S. Rahnama; M. Maharlooei; M. A. Rostami; H. Maghsoudi
Abstract
Introduction Date palm is one of the most valuable horticultural products in Iran, which includes 16% of non-oil exports to the world. Kerman province has the second rank for the cultivation area of date palm in Iran. Having information about the exact cultivated area has gained importance for further ...
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Introduction Date palm is one of the most valuable horticultural products in Iran, which includes 16% of non-oil exports to the world. Kerman province has the second rank for the cultivation area of date palm in Iran. Having information about the exact cultivated area has gained importance for further decision makings. To determine the cultivated area, organizations usually use census which has the disadvantages of high cost, wasting time and labor intensive. The aim of this research was to study the feasibility of using Landsat 8 OLI images to identify and classify the area under date palm cultivation. To accomplish this purpose, four supervised classification methods were evaluated. Materials and Methods The study area was in Bam region located at 200 km southeast of Kerman province. In this research, a total of 14 images of Landsat8 OLI satellite from the study area during fall and winter were downloaded from Landsat official web page. After preliminary inspections for interested classes (Date palm gardens, Lands covered with bare soil and forage crop fields), one of the images that was taken on Jan 14, 2017, was selected for further analysis. After initial corrections and processing, 32 images of alfalfa farms, 32 images of date palm gardens and 32 images of lands covered with bare soil, were selected using GPS data points collected in study area scouting. Shape files of all selected fields were created and utilized for supervised classification training set. The same process was also done for the unsupervised classification method. To evaluate the classification methods confusion matrix and Kappa coefficient were used to determine the true and miss-classified area under date palm cultivation. It is worth mentioning that these factors alone cannot identify the most powerful method for classification and they just give us a general overview to choose acceptable methods among all available methods. To identify the most powerful method among selected methods, confusion matrix and investigating the pixel transfers between classes is the crucial method. Results and Discussion Results of classifications revealed that the overall classification accuracy by using NN, MLC, SVM, MDC, and K-Means were 99.10% (kappa 0.973), 98.77% (kappa 0.975), 98.66% (kappa 0.973), 98.52% (kappa 0.97), and 52.66% (kappa 0.31) respectively. Concerning the confusion matrix in the NN method, the percentage of producer accuracy error in date palm class was 0% and in user, accuracy error was 1.44%. In the review of other methods, the lowest producer accuracy error value in date palm class obtained by NN and SVM methods was 0% and the highest producer accuracy error belonged to MLC method which was 1.35%. Checking the recognition power of other classes showed that in the soil class, the highest producer accuracy error was 2.32% by MDC method and the least one was 0.64% by MLC. For forage class, the highest producer accuracy error was calculated 37.07% by SVM and the least accurate one was 4.92% by MDC. Although the K-Means method with Kappa Coefficient of 0.31 did not have a good classification quality, concerning classes and samples, it successfully could identify date palm according to selective samples with 100% accuracy. Results of calculated date palm area using supervised classification methods versus actual area measurements showed that NN and SVM methods with the coefficient of determination (R2) of 0.9995% and 0.9986% had the highest coefficients. K-Means method with R-square of 0.9228% had a good correlation. In general, all supervised classification methods obtained acceptable results for distinguishing between date palm classes and two other classes. NN and SVM methods could successfully recognize date palm class. K-Means method also could recognize date palm class but the recognition included some errors such as dark clay soil textures which were classified as the date palm. Conclusion In general, overall accuracy and kappa Coefficient alone cannot identify the most powerful method for classifying and these methods just give us a general overview to choose an acceptable percentage of accuracy coefficients among available methods. After the initial selection, to identify the most powerful method of classification the pixel transfer calculations in a confusion matrix would be an acceptable technique.
Design and Construction
M. A. Zamani Dehyaghoubi; K. Jafari Naeimi; M. Shamsi; H. Maghsoudi
Abstract
Introduction It is common to use rod weeders for onion harvesting according to their prevention of root blocking in front of the machine and separation of onion bulbs from soil by shaking. Chesson et al., (1977), used a rod weeder for manufacturing an onion harvester. This machine had a rectangular rotor ...
