Precision Farming
M. Saadikhani; M. Maharlooei; M. A. Rostami; M. Edalat
Abstract
IntroductionRemote sensing is defined as data acquisition about an object or a phenomenon related to a geographic location without physical. The use of remote sensing data is expanding rapidly. Researchers have always been interested in accurately classifying land coverage phenomena using multispectral ...
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IntroductionRemote sensing is defined as data acquisition about an object or a phenomenon related to a geographic location without physical. The use of remote sensing data is expanding rapidly. Researchers have always been interested in accurately classifying land coverage phenomena using multispectral images. One of the factors that reduces the accuracy of the classification map is the existence of uneven surfaces and high-altitude areas. The presence of high-altitude points makes it difficult for the sensors to obtain accurate reflection information from the surface of the phenomena. Radar imagery used with the digital elevation model (DEM) is effective for identifying and determining altitude phenomena. Image fusion is a technique that uses two sensors with completely different specifications and takes advantage of both of the sensors' capabilities. In this study, the feasibility of employing the fusion technique to improve the overall accuracy of classifying land coverage phenomena using time series NDVI images of Sentinel 2 satellite imagery and PALSAR radar imagery of ALOS satellite was investigated. Additionally, the results of predicted and measured areas of fields under cultivation of wheat, barley, and canola were studied.Materials and MethodsThirteen Sentinel-2 multispectral satellite images with 10-meter spatial resolution from the Bajgah region in Fars province, Iran from Nov 2018 to June 2019 were downloaded at the Level-1C processing level to classify the cultivated lands and other phenomena. Ground truth data were collected through several field visits using handheld GPS to pinpoint different phenomena in the region of study. The seven classes of distinguished land coverage and phenomena include (1) Wheat, (2) Barley, (3) Canola, (4) Tree, (5) Residential regions, (6) Soil, and (7) others. After the preprocessing operations such as radiometric and atmospheric corrections using predefined built-in algorithms recommended by other researchers in ENVI 5.3, and cropping the region of interest (ROI) from the original image, the Normalized Difference Vegetation Index (NDVI) was calculated for each image. The DEM was obtained from the PALSAR sensor radar image with the 12.5-meter spatial resolution of the ALOS satellite. After preprocessing and cropping the ROI, a binary mask of radar images was created using threshold values of altitudes between 1764 and 1799 meters above the sea level in ENVI 5.3. The NDVI time series was then composed of all 13 images and integrated with radar images using the pixel-level integration method. The purpose of this process was to remove the high-altitude points in the study area that would reduce the accuracy of the classification map. The image fusion process was also performed using ENVI 5.3. The support Vector Machine (SVM) classification method was employed to train the classifier for both fused and unfused images as suggested by other researchers.To evaluate the effectiveness of image fusion, Commission and Omission errors, and the Overall accuracy were calculated using a Confusion matrix. To study the accuracy of the estimated area under cultivation of main crops in the region versus the actual measured values of the area, regression equation and percentage of difference were calculated.Results and DiscussionVisual inspection of classified output maps shows the difference between the fused and unfused images in classifying similar classes such as buildings and structures versus regions covered with bare soil and lands under cultivation versus natural vegetation in high altitude points. Statistical metrics verified these visual evaluations. The SVM algorithm in fusion mode resulted in 98.06% accuracy and 0.97 kappa coefficient, 7.5% higher accuracy than the unfused images.As stated earlier, the similarities between the soil class (stones and rocks in the mountains) and manmade buildings and infrastructures increase omission error and misclassification in unfused image classification. The same misclassification occurred for the visually similar croplands and shallow vegetation at high altitude points. These results were consistence with previous literature that reported the same misclassification in analogous classes. The predicted area under cultivation of wheat and barley were overestimated by 3 and 1.5 percent, respectively. However, for canola, the area was underestimated by 3.5 percent.ConclusionThe main focus of this study was employing the image fusion technique and improving the classification accuracy of satellite imagery. Integration of PALSAR sensor data from ALOS radar satellite with multi-spectral imagery of Sentinel 2 satellite enhanced the classification accuracy of output maps by eliminating the high-altitude points and biases due to rocks and natural vegetation at hills and mountains. Statistical metrics such as the overall accuracy, Kappa coefficient, and commission and omission errors confirmed the visual findings of the fused vs. unfused classification maps.
