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“Stability Criterion of a Processing Plant using PID Controller”

“Stability Criterion of a Processing Plant using PID Controller”

In this paper, the stability of processing plant is analyzed in the presence of time delays. A delay margin is computed for the delay dependent case and the computed delay margins are verified using time domain simulations of Matlab/Simulink for various processing plant. A comparison is also made for stability using certain PID without stabilizer and PID with stabilizer by a simulink model. In the future Fuzzy Logic Controller will also be used instead of conventional PID controller. Thus stability criterion has been derived and applied for various processing plant, where nos of control variables are involved.

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Optimal capacity planning with uncertain supply in a whitefish processing plant

Optimal capacity planning with uncertain supply in a whitefish processing plant

Using overtime production in combination with the forecasting model we created generates an increase in profit of 43.4 million NOK. Also, the productivity (kilogram processed filet per cutting hour) in the cutting department increased significantly compared with the current situation at LNS, which we determined using actual data from the processing plant in Båtsfjord. We conclude that reducing the uncertainty in supply, using the forecasting model, is valuable for LNS. We also conclude that using the forecasting model is most profitable in situations where the processing plant is not used close to full capacity, as the forecasting model yields the best improvements in the first cluster, both for the profit as the productivity. This is because when there is more capacity needed than available, it is not necessary anymore to accurately plan the supply. LNS can simply plan the capacity to its maximum level.

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Analysis Of Preventive Maintenance For Processing Plant (Gassification Process Unit)

Analysis Of Preventive Maintenance For Processing Plant (Gassification Process Unit)

Preventive maintenance defined as a philosophy, a skill of management series in how to manage the plant operation to be more efficient in terms of cost and times. The purposes of this research is to find the value of preventive maintenance performance that consists of a several activities, according to standard operation of a particular

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Ecoremediation – A case study in a vegatable processing plant

Ecoremediation – A case study in a vegatable processing plant

The production plant produces wastewater in the vegetable and mushroom canning process and in the vegetable souse process. Batch processes and various products are the main reasons for the changes in the wastewater composition and flow. The previous wastewater treatment system with the equalisation basin, settling and neutralisation had low treatment efficiency. The retrofitting of the wastewater treatment plant (WWTP) started with the assessment phase where the composition and wastewater quantities were obtained (maximum, average and minimum) from the various processes such as the processing of cucumbers, mushrooms, beetroots, red pepper and vegetable souses. Data obtained were the basis for the wastewater treatment design.The main results showed that nearly half of the wastewater consisted of cooling wastewater, which was separated and released into the environment without treatment. Regarding the legislation requirements and investment costs, the following wastewater treatment system were proposed and implemented: physical and chemical pre-treatment and treatment in the wetland treatment plant. After the implementation, all legislation requirements were fulfilled, and the investment and operating cost were decreased (compared to the traditional WWTP). The system has been operating efficiently for four years and it represents an example of the ecoremediation system in industry. KEY WORDS:

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Analyses of Shift Pattern Effect on Productivity at the Kaingaroa Central Processing Plant

Analyses of Shift Pattern Effect on Productivity at the Kaingaroa Central Processing Plant

Operator experience is an important factor to consider as each of the operating booths requires a different level of experience. Generally, the grading booth is the constraining part (‘bottle-neck’) on the plant as the grading for defects can be the slowest part of the operation. The most experience operator is therefore often in the grading booth. However, the debarker is at the head of the production line and hence it is important to ensure that the debarker operator provides a constant resource onto the production line at an acceptable pace that does not constrain the production line. Each crew also has a team leader who is responsible for overseeing the crew operations and training operators. There is a natural tension between allocating individuals to specialist roles within the plant versus a ‘bench strengthening’ approach which ensures a stronger team over the longer term.

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An analysis of cooperative biodiesel production by smallholders in Kwazulu-Natal, South Africa

An analysis of cooperative biodiesel production by smallholders in Kwazulu-Natal, South Africa

The smallholder ISC model was then solved iteratively to find the soybean price at which a co-owned processing plant would become viable. This occurred at a price of R3800 per ton of soybeans, ceteris paribus, and the solution for each (1ha) farmer included maize (0.47ha) and drybeans (0.2ha) as its main food crops. Figure 1 compares minimum levels of government subsidy needed to draw soybeans (as feedstock for biodiesel) into the linear programming solutions computed for the large commercial farm (Sparks et al., 2010) and smallholder ISC models. Under the more realistic baseline conversion ratio, the minimum level of government intervention (subsidy) needed to produce biodiesel from soybean cultivated by smallholders in the ISC model is estimated at R12.14 per litre. This is nearly three times as much as the minimum subsidy estimated for the commercial farm model (R4.37/litre). A similar situation exists under an optimistic conversion ratio, where the levels of subsidy are estimated to be R7.22 and R3.64 per litre for the ISC and large commercial farmer models, respectively.

