Image Processing
M. Nadafzadeh; A. Banakar; S. Abdanan Mehdizadeh; M. R. Zare-Bavani; S. Minaei
Abstract
IntroductionNowadays, machine vision systems are extensively used in agriculture. The application of this technology in the field can help preserve agricultural resources while reducing manual labor and production costs. In the field of agricultural automation, accurately detecting crop rows is recognized ...
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IntroductionNowadays, machine vision systems are extensively used in agriculture. The application of this technology in the field can help preserve agricultural resources while reducing manual labor and production costs. In the field of agricultural automation, accurately detecting crop rows is recognized as a crucial and challenging issue for weed identification and the automatic guidance of machines. Therefore, it is necessary to explore practical solutions to optimize this process. Hence, the purpose of this study is the precise identification of basil cultivation rows to enable the automatic navigation of robots in the cultivation field.Materials and MethodsIn the first stage of this research, six images from each growth period of basil plants (third, fourth, and fifth week) were taken and weeds were removed from the area between the crop rows using three different methods of area opening, dimensional removal, and masking. In the next stage, six images of crop rows without weeds were examined by performing image processing operations and implementing several routing algorithms, namely, Hough transform, wavelet transform, Gabor filter, linear regression, and an additional algorithm proposed in this study. The output of each of these algorithms was compared with the ideal path identified by the user. For this purpose, after capturing an image, green areas were extracted from it by performing the segmentation process. By applying each of the routing algorithms to the image, plant cultivation lines were identified and their equations were determined. Finally, the performance of the designed robot was evaluated using the most appropriate routing algorithm.Results and DiscussionExamining the performance of three different methods of weed removal in three periods of plant growth (third, fourth, and fifth week) showed that during this interval, the masking method had the lowest error rate compared to the ideal path and the shortest average operation time of 1.64 seconds, followed by the dimensional removal and the area opening methods. Comparing the routes detected by different routing algorithms with the ideal routes and according to the results of the t-test at 5% probability level, the order of the studied routing methods from the most superior is as follows: the proposed algorithm, Gabor filter, linear regression, Hough transform and wavelet transform algorithm. Overall, the proposed algorithm had the highest rate of adaptation to the ideal path (with an average error of 3.65 pixels) and the shortest operation time (4.79 seconds) and was selected as the most appropriate routing algorithm and the performance of the designed robot was evaluated using it.ConclusionA reliable crop row detection algorithm can reduce production costs and preserve the environment. In this study, the masking method was used for removing weeds from the images. The new proposed routing algorithm has superior performance when compared with common routing algorithms such as the Gabor filter, linear regression, Hough transform, and wavelet transform. Additionally, it was shown that the designed robot using the proposed algorithm (with an average error of 3.65 pixels) has the desired performance.AcknowledgmentThe authors express appreciation for the financial support provided by Tarbiat Modares University.
Agricultural systems engineering (greenhouse, fish farming, mushroom production)
M. Jalali; A. Banakar; B. Farzaneh; M. Montazeri
Abstract
IntroductionIn the poultry industry, reducing energy consumption is essential for reducing costs. Energy requirements in the poultry industry include heating, cooling, lighting, and power line energy. Identifying factors that increase energy usage is crucial, and providing appropriate solutions to reduce ...
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IntroductionIn the poultry industry, reducing energy consumption is essential for reducing costs. Energy requirements in the poultry industry include heating, cooling, lighting, and power line energy. Identifying factors that increase energy usage is crucial, and providing appropriate solutions to reduce costs and energy consumption is inevitable. One of the major and expensive factors in the poultry industry is the use of fossil fuels, which also causes pollution. Energy costs directly impact the cost of production and increase the per capita cost of production in the meat and egg sectors. In Iran, poultry farms are among the most widely used energy consumers, especially for heating breeding halls, making them a significant subset of the agricultural sector.Materials and MethodsThe problem under study is the thermal simulation of a meat poultry farm located in Ardestan city, Isfahan province. Ardestan city is situated in a desert region in the north of Isfahan province, at a latitude of 33 degrees and 23 minutes north, and a longitude of 52 degrees and 22 minutes east. The dimensions of the poultry hall floor are 5 meters by 8 meters, and it has a capacity of 300 poultry pieces. There are two inlet air vents (windows), each with dimensions of 1.90 by 1.6 meters. The roof has an average height of 2.5 meters and is sloping, made from a combination of plastic carton, fiberglass, and sheet metal.To reduce energy consumption in this poultry farm, a solar heating system is designed and studied in this research. The farm is one of the functions of Isfahan province, with dimensions of 8 meters in length and 5 meters in width. The simulation is performed using TRNSYS software.Results and DiscussionThe results demonstrate that a collector surface area of 26 m2 is necessary to reach the technically optimal point, where the sun's maximum production is achieved with no energy dissipation. Furthermore, the findings indicate that a balance of 16 m2 is required to align the solar system with the auxiliary system.ConclusionBy installing 2 square meters of solar collectors, 5.2% of the total energy demand can be met with solar energy. To fully meet the energy demand using solar energy, a collector area of 30 square meters is required. As the solar fraction increases, the system's ability to extract solar energy also increases. The maximum production of solar energy without any wastage is achievable with a collector area of 26 square meters. Moreover, to maintain a balance between the use of solar energy and the auxiliary system, a collector area of 16 square meters is needed.
M. Yavari; A. Banakar; M. Sharafi
Abstract
IntroductionImmature birds, like humans and many animals, pass through the puberty period to sexual maturity that is accompanied by sound changes and after the sexual maturity, the sound structure evolves. The puberty period is one of the most important periods in the breeder chicken farms. Because the ...
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IntroductionImmature birds, like humans and many animals, pass through the puberty period to sexual maturity that is accompanied by sound changes and after the sexual maturity, the sound structure evolves. The puberty period is one of the most important periods in the breeder chicken farms. Because the feeding of roosters at this age can delay or accelerate the time of sexual maturity. On the other hand, the diagnosis of mature roosters to mating with chickens increases egg production in early adulthood. Sexual maturity is a summary of the morphological and physiological changes its peak in the roosters from the age of 16 to 24 weeks. In female birds, the beginning of the first laying is considered to be sexual maturity, while the exact timing of sexual maturity in a male bird cannot be determined. The puberty term means the age at which reproduction is possible for the first time, but reproductive processes have not yet evolved. Therefore, the chance of pregnancy at this age is very low and fertility will not be optimal. Puberty can be likened to teenage years in humans. Bird sounds are generated mainly by the syrinx and humans speak with the stimulation of the vocal cords. The sound produced by the bird is similar to how human speech is produced. Therefore, techniques used to recognize human speech are also likely to be useful for classifying bird sounds.Material and MethodsVariation in an animal’s vocalizations can provide clues about how the animal uses sound, as well as qualities of the individual that is vocalizing. Bioacoustics research depends heavily on the ability to characterize these variations. The main goal of this study is to diagnosis puberty and the sexual maturity in bred roosters based on sound signals. To do this, the number of roosters with the first ejaculation for puberty and sperm concentration criterion for sexual maturity was divided into three groups of immature males, roosters during the puberty period and adult roosters and the rooster's acoustic signals were recorded by a microphone in a double-sided glass box (50x50x60 cm). The main purpose of using the box is to prevent the effects of noise in the environment on acoustic signals because otherwise, the sound signal of the rooster is unreliable due to the characteristics of the normal sound. Linear predictive coding (LPC) coefficients from the frequency domain were extracted as sound features. The sound features were used to classify k- nearest neighbors (K-NN) inputs for network training.Results and DiscussionThe results of accuracy, recall and precision values are, respectively, 97.7%, 98.3%, and 98.8% for the classification of roosters. Immature roosters had similar sound structures that with start the puberty and Leakage testosterone hormone, the rooster's syrinx, which is part of the secondary sexual feature, also begins to change. After sexual maturity, the syrinx has grown and this evolution also makes the sound structure of the mature rooster very similar. Therefore, according to the similarity of the sound of the mature rooster and immature one, as well as the syrinx continuous changes during the puberty period, the K-NN classifier with the LPC coefficients can show a high degree of accuracy in the classification of roosters. Because a feature of the k-NN algorithm is that it is sensitive to the data local structure.ConclusionThe main objective of the present study is to detect sexual and puberty of roosters using acoustic signals. The LPC coefficients as K-NN classification inputs show accuracy, recall, and precision values of 98.7%, 98.3%, and 98.8%, respectively. These results indicate high accuracy of K-NN classification to identify and categorize immature roosters, rooster during puberty period, and mature roosters.