Agricultural waste management
M. Safari; M. A. Rostami
Abstract
IntroductionIn conventional combine harvesters, wheat chaff is typically removed from the end of the machine and deposited on the field surface. Depending on the wheat cultivar, cultivation method, and growing conditions, the amount of chaff produced can range from 0.8 to 1.5 times the amount of grain ...
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IntroductionIn conventional combine harvesters, wheat chaff is typically removed from the end of the machine and deposited on the field surface. Depending on the wheat cultivar, cultivation method, and growing conditions, the amount of chaff produced can range from 0.8 to 1.5 times the amount of grain harvested per hectare (Tavakoli, 2012). On average, this translates to an annual production of approximately 14 million tons of chaff, which is valued at around $240000000 based on regional prices in 2018-2019 ($1000 per kilogram). If collected, these chaff residues could be used as animal feed for livestock. Additionally, the long stems protruding from the back of conventional combine harvesters can interfere with subsequent cultivation efforts. Chaff combine harvesters have a similar structure to conventional machines, but feature a modified end that includes a tank and blower for collecting and depositing crushed chaff. Apart from the threshing unit, all other components of the harvester remain unchanged.Materials and MethodsThis study was conducted in 2019 in dryland wheat fields to determine the performance of Chaff combine harvesters in Kurdistan province. The study used 15 combine harvesters, including John Deere models equipped with chaff threshers from Shiraz, Bookan, and Hamedan, as well as the Hamedan Barzegar specific chaff collector combine. These combines were evaluated and compared based on natural losses, head and chaff storage losses, field capacity, purity percentage, and yield in field conditions in Kurdistan province. The total number of combines evaluated was 15, using a completely randomized design. Among these, 33% belonged to Shiraz company (5 samples), 33% to Bookan (5 samples), 20% to Hamedan (3 samples), and 14% to Hamedan Barzegar (2 samples). Sampling included measurement of natural losses, header losses, threshing tank losses (losses of the threshing unit, separating unit, and cleaning unit), and quality losses (broken grains and impurities) in the combine tank.Results and DiscussionThe results showed that the average yield, natural loss, and combine loss were 1,698.14 kg.ha-1, 2.39%, and 4.92%, respectively. In terms of the loss rates in different parts of the combine, 43.49% was related to the chaff storage of the combine, and 56.50% was related to the combine head.The natural loss rate in this province was 2.39%. The total combine loss was 5.18%, with 40.44% of that related to chaff storage and the rest related to the combine head. The results also showed a significant difference between the treatments in terms of field capacity, chaff storage loss, and purity percentage at a probability level of 5%.The total loss of the Hamedan Barzegar combine was 6.67%, which was higher than the other combines. The chaff storage loss of the Shiraz, Bookan, Hamedan, and Hamedan Barzegar combines were 0.87%, 2.64%, 0.78%, and 4.28%, respectively, showing a significant difference at a 5% level. There was also a significant difference between the treatments in terms of total grain loss.Based on these results, it is recommended to use the Hamedan, Bookan, Shiraz, and Hamedan Barzegar combines, with total losses of 4.33%, 4.33%, 4.52%, and 6.56%, respectively.ConclusionThe average purity of harvested grains was 96.62%, and there was no significant difference between the combine harvesters in this regard.There was a significant difference between the combines in terms of field capacity at a probability level of 5%. The field capacity of the Bookan, Hamedan Barzegar, Hamedan, and Shiraz combine harvesters were 0.83, 0.87, 0.83, and 0.73 hectares per hour, respectively.In Kurdistan province, the average grain combine loss in dryland wheat harvesting with chaff combine harvesters was 4.92%, which is higher than in other provinces.The loss in the chaff tank of the Shiraz, Bookan, Hamedan, and Hamedan Barzegar combine harvesters was 0.87%, 2.64%, 0.78%, and 4.28%, respectively. Regardless of head losses, the loss in the Hamedan combine was less than other combine harvesters.The total losses of the Hamedan Barzegar, Bookan, Shiraz, and Hamedan combine harvesters were 6.56%, 4.32%, 4.52%, and 4.30%, respectively, with the Hamedan Barzegar and Hamedan combine harvesters having the highest and lowest losses, respectively.Based on the results obtained from this study, using the Hamedan combine is recommended in the dryland conditions of Kurdistan due to its low losses, high purity, and field capacity.AcknowledgementThanks to the Agricultural Jihad Organization of Kurdistan Province, specifically the deputy of the Plant Production and Mechanized Technologies Department, for their assistance and cooperation in the implementation of the project.