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An Android based Image Processing Application to Detect Plant Disease

An Android based Image Processing Application to Detect Plant Disease

detection, classification, and quantification which recognize the pattern to detect the plant disease. Anand.H.Kulkarni et al [3] proposed the technique to detect plant disease by applying image processing. The captured image is filtered and segmented using Gabor filter. Texture and color features are extracted and plant disease is classified using Artificial Neural Networks (ANN) and showed about 91% accuracy. Jayme Garcia Arnal Barbedo proposed a computer based algorithm [4] to differentiate signs and symptoms of plant disease from asymptomatic tissues of plant leaf. This method segments the captured color image into various channels of color to detect the infected regions in the plant leaf. This semi- automated novel algorithm shows about 95% accuracy in differentiating the symptoms of plant. Samy Abu Naser et al [5] proposed the method for developing an expert system for plant disease diagnosis. In this proposed system the development of plant disease diagnosis with two methods: step by step description and Graphical representations are employed. Ashish Gupta et al [6] proposed an android phone platform for processing image recognition. Here the recognition of lottery ticket number using Tesseract OCR engine from mobile phones is developed by Ashish Gupta for identifying the number series in lottery ticket and knowing the winner of the lottery ticket online. K.Deepak et al [7] proposed an application using android operating system to detect the various types of leaf. This application classifies the leaf image by obtaining leaf contour and features of the leaf and compared with the dataset of obtained leaf images. Nickos Petrellis proposed a windows phone application [8] for diagnosis of plant disease. Here, the windows phone application is build using visual studio. The application detects the diseases in leaves and also describes the number of infected areas present in the leaf. Dixit Ekta Gajanan [9] developed an android based identification of plant fruit diseases using feature extraction technique. Here the fruit image is captured, segmented and various features such as color, texture, morphology and structure of fruit are extracted disease detection in fruit. Chaitra K M [10] presented a plant leaf disease identification system for android. Here, the system identifies the diseases by using Support Vector Machine (SVM) classifier. This system also describes the precaution action to be made for the identified disease.

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Natural Radioactivity in Miscanthus floridulu Plant from the Uranium Tailing Pile at Guangdong, South China

Natural Radioactivity in Miscanthus floridulu Plant from the Uranium Tailing Pile at Guangdong, South China

The transfer factors (TFs) of Miscanthus floridulu root to aboveground were also calculated and observed to be in the range of 0.03 to 0.73 (0.23 ± 0.20) for 238 U, 0.18 to 1.46 (0.65 ± 0.42) for 232 Th, 0.01 to 1.59 (0.43 ± 0.37) for 226 Ra and 0.09 to 1.94 (0.79 ± 0.48) for 40 K, respectively. A major average percentage of total 83% (57% - 97%) for 238 U, 64% (40% - 85%) for 232 Th and 73% (39% - 98%) for 226 Ra activity in the plant is con- centrated in the roots, and only about 17% (2.6% - 42%) for 238 U, 36% (15% - 49%) for 232 Th and 27% (1.5% - 61%) for 226 Ra was distributed in the aboveground, whereas about 59% of 40 K activity accumulated in the roots and 41% in the aboveground. The results showed that, Miscanthus floridulu is 238 U and 226 Ra-accumulating plant with significant absorption and accumulation characteristics, but their BCFs and TFs are not in accord with tra- ditional hyperaccumulator definition.

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Plant Monitoring using Image Processing, Raspberry PI & IOT

Plant Monitoring using Image Processing, Raspberry PI & IOT

Shown in figure , components of the automatic plant monitoring system on Raspberry Pi Model-B and Arduino Uno are demonstrated. The system can notify using a real- time alarming system to smart phones reporting such as the current and daily highest/lowest temperature, humidity, and weather quality of the farm surroundings. Users can also control the filter fan switches and customize the notification system to the smart phone.

