantages and problems. Moreover, this article describes the deployment of biometric identifiers for effectively identifying beef animals. The article presents recent research findings in animal biometrics, with a strong focus on cattle biometric identifiers such as muzzle prints, iris patterns, and retinal vascular patterns. A discussion of current challenges involved in the biometric-based identification systems appears in the conclusions, which may drive future research directions.
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| 3. |
- Bånkestad, Daniel, et al.
(författare)
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Growth tracking of basil by proximal remote sensing of chlorophyll fluorescence in growth chamber and greenhouse environments
- 2016
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Ingår i: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699. ; 128, s. 77-86
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Tidskriftsartikel (refereegranskat)abstract
- Remote sensing is a promising tool for plant phenotyping and precision farming, as it allows for non-invasive, fast and automated measurements of relevant plant traits with spatial and temporal resolution. The simplest and most used remote sensing application in the field is to use reflectance vegetation indices, based on the optical properties of chlorophyll, as indicators of variables of interest. However, the applicability is limited by their sensitivity to environmental conditions and canopy structure. Another remotely sensed signal related to chlorophyll is chlorophyll fluorescence. Compared to reflectance it is plant specific and directly linked to plant physiological processes; but it is also weak, which complicates its use for in-field applications. This study evaluates the performance of an active proximal remote sensing system utilizing the chlorophyll fluorescence ratio method, measuring the ratio of red fluorescence to far-red fluorescence (termed SFR), for the assessment of growth and biomass as an alternative or complement to reflectance vegetation indices. Basil plants were subject to chlorophyll fluorescence and weight measurements periodically throughout commercial growth cycles, both in a laboratory and commercial greenhouse environment. In the laboratory, SFR showed a strong linear relationship with dry weight on logarithmic scales. Further characterization of the method indicated that it is independent of background light and the same growth dynamics is obtained irrespective of point in time during chlorophyll fluorescence induction. The same trend that was observed in the laboratory was also observed in the greenhouse, but varying background light from the sun and from supplemental lighting added complexity that needs to be addressed in further studies. To our knowledge, the strong link between SFR and biomass, both in a closed environment and greenhouse setting, has not so clearly been demonstrated on canopy level before. Owing to the simplicity of the method, being relatively cheap and fast, it has potential for commercial applications.
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| 4. |
- Carstensen, Anna-Maria, 1982, et al.
(författare)
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Remote detection of light tolerance in Basil through frequency and transient analysis of light induced fluorescence
- 2016
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Ingår i: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699. ; 127, s. 289-301
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Tidskriftsartikel (refereegranskat)abstract
- © 2016 Elsevier B.V.Artificial lighting control in industrial scale greenhouses has a large potential for increased crop yields, energy savings and timing in greenhouse production. One key component in controlling greenhouse lighting is continuous and accurate measurement of plant performance. This paper presents a novel concept for remote detection of plant performance based on the dynamics of chlorophyll fluorescence (CF) signals induced by a LED-lamp. The dynamic properties of the CF is studied through fitting a linear dynamic model to CF data. The hypothesis is that changes in photochemistry affects the fluorescence dynamics and can therefore be detected as changes in the model parameters and properties. The dynamics was studied in experiments using a sinusoidal varying light intensity (period 60 s) or step changes (step length 300 s). Experiments were performed in a controlled light environment on Basil plants acclimated to different light intensities. It is concluded that the capacity to use a certain light intensity is reflected by how fast and how complex the dynamics are. In particular, the results show that optimal model order is a potential indicator of light tolerance in plants that could be a valuable feedback signal for lighting control in greenhouses.
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| 5. |
- Daum, Thomas, 1990, et al.
(författare)
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Smartphone apps as a new method to collect data on smallholder farming systems in the digital age: A case study from Zambia
- 2018
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Ingår i: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699. ; 153, s. 144-150
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Tidskriftsartikel (refereegranskat)abstract
- Across the developing world, the spread of mobile- and smartphones has led to a surge in mobile services for rural populations. While the potentials of mobile services to provide development opportunities for smallholder farmers are widely acknowledged, the potentials to use smartphone applications to collect data on smallholder farming systems are little explored. Yet, researchers studying farming systems need good quality data. So far, data on smallholder farming systems is typically collected using household surveys. Survey questions are prone to recall biases, however, which can be substantial. This paper assesses whether smartphone can be used to collect data in real time and thus increase the accuracy of socioeconomic and agronomic data collection. In this paper, we present a smartphone application that was developed for this purpose. We use the application to analyze the effects of agricultural mechanization on intra-household time-use and nutrition in rural Zambia. While the early, descriptive results shed interesting light on the effects of mechanization, the contribution of this study is primarily methodological. The study highlights the potentials of using smartphone applications to collect socioeconomic and agronomic data on smallholder-farming systems, potentially in real time. It also suggests ways to combine data recorded by respondents with built-in sensors of smartphones and external sensors and thus shows fascinating new pathways for researchers in the digital age.
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| 6. |
- De Conto, Tiago, et al.
(författare)
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Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning
- 2017
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Ingår i: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 143, s. 165-176
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Tidskriftsartikel (refereegranskat)abstract
- The present study assessed the performance of three different methods of stem denoising and three different methods of stem modelling on terrestrial laser scanner (TLS) point clouds containing single trees - thus validating all tested methods, which were made available as an open source software package in the R language. The methods were adapted from common TLS stem detection techniques and rely on finding one main trunk in a point cloud by denoising the data to precisely extract only stem points, followed by a circle or cylinder fitting procedure on stem segments. The combination of the Hough transformation stem denoising method and the iteratively reweighted total least squares modelling method had best overall performance - achieving 2.15 cm of RMSE and 1.09 cm of bias when estimating diameters along the stems, detecting 80% of all stem segments measured on field surveys. All algorithms performed better on point clouds of boreal species, in comparison to tropical Eucalypt. The point clouds underwent reduction of point density, which increased processing speed on the stem denoising algorithms, with little effect on diameter estimation quality.