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.
M. A. Rostami; H. Afzali Gorouh
Abstract
Introduction Preserving of crop residues in the field surface after harvesting crops, making difficult farm operations. The farmers for getting rid of crop residues always choose the easiest way, i.e. burning. Burning is one of the common disposal methods for wheat and corn straw in some region of the ...
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Introduction Preserving of crop residues in the field surface after harvesting crops, making difficult farm operations. The farmers for getting rid of crop residues always choose the easiest way, i.e. burning. Burning is one of the common disposal methods for wheat and corn straw in some region of the world. Present study was aimed to investigate the accurate methods for monitoring of residue management after wheat harvesting. With this vision, the potential of Landsat 8 sensor was evaluated for monitoring of residue burning, using satellite spectral indices and Linear Spectral Unmixing Analysis. For this purpose, correlation of ground data with satellite spectral indices and LSUA data were tested by linear regression. Materials and Methods In this study we considered 12 farms where remained plants were burned, 12 green farm, 12 bare farms and 12 farms with full crop residue cover were considered. Spatial coordinates of experimental fields recorded with a GPS and fields map were drawn using ArcGissoftware, version of 10.1. In this study,t wo methods were used to separate burned fields from other farms including Satellite Spectral Indices and Linear Spectral unmixing analysis. In this study, multispectral landsat 8 image was acquired over 2015 year. Landsat 8 products are delivered to the customer as radiometric, sensor, and geometric corrections. Image pixels are unique to Landsat 8 data, and should not be directly compared to imagery from other sensors. Therefore, DN value must be converted to radiance value in order to change the radiance to the reflectance, which is useful when performing spectral analysis techniques, such as transformations, band ratios and the Normalized Difference Vegetation Index (NDVI), etc. In this study, a number of spectral indices and Linear Spectral Unmixing Analysis data were imported/extracted from Landsat 8 image. All satellite image data were analyzed by ENVI software package. The spectral indices used in this study were NDVI, BAI, NBR and NBRT. Classification accuracy was evaluated and expressed by confusion matrix and Kappa coefficient. Natural surfaces are rarely composed of a single uniform material. Spectral mixing occurs when materials with different spectral properties are represented by a single image pixel. The condition where scale of the mixing is large (macroscopic), mixing would occur in a linear fashion. However for microscopic situations, the mixing is generally nonlinear. The linear model ahich wasadopted in this study, assumes that there is no interaction between materials. Assumption of LSUA is that each pixel on the surface is a physical mixture of several constituents weighted by surface abundance, and the spectrum of the mixture is a linear combination of the endmember reflectance spectra. Within the context of this study, LSUA is a classification method that can determine contribution of each material (or endmember) such as soil or residue for each image pixel. Results and Discussion The spectral response curve extracted from Landsat 8 image used as input into the LSUA model in ENVI software. As expected, crop burned residue (Ash) spectra had lower reflectance when compared to the soil, residue and green plant spectra. The contrast between residue, green plant, soil and residue ash spectra was particularly evident in the NIR and SWIR bands. It is suggested that these bands are essential for residue discrimination. Differences of reflectance in the visible bands were minimal, providing little discrimination between residue, green plant, soil and residue ash. Burned area estimated by LSUA method from Landsat 8 image was correlated against the ground data (measured coincident to the ground data). The overall accuracy of classification with BAI index and LSUA method was 91.7 and 88.3 and Kappa coefficient was 0.89 and 0.84 respectively. Results indicated that burned field area can be located and its area can be estimated using Landsat 8 images. The Index BAI was selected as discernment index for burned/unburned fields in Landsat 8 images. Conclusion Present study was aimed to evaluate the accurate methods for monitoring residue management after wheat harvesting. With this vision, the potential of Landsat 8 sensor local data for monitoring residue burning was evaluated using satellite spectral indices and Linear Spectral Unmixing Analysis. Results indicate that residue ash spectra had lower reflectance when compared to the residue, soil and green plant except NIR band spectra. The contrast between residue, soil, green plant and residue ash spectra was particularly evident in the NIR bands. Results indicated that burned field area can be located and its area can be estimated using Landsat 8 images. The Index BAI was selected as discernment index for burned/unburned fields in Landsat 8 images.