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An Android based Image Processing Application to Detect Plant Disease

An Android based Image Processing Application to Detect Plant Disease

detection, classification, and quantification which recognize the pattern to detect the plant disease. Anand.H.Kulkarni et al [3] proposed the technique to detect plant disease by applying image processing. The captured image is filtered and segmented using Gabor filter. Texture and color features are extracted and plant disease is classified using Artificial Neural Networks (ANN) and showed about 91% accuracy. Jayme Garcia Arnal Barbedo proposed a computer based algorithm [4] to differentiate signs and symptoms of plant disease from asymptomatic tissues of plant leaf. This method segments the captured color image into various channels of color to detect the infected regions in the plant leaf. This semi- automated novel algorithm shows about 95% accuracy in differentiating the symptoms of plant. Samy Abu Naser et al [5] proposed the method for developing an expert system for plant disease diagnosis. In this proposed system the development of plant disease diagnosis with two methods: step by step description and Graphical representations are employed. Ashish Gupta et al [6] proposed an android phone platform for processing image recognition. Here the recognition of lottery ticket number using Tesseract OCR engine from mobile phones is developed by Ashish Gupta for identifying the number series in lottery ticket and knowing the winner of the lottery ticket online. K.Deepak et al [7] proposed an application using android operating system to detect the various types of leaf. This application classifies the leaf image by obtaining leaf contour and features of the leaf and compared with the dataset of obtained leaf images. Nickos Petrellis proposed a windows phone application [8] for diagnosis of plant disease. Here, the windows phone application is build using visual studio. The application detects the diseases in leaves and also describes the number of infected areas present in the leaf. Dixit Ekta Gajanan [9] developed an android based identification of plant fruit diseases using feature extraction technique. Here the fruit image is captured, segmented and various features such as color, texture, morphology and structure of fruit are extracted disease detection in fruit. Chaitra K M [10] presented a plant leaf disease identification system for android. Here, the system identifies the diseases by using Support Vector Machine (SVM) classifier. This system also describes the precaution action to be made for the identified disease.

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Height Measurement of Basil Crops for Smart Irrigation Applications in Greenhouses using Commercial Sensors

Height Measurement of Basil Crops for Smart Irrigation Applications in Greenhouses using Commercial Sensors

Vision-based phenotyping techniques are integrated approaches that greatly extend plant researchers’ capability to determine various plant attributes. These techniques can acquire many plant traits precisely, non-invasively and relatively fast that crop researchers cannot obtain. Image-based techniques can be classified into 2D and 3D image-based approaches. While 2D image-based approaches have many advantages in plant phenotyping, there are some constraints in studying 3D plant structures. As an instance, 2D image-based techniques can be used for estimation of curved leaf area or plant volume, but results might be inaccurate. 3D image-based approaches are good candidates to acquire the 3D structure of plants. They do not have the limitations of 2D-image based methods and can properly indicate the plant responses to environmental conditions. As an example, plants can change in shape, structure, color, and pattern under stresses such as drought and extreme temperatures. However, high-throughput phenotyping from a complete 3D model is relatively slow and expensive due to a large number of plants. Based on the context, it may not be essential to reconstruct the entire plant [10]. To estimate organ properties such as accurate leaf size or branching angle, interpretation of 3D data and plant part models are required, but simple measurement of summary traits such as covered volume or plant height can be carried out without 3D reconstruction [14]. In the past decades, many sensing technologies have been developed to acquire 3D information. These sensors are categorized into two types: passive and active. Active sensors are generally sensors with a source of energy that acquire depth data by illuminating the objects they observe and measuring the radiation that is reflected from the target. Common examples of active sensors for depth measurement are laser triangulation and time-of-flight (TOF). On the other hand, stereo vision or structure from motion (SFM) are examples of passive sensors [[15],[16]] .

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A Review: Crop Plant Disease Detection Using Image Processing

A Review: Crop Plant Disease Detection Using Image Processing

Each crop is detected by many different types of plant pathogens, causing different diseases and some of them are significant and occur most widely around the world. Blast, Bacterial Leaf Blight, Brown spot, Sheath Blight and False smut are main diseases found in rice, whereas the important diseases of wheat are Rust, Loose smut, Karnal bunt and spot blotch. Sugarcane is a crop or we can say it's a cash crop which is affected by a number of disease or pathogens. The important ones include Red rot, Smut, Grassy shoot and Red stripe. These are the four important diseases that are considered to be a more important economically. Plant pathogens are mostly developed due to environmental factor. It affects plant leafs, roots or crop itself. Precision Agriculture is everything that makes the practice of farming more accurate and controlled. For Precision Agriculture, Image processing technique

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Plant Disease Prediction using Image Processing Techniques- A Review

Plant Disease Prediction using Image Processing Techniques- A Review

images using digital image processing for diagnosis of plant diseases. Computer processing Systems are developed for agricultural applications, such as detection of leaf diseases, fruits diseases etc. In all these techniques, digital images are collected using a digital camera and image processing techniques are applied on these images to extract useful information that are necessary for further analysis. Digital Image processing is used for the implementation which will take the image as input and then perform some operation on it and then give us the required or expected output. Application of computer vision and image processing techniques certainly assist farmers in all the areas of agriculture activities.

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Processing laser scanner plant data to extract structural information

Processing laser scanner plant data to extract structural information

Plant architecture information has traditionally used the hierarchical structure of the plant as its basis, with digitised point data from real plants entered in a particular hierarchical pattern. Data collection and classification have thus been inseparable. The advent of laser profile scanners, such as the Polhemus FastSCAN, means that 3D plant data can be captured holistically. The data acquired from laser surface scanners is in unordered point cloud form, with points collected only on the surface of the object under study.

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Plant Health Monitoring System using Image Processing: A Review

Plant Health Monitoring System using Image Processing: A Review

challenging task. Usually the diseases or its symptoms such as coloured spots or streaks are seen on the leaves of a plant. In plants most of the leaf diseases are caused by fungi, bacteria, and viruses. The diseases caused due to these organisms are characterized by different visual symptoms that could be observed in the leaves or stem of a plant. Usually, these symptoms are detected manually. With the help of image processing, Automatic detection of various diseases can be detected with the help of image processing. Image processing plays a crucial role in the detection of plant diseases since it provides best results and reduces the human efforts.

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IDENTIFICATION OF NITROGEN DEFICIENCY IN COTTON PLANT BY USING IMAGE PROCESSING

IDENTIFICATION OF NITROGEN DEFICIENCY IN COTTON PLANT BY USING IMAGE PROCESSING

8. Enrique Rico-Garcia, Fabiola Hernandez, “Two new methods for theEstimation of leaf area using digital photography” International Journal of agriculture and Biology, ISSN Online: 1814-9596, 2009. 9. H. Al-Hiary, S. Bani Ahmad. “Fast and Accurate Detection and classification of plant diseases” IJCA (0975-8887), Volume 17-No.1 March 2011.

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DETERMINATION OF PRIMARY NUTRIENTS DEFICIENCY IN PLANT USING IMAGE PROCESSING

DETERMINATION OF PRIMARY NUTRIENTS DEFICIENCY IN PLANT USING IMAGE PROCESSING

For the analysis the Cotton crop of AJIT brand is selected. The leaf whose deficiency is to be determined was placed flat on the white background. The camera was held horizontally to the plane of the leaf. The distance between the camera and the leaf was neither too closed nor too far; it was adjusted such a way that the photograph was covering only background and leaf as shown in figure 2.2. Nearly 15 such photographs of cotton leafs of same plot are taken for the analysis. These images are saved in JPEG format for further processing.

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A Review on Plant Disease Detection using Image Processing

A Review on Plant Disease Detection using Image Processing

Image pre-processing is used to enhance the quality of the image necessary for further processing and analysis. It includes color space conversion and image enhancement. The RGB images of leaves are converted into L*a*b color space. The color transformation is done to determine the luminosity and chromaticity layers. The color space conversion is used for the enhancement of visual analysis.

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A Review on Plant Disease Detection using Image Processing

A Review on Plant Disease Detection using Image Processing

Image pre-processing is used to enhance the quality of the image necessary for further processing and analysis. It includes color space conversion and image enhancement. The RGB images of leaves are converted into L*a*b color space. The color transformation is done to determine the luminosity and chromaticity layers. The color space conversion is used for the enhancement of visual analysis.

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Image Processing: A Methodology to Detect Plant Diseases

Image Processing: A Methodology to Detect Plant Diseases

Monica Jhuria, Ashwani Kumar, RushikeshBorse,[03], due to the increasing demand in the agricultural industry, the need to effectively grow a plant and increase its yield is very important. In order to do so, it is important to monitor the plant during its growth period, as well as, at the time of harvest. In this paper image processing was used as a tool to monitor the diseases on fruits during farming, right from plantation to harvesting. For this purpose artificial neural network concept is used. Three diseases of grapes and two of apple have been selected. The system uses two image databases, one for training of already stored disease images and the other for implementation of query images. Back propagation concept is used for weight adjustment of training database. The images are classified and mapped to their respective disease categories on basis of three feature vectors, namely, color, texture and morphology. From these feature vectors morphology gives 90% correct result and it is more than other two feature vectors. This paper demonstrates effective algorithms for spread of disease and mango counting. Practical implementation of neural networks has been done using MATLAB.

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