Cattle fattening proccess is very important to increase the weight of cattle before being sold. However, there are many traditional breeders looking for cows that have reached puberty, but the cow's ...body is still thin. This could be due to the absence of records in monitoring the weight growth of cows so that feeding becomes less precise. Based on these problems, a system was designed that helps farmers in managing their livestock management by recording the progress of each beef cattle that are kept, regulating cattle feeding so that it is more optimal, and knowing the cost benefits of cattle that are kept based on the growth of cow body weight. This system uses two load cells to weigh cows. The weight of the weighed cow can be stored in the web application database. The main function of this system is to be able to calculate and display the cost of benefits of cattle based on the growth of the cow's body weight. Based on the tests carried out, the system can weight cows using two 500kg load cells with an error value of 3.61%. The system can check cow data using keypad input with an average time of 3.99 seconds and store cattle data in the database with an average time of 4.99 seconds. The system can calculate the cost of cow feed needs in one month and income due to changes in cow weight with a 100% success rate.
The aim of the article is to compare and analyze the impact of technologies and data transfer techniques in term of displaying the image using a holographic pyramid. When assessing the usability of ...the solution, the following parameters will be taken into account: time of image transfer, use of physical parameters of the machine and parameters of the Java Virtual Machine.
The aim of the article is to compare two methods for identifying mushroom species. In article, two methods based on one of the most popular solutions in the field of image recognition, Tenosorflow ...and OpenCV, have been described. A research application was created to carry out the research, in which both algorithms were implemented and tested. In addition, the application was equipped with mechanisms facilitating the collection of application data and algorithms. The results of the research have show that the method based on Tensorflow by 9% moreeffectively recognizes mushroom species.
This paper presents a method for automatic plants’ height measurements in indoor hydroponic plantations by using the OpenCV library and the Python programming language. Using the elaborated algorithm ...and Raspberry Pi driven system with an external camera, the growth process of multiple pak choi cabbages (Brassica rapa L. subsp. Chinensis) was observed. The main aim and novelty of the presented algorithm was to observe the plants’ height in hydroponic stations, where the reflective foil is used, to extract the plants’ growth rate as a parameter. Basing on the pictures of the hydroponic plantation, the bases of the plants, reflections, and plants themselves were separated. Finally, the algorithm was used for plants’ height estimation. The achieved results were then compared to the results obtained manually. The algorithm will be used in future research for plants’ growth optimization.
This paper presents a method for automatically measuring plants’ heights in indoor hydroponic plantations using the OpenCV library and the Python programming language. Using the elaborated algorithm ...and Raspberry Pi-driven system with an external camera, the growth process of multiple pak choi cabbages (Brassica rapa L. subsp. Chinensis) was observed. The main aim and novelty of the presented research is the elaborated algorithm, which allows for observing the plants’ height in hydroponic stations, where reflective foil is used. Based on the pictures of the hydroponic plantation, the bases of the plants, their reflections, and plants themselves were separated. Finally, the algorithm was used for estimating the plants’ heights. The achieved results were then compared to the results obtained manually. With the help of a ML (Machine Learning) approach, the algorithm will be used in future research to optimize the plants’ growth in indoor hydroponic plantations.
The paper presents advancements in healthcare data capture through the application of image-based extraction techniques, which include sophisticated image processing techniques such as resizing and ...adaptive thresholding, for prescription information. With the increasing digitization of medical records, automating the extraction of relevant data from prescription documents has become crucial. This research explores the utilization of image processing and optical character recognition (OCR) methodologies to extract prescription information accurately. By converting prescription documents into image format and employing OCR algorithms, the text content is extracted and parsed for critical details such as medication names, dosages, and patient instructions. Notably, our methodology excels in overcoming limitations associated with handwritten documents, achieving an impressive accuracy rate of 98%. This image-based approach offers a streamlined and efficient method for capturing prescription data, reducing manual data entry efforts, and minimizing potential errors. Experimental evaluations demonstrate the effectiveness and accuracy of the proposed approach, highlighting its potential to enhance healthcare data capture and improve patient care.
Crop diseases are becoming one of the major threats to food security at an alarming rate, and timely detection is challenging due to the shortage of infrastructure in many areas of the world. Plant ...diseases affect crop yield on a large scale, as different pathogens from bacteria to viruses, prove to be major, and possibly irreparable, causes of food loss. The situation has worsened by the fact that diseases are now more easily transmitted globally than ever before. Therefore, an accurate system is required which would provide this required assistance to the professionals that could ensure the error-free diagnosis of the plant disease and an accessible tool that would help farmers and even simple gardeners to have access to agronomic advice. The proposed system uses Convolutional Neural Networks (CNN) on simple digital images of diseased as well as healthy plants, to perform plant disease diagnoses. The authors intend to develop a highly accurate web application implemented using a deep learning model, to provide the user with a platform to identify and mitigate this issue. The proposed model is deployed with 7 convolutional layers, 2 densely connected layers, and 4 pooling layers. All the experiments are conducted on the PlantVillage dataset available on Kaggle and achieved 94% recognition accuracy in comparison to the other state-of-the-art approaches. The intended model is also evaluated on pre-trained models and surpasses in terms of accuracy and storage requirements.
Closed circuit television systems (CCTV) are becoming more and more popular and are being deployed in many offices, housing estates and in most public spaces. Monitoring systems have been implemented ...in many European and American cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by human factors. In this paper, we focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm or knife is visible in the image. We have focused on limiting the number of false alarms in order to allow for a real-life application of the system. The specificity and sensitivity of the knife detection are significantly better than others published recently. We have also managed to propose a version of a firearm detection algorithm that offers a near-zero rate of false alarms. We have shown that it is possible to create a system that is capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.
PDF