Design and Construction
M. A. Rostami
Abstract
IntroductionNowadays, in a lot of farm land due to reasons such as high density, heavy textured soils, steep terrain and a large body of water at each irrigation, rapid and complete absorption of water in the soil does not happen and runoff will be accrued. Improvement of infiltration reduces runoff ...
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IntroductionNowadays, in a lot of farm land due to reasons such as high density, heavy textured soils, steep terrain and a large body of water at each irrigation, rapid and complete absorption of water in the soil does not happen and runoff will be accrued. Improvement of infiltration reduces runoff and thus increases available water capacity. The main methods used to increase the infiltration area: The use of soil amendments, soil management by tillage and conservation farming. These methods may be used separately or together. Reservoir tillage is the process by which small holes or depressions are punched in the soil to prevent runoff of water from irrigation or rainfall. The objective of this study was to develop and evaluate a new reservoir tillage machine for runoff control in the fields. Materials and MethodsFabricated machine has four main units include three-point hitch, toolbar, frame and tillage unit. Tillage unit was a spider wheel with 6 arms that has 6 Wedge-shaped blades, mounted on them. Each tillage unit mounted on a frame and the frame is attached to the toolbar with a yoke. The toolbar was attached to the tractor by three-point hitch. The movement of tractor caused blades impact soil and spider wheel was rotating. Spider wheel rotation speed was depended on the forward speed of the tractor. Blades were created mini-reservoirs on the soil surface for "In situ" irrigation water or rainwater harvesting. Theoretically distance between basins, created by reservoir tillage machine, fabricated in this study was 57 and 68 cm for Arm's length of 30 and 40 cm respectively.For the construction of machine, first the plan was drawn with SolidWorks software and then the parts of the machine were built based on technical drawings. First tillage unit was constructed and its shaft was based in two bearings. Six of the arms were positioned at 60 degrees from each other around tillage units and connected by welding. For evaluation of machine performance, two factors contain of machine speed (in three levels of 5, 7.5 and 10 km h-1) and Arm's length (in two levels of 30 and 40 cm) were evaluated. The machine was evaluated based on a completely randomized block factorial design with three replications. Effects of these factors on depth, distance and volume of basins and runoff were evaluated. Results and DiscussionMean comparisons of depth, distance and size of reservoirs in different machine forward speed and different Arm's length are summarized in Table 1 and 2. The results showed that the effect of arm length and forward speed on changes in the depth and volume of the reservoirs were significant at the probability level of one percent but changes of the distance between the reservoirs was only affected by Arm's length. The results also showed that increasing the forward speed from 5 to 10 km h-1 and increase the Arm's length from 30 to 40 cm increased depth, distance and volume of reservoirs. Reservoir tillage practices were control runoff in all plots.ConclusionsIn this research project, a reservoir tillage machine was built and assessed. Tillage unit of this machine is similar to the spider wheel. By this machine the small holes generated in the ground periodically. For evaluation of machine performance, effect of two factors, including machine speed and arm's length on depth, distance and volume of the basins were evaluated. The results showed that increasing the ground speed from 5 to 10 km h-1 and increase the arm's length from 30 to 40 cm increased depth, distance and volume of reservoirs. Reservoir tillage practices were controlled runoff in all plots.
M. A. Rostami; A. Javadi; M. Heidari Soltanabadi; A. Mehdinia; M. Shaker
Abstract
Introduction: Different models of tractors have been imported from foreign countries or assembled in Iran for many years. Consistency of foreign manufactured products with native specifications and the improvement of locally manufactured tractors are important problems that must be considered. Moreover, ...
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Introduction: Different models of tractors have been imported from foreign countries or assembled in Iran for many years. Consistency of foreign manufactured products with native specifications and the improvement of locally manufactured tractors are important problems that must be considered. Moreover, tractor dimensions, sitting space and some other important factors such as the ability of Iranian users to operate them must be taken into consideration. In this study, we surveyed the proper proportion of tractors dimension, location of control tools and driver’s work space, with Iranian users’ anthropometric data of drivers from five provinces. Human factors are of paramount importance in developing farm machