alireza rezaee; seyed morteza khalili; milad asadpour
Abstract
Today, with the expansion of industrial agriculture, the use of new sciences and technologies such as artificial intelligence has a significant role in increasing productivity and smartening of agricultural methods. These methods include estimating seedling density using image processing methods. In ...
Read More
Today, with the expansion of industrial agriculture, the use of new sciences and technologies such as artificial intelligence has a significant role in increasing productivity and smartening of agricultural methods. These methods include estimating seedling density using image processing methods. In addition to helping agricultural land management, this is also effective in assessing the amount of fertilizer and chemicals and determining the storage space required. In this paper, a new method for estimating rapeseed crop density at the two-leaf stage is proposed. To prepare the images, first, images were taken from several random areas of the rapeseed field in the two-leaf stage located in the village of Mohammadabad, Qazvin, using square frames one meter long to meet the usual standards; Then the Gaussian mixed model with the Expectation Maximization algorithm is used to segment the images. In order to improve the segmentation of the images, the k-mean clustering algorithm was used and finally, when the leaves were separated from the other components of the image, morphological operators were used to count the number of products in the images. The number of rapeseed products in the images is also averaged manually and used as reference values to evaluate the performance of the proposed algorithm. The results of the proposed method have a correlation of R=0.96 with the manual counting method and have an accuracy of 96.5%. The results of the proposed method are also compared with two common methods called the Normalized Difference Index (NDI) and the Otsu threshold methods which are based on the color characteristics of the images and used in recent studies, and it is observed that the proposed method works better. Although the images were taken in different environmental conditions and with different light intensities, the error rate of the proposed method for the images used was only less than four percent, which shows the efficiency of the proposed method in estimating canola density. Therefore, the proposed method can be used in estimating canola seedlings in practice in agricultural fields.
Agricultural operations automation
farhad chabok; alireza rezaee; milad asadpour
Abstract
In recent year, using smart systems in agriculture in order to save costs, increase the production per unit area, minimize the hard working conditions as well as dangerous and long works, and also the precise control and supervision is unavoidable in the modern agriculture. The positioning of a mobile ...
Read More
In recent year, using smart systems in agriculture in order to save costs, increase the production per unit area, minimize the hard working conditions as well as dangerous and long works, and also the precise control and supervision is unavoidable in the modern agriculture. The positioning of a mobile agriculture robot with any kind of structure and working role is one of the most fundamental and essential issues in the area of agricultural machines, and it is also a prerequisite of movement for any kind of mobile system in the farm. Therefore, this positioning always faces challenges and also is gotten a lot of attention from scholars working in this field of study. Sensor data fusion from several information sources and using various data fusion methods gives us a general precise image of the agriculture robot’s position. The Dempster-Shafer theory is one of these mentioned methods which benefits from a better performance compared with other data fusion methods, regarding the variable and unspecified workspace of agricultural robots. In this study, the methods of Dempster-Shafer and Kalman filter were used as two major tools of positioning sensors fusion related to an agriculture controllable tractor, in order to achieve the best estimation of the positioning, regarding the environmental conditions. So as to use Dempster-Shafer method in the fusion of numerical data of global positioning system (GPS), inertial measurement unit (IMU) and wheel (shaft) encoder sensors, the data reliability of each sensor is firstly determined by the standard deviation of data for each last n generated data. Then, the weighting is accomplished by the Shannon entropy method. In the simulation section, the dominant geometric equations of the studied tractor are extracted, and a proportional integral derivative (PID) controller is used in order to kinematic control of the robot. Afterward, the simulation process is run in Sim-mechanics MATLAB software. Finally, the performance of two investigated methods in this work is assessed and then compared by addition of different noises into the data of each sensor.