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Introduction It is common to use rod weeders for onion harvesting according to their prevention of root blocking in front of the machine and separation of onion bulbs from soil by shaking. Chesson et al., (1977), used a rod weeder for manufacturing an onion harvester. This machine had a rectangular rotor axis with 25mm×25mm cross section. The rotor power was provided by a hydro-motor. An investigation into onion losses during the harvesting operation showed that the majority of crop damages have been occurred due to the collision of rods with onion bulbs. Therefore, the objective of this study is to design and evaluate an onion harvester based on rod weeders with the capability of crop harvesting with minimum damage. Materials and Methods The main components of the examined onion harvester are chassis, furrower, and power transmission system and excrescence axes. Rectangular 100mm×100mm and 40mm×80mm profiles with 4mm profile thickness are used to fabricate the chassis. The furrowers were installed on each side of the chassis as the first parts of the harvester that comes into contact with the soil. Power transmission system provided rotation of two axes from both sides of the machine due to the lack of space for working of two chains on the one side. Therefore, a gearbox having one input shaft and two output shafts was selected for the machine. The gearbox output shafts turn the rotors with a reduction ratio of 1 to 3.5. The rotary motion of the excrescence axes cuts and moves the soil located under the onions bulbs upward and finally the onion bulbs are placed on the soil surface. Therefore, excrescence axes can be considered as the main part of the onion harvester. The excrescence shape of the axes were created by star wheels. Star wheels had a hole with a square section in center (30mm×30mm), for installing them on their shaft. Choosing this kind of the connection, dose not let star wheels to move freely. Also to limit the lateral movement of the star wheels on axis, metallic spacers were used between the adjacent pairs of them. To evaluate the machine performance three variable factors were defined: working depth (20 and 26 cm), forward speed (3, 4.5 and 6 km h-1) and rotational speed of the excrescence axes (150, 220 and 290 rpm). The conducted experiments were analyzed in a complete randomized design with three replications. Results and Discussion The analysis of variance showed that the working depth and forward velocity of axis had significant effect (in 5% level) on the success rate of onion harvester. Also the interaction between depth and forward velocity and the interaction between rotational speed of axes and forward speed were significant. The interaction between depth and rotational speed of axes and the interaction between depth, rotational speed of axes and forward speed were not significant. Evaluation of the interaction between depth and forward velocity showed that the most success rate of onion harvesting was in 20 cm depth and forward velocity equal to 3 and 4.5 km h-1. The least success was gained in 26 cm depth with 4.5 and 6 km h-1 forward speed. Evaluation of the interaction between rotational speed of axes and forward speed showed that the most success in the onion harvesting was occurred with a machine having 3 km h-1 forward velocity and 150 rpm rotational speed and also 4.5 km h-1 forward velocity and 220 rpm rotational speed. Conclusion The success rate of the onion harvesting decreased by increasing the working depth of the machine and axes distance to the onion bulbs. Also with excessive forward velocity the success rate of onion harvesting decreased because of difficulties in controlling the tractor guidance in straight line. The best performance of this onion harvesting machine was in 20 cm depth, 4.5 km h-1 forward velocity and 220 rpm axes rotational speed. Adjusting the machine working parameters according to these values, the ratio of the linear speed of the star wheel tips to the forward velocity of the machine (kinematic index) was equal to 0.82.
H. Maghsoudi; S. Minaei; B. Ghobadian; H. Masoudi
Abstract
Electronic canopy characterization to determine structural properties is an important issue in tree crop management. Ultrasonic and optical sensors are the most used sensors for this purpose. The objective of this work was to assess the performance of an ultrasonic sensor under laboratory and field conditions ...
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Electronic canopy characterization to determine structural properties is an important issue in tree crop management. Ultrasonic and optical sensors are the most used sensors for this purpose. The objective of this work was to assess the performance of an ultrasonic sensor under laboratory and field conditions in order to provide reliable estimations of distance measurements to apple tree canopies. To achieve this purpose, a methodology has been designed to analyze sensor performance in relation to foliage distance and to the effects of interference with adjacent sensors when working simultaneously. Results showed that the average error in distance measurement using the ultrasonic sensor in laboratory conditions was 0.64 cm. However, the increase of variability in field conditions reduced the accuracy of this kind of sensors when estimating distances to canopies. The average error in such situations was 3.19 cm. When analyzing interferences of adjacent sensors 30 cm apart, the average error was ±14.65 cm. When adjacent sensors were placed apart by 60 cm, the average error became 6.73 cm. The ultrasonic sensor tested has been proven to be suitable to estimate distances to the canopy in pistachio garden conditions when sensors are 60 cm apart or more and can, therefore, be used in a system to estimate structural canopy parameters in precision horticulture.
