Modeling
H. Zaki Dizaji; H. Bahrami; N. Monjezi; M. J. Sheikhdavoodi
Abstract
Introduction The sugar industry usually gathers huge amounts of information during normal production operations, which is rarely used to study the relative importance of both management and environment on sugarcane yield performance. Yield prediction is a very significant problem of agricultural organizations. ...
Read More
Introduction The sugar industry usually gathers huge amounts of information during normal production operations, which is rarely used to study the relative importance of both management and environment on sugarcane yield performance. Yield prediction is a very significant problem of agricultural organizations. Each agronomist wants to know how much yield to expect as soon as possible. The aim of this study was to determine the performance of C5.0 and QUEST algorithms to predict the yield of sugarcane production in Amir-Kabir agro-industry Company of Khuzestan province, Iran. However, the working method described in this paper is applicable to other geographical areas and other kinds of crops. Materials and Methods The data for the study were collected from Amir-Kabir agro-industry Company. The data is obtained from 2012 to 2016 years. The study area is located in Khuzestan Province which is a major agricultural region in Iran. The geographical location of the study area is between latitudes 31° 15′ to 31° 40′ north and longitudes 48° 12′ to 48° 30′ east. It covers an area of about 12000 ha. The average elevation of the study area is 8m above sea level. Mean annual rainfall within the study area is 147.1mm, the mean annual temperature is approximately 25°C and the mean soil temperature at 50cm depth is 21.2°C. The used data were obtained from a survey with 15 variables carried out on 1201 sugarcane farms. Variables used in the study of data mining can be divided into two categories: target variable and predictor variables. The variable of yield was used as the target variable (dependent) and other variables as predictor variables (independent). In two models, the input data included crop cultivar, month of harvest, chemical fertilizer (Nitrogen), chemical fertilizer (Phosphate), age (plant or ratoon), times irrigation, ratio of surface spraying, soil texture, soil electrical conductivity (EC), water consumption per hectare, drain, farm management, crop duration, area, and yield-category. The study was included in 1201 farms. The necessary data were collected and pre-processing was performed. We propose to analyze different decision tree methods (C5.0 and QUEST). Results and Discussion First, decision tree methods were analyzed for variables. Then, according to C5.0 method (error rate 0.2319 for the training set and 0.3306 for test set) performed slightly better than another method in predicting yield. Crop cultivar is found that an important variable for the yield prediction. 24 rules were found in this study, C4.5 showed a better degree of separation. The measured prediction rate of C5.0 was correct: 76.81% and wrong: 23.19% in the training data, and correct: 66.94% and wrong: 33.06% in the test data. The prediction rate of QUEST was correct: 68.25% and wrong: 31.75% in the training data, and correct: 70.83% and wrong: 29.17% in the test data. Using the training data comparison between the model types showed that the C5.0 model produces a more accurate prediction model and was, therefore, the model to use. Using the testing data in comparison with the model types showed that the QUEST model produced a more accurate prediction model. The results of our assessment showed that C5.0 and QUEST algorithms were capable to produce rules for sugarcane yield. Therefore, our proposed methods as an expert and intelligent system had an impressive impact on sugarcane yield prediction. Conclusion In today's conditions, agricultural enterprises are capable of generating and collect large amounts of data. Growth of data size requires an automated method to extract necessary data. By applying data mining technique it is possible to extract useful knowledge and trends. Knowledge gained in this manner may be applied to increase work efficiency and improve decision making quality. Data mining techniques are directed towards finding those schemes of work in data which are valuable and interesting for crop management. In this research, decision tree algorithms (C5.0 and QUEST) were used. This classification algorithm was selected because it has the potential to yield good results in prediction and classification applications. This study was performed to present a model-based data mining to predict sugarcane yield in 2012-2016. The 24 classification rules generated from the C5.0 decision tree algorithm have great practical value in agricultural applications. The results showed the QUEST and C5.0 decision tree algorithms produced the best prediction accuracy. Sensitivity analysis results indicated that crop cultivar was the most important variables. It was observed that efficient technique can be developed and analyzed using the appropriate data, which was collected from Khuzestan province to solve complex agricultural problems using data mining techniques (decision tree). The decision tree has been found useful in classification and prediction modeling due to the fact that it can capability to accurately discover hidden relationships between variables, it is capable of removing insignificant attributes within a dataset.