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| 7. |
- Gronskyte, Ruta, et al.
(författare)
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Pig herd monitoring and undesirable tripping and stepping prevention
- 2015
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Ingår i: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 119, s. 51-60
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Tidskriftsartikel (refereegranskat)abstract
- Humane handling and slaughter of livestock are of major concern in modern societies. Monitoring animal wellbeing in slaughterhouses is critical in preventing unnecessary stress and physical damage to livestock, which can also affect the meat quality. The goal of this study is to monitor pig herds at the slaughterhouse and identify undesirable events such as pigs tripping or stepping on each other. In this paper, we monitor pig behavior in color videos recorded during unloading from transportation trucks. We monitor the movement of a pig herd where the pigs enter and leave a surveyed area. The method is based on optical flow, which is not well explored for monitoring all types of animals, but is the method of choice for human crowd monitoring. We recommend using modified angular histograms to summarize the optical flow vectors. We show that the classification rate based on support vector machines is 93% of all frames. The sensitivity of the model is 93.5% with 90% specificity and 6.5% false alarm rate. The radial lens distortion and camera position required for convenient surveillance make the recordings highly distorted. Therefore, we also propose a new approach to correct lens and foreshortening distortions by using moving reference points. The method can be applied real-time during the actual unloading operations of pigs. In addition, we present a method for identification of the causes leading to undesirable events, which currently only runs off-line. The comparative analysis of three drivers, which performed the unloading of the pigs from the trucks in the available datasets, indicates that the drivers perform significantly differently. Driver 1 has 2.95 times higher odds to have pigs tripping and stepping on each other than the two others, and Driver 2 has 1.11 times higher odds than Driver 3.
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| 8. |
- Guijarro, M., et al.
(författare)
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Discrete wavelets transform for improving greenness image segmentation in agricultural images
- 2015
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Ingår i: Computers and Electronics in Agriculture. - : ELSEVIER SCI LTD. - 0168-1699 .- 1872-7107. ; 118, s. 396-407
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Tidskriftsartikel (refereegranskat)abstract
- We propose a segmentation strategy for agricultural images in order to successfully distinguish between both soil and green parts, the last ones including weeds and crop plants, based on discrete wavelets transform. Vegetation indices have been commonly used for greenness image segmentation, but improvements are still possible. In agricultural images weeds and crops plants display high spatial variability with irregular and random distributions. Textures descriptors have the ability to capture this information, which conveniently combined with vegetation indices improve the greenness segmentation results. The proposed approach consists of the following steps: (a) greenness extraction based on vegetation indices; (b) application of the wavelets transform to the resulting image, allowing the extraction of spatial structures in three bands (horizontal, vertical and diagonal) containing detailed information; (c) use of texture descriptors to capture the spatial variability in the three bands; (d) combination of greenness and texture information, in the approximation coefficients of the wavelets transform, for enhancing plants (weeds and crops) identification; and (e) application of an image thresholding method for final image identification. The wavelets transform allows both capture of spatial texture and its fusion with the greenness information, making the main contribution of this paper. This approach is especially useful when the quality of imaging greenness is low. It has been favorably compared against existing strategies, obtaining better results, quantified by 4,5%. (C) 2015 Elsevier B.V. All rights reserved.
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| 9. |
- Guzhva, Oleksiy, et al.
(författare)
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Feasibility study for the implementation of an automatic system for the detection of social interactions in the waiting area of automatic milking stations by using a video surveillance system
- 2016
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Ingår i: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 127, s. 506-509
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Tidskriftsartikel (refereegranskat)abstract
- A well-planned waiting area is crucial for automatic milking systems. In an enclosed waiting area, cows of different rank compete for entering the milking station and they are exposed for a variety of social interactions. Such interactions could increase standing time and delay milking, which may result in stress, lameness, impaired welfare and reduced performance. The aim was to monitor the waiting area in a free stall dairy by the use of three video cameras to detect occurrence of social interactions by using improved image segmentation and tracking methods. The surveillance system observed 252 cows having free access to any of four milking stations during 24 h over a period of two weeks. A two-step pattern recognition approach was used. In the first step geometric features (distances) were extracted from every pair of cows in every frame. These features form the input of the second step. It consists of a classifier of the behaviour of the cows. A support vector machine was used to realise this classifier. The social interactions were identified based on collision of geometrical shapes segmented from the image and positively identified as cows by experienced observers. The results showed that the proposed system was capable of a fairly accurate detection of social interactions.
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| 10. |
- Johansson, Erik, et al.
(författare)
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Fast visual recognition of Scots pine boards using template matching
- 2015
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Ingår i: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 118, s. 85-91
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Tidskriftsartikel (refereegranskat)abstract
- This paper describes how the image processing technique known as template matching performs when used to recognize boards of Scots pine (Pinus sylvestris L.). Recognition of boards enables tracking of individual boards through an industrial process, which is vital for process optimization.A dataset of 886 Scots pine board images were used as a database to match against. The proposed board recognition method was evaluated by rescanning 44 of the boards and matching these to the larger dataset. Three different template matching algorithms have been investigated while reducing the pixel densities of the board images (downsampling the images). Furthermore, the effect of variations in board length has been tested and the computational speed of the recognition with respect to the database size has been measured. Tests were conducted using the open source software package OpenCV due to its highly optimized code which is essential for applications with high production speed.The conducted tests resulted in recognition rates above 99% for board lengths down to 1 m and pixel densities down to 0.06 pixels/mm. This study concluded that template matching is a good choice for recognition of wooden board surfaces.
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