J. Taghinazhad
Abstract
Introduction One of the most important agricultural crops is rape seed oil as its special features can play an important role in the agricultural region. Due to the presence of more than 40% oil and 25% protein in the grain can play an important role in the supply of edible oil. After determining of ...
Read More
Introduction One of the most important agricultural crops is rape seed oil as its special features can play an important role in the agricultural region. Due to the presence of more than 40% oil and 25% protein in the grain can play an important role in the supply of edible oil. After determining of various factors such as uniformity of planting depth, evenness between shrub, plant height and grain yield concluded that Nordsten drill along the seeding density of 75 cm for mechanized planting is acceptable yield. Afzali nia et al. (1999) in one study aimed to assess the performance of common grain drills in Iran in Zarghan area in Fars Province showed that differences between treatments in terms of seed distribution uniformity factor, plant population per unit area and yield product is not significant. The purpose of this study was to evaluate and select the most suitable types of canola planter and variable seed rate planting density and aims to increase the canola cultivated area by the highest yield. Materials and Methods Moghan Plain, located in the north areas of Ardebil province, is considered as an important areas of canola planting in Iran. This study was performed in the agricultural research center of Ardabil Province (Moghan) (39°39´N; 48°88´E; 78 m a.s.l.) in Northwest of Iran. To evaluate different planters with varied seed rates on canola yield. The experimental design was carried out in a randomized complete block design with strip splits (varied seed rates 6, 8 and10 kg per hectare and different drills consist of B1: Barzagar Hamadani drill (conventional method) B2: Amazon drill pals teeth harrow, B3: Gaspardo drill pals teeth harrow and B4: Agromaster drill) and four replications. To investigatethe different treatments in the experiment, various parameters such as percent germination, seeding uniformity of width and depth intervals, plant establishment, effective field capacity, fuel consumption rate andgrain yield were measured. Results and Discussion The evaluation of results of drill types showed that there was significant difference between the planters type and other performance parameters. Different planters with varied seed rates also had significant effects on germination at 1% probability level and B4 had maximum percentage of seed germination (89.45%). Uniformity of seed distribution was found to be the highest for B4 in vertical distribution uniformity (72.62%) and inter-row uniformity (84.25%). The analysis of variance for two years showed that the grain yield and establishment of seed were significantly affected by year. Result of variance analysis for yield indicated that there was a significant difference between planting machines in 1% of probability level. Therefore, maximum yield in this experiment related to B4 with 2672 kg ha-1. The results of technical and economic comp ration indicate that the added net income of B4, was 4940 thousand Rails per hectare compared to the conventional method. Conclusion Results showed that the average of yield of the first year was significantly greater than that of the second year. Results indicated that use of B4 lead to the highest of yield 2672 kg ha-1. But in terms of plant height no significance was found. The results of technical and economic comp ration comparison indicated that the added net income from B4, was 4940 thousand Rails per hectare compared to the conventional method. Therefore, considering many factors, the Agromaster drill tested in this study was found to be the best suited planter and therefore is recommended for canola planting in the region.
Kh. Mohammadi; H. R. Ghasemzadeh; H. Navid; M. Moghaddam; H. Ghaffari
Abstract
In this research the quality of walnut kernels under impact loading were studied. Due to unavailability of specific varieties of walnut in Iran, the tests were carried out on the available genotypes. Three different genotypes from walnut orchards of Azarshar region were selected and were collected in ...
Read More
In this research the quality of walnut kernels under impact loading were studied. Due to unavailability of specific varieties of walnut in Iran, the tests were carried out on the available genotypes. Three different genotypes from walnut orchards of Azarshar region were selected and were collected in 2009. A drop test device was designed and constructed to perform the experiments. The impact tests were performed considering five factors in a factorial experiment using completely randomized design with five replications. The factors were genotype, moisture content, geometrical mean diameter, load direction with three levels and the hammer drop height (five levels). The effect of these factors on kernel quality was examined. Walnut cracking assessments and kernel quality were evaluated by well-defined criteria. Generally, by increasing the moisture content, the percentage of broken kernels decreased while the number of unbroken kernels increased and the quality grade of the kernels improved. The percentage of broken kernels increased as hammer drop height increased. Soaking the walnuts in water for 3 hours, with transverse loading (in Y direction) and hammer drop height of 35cm were formed the best set of walnut cracking parameters for obtaining quality kernels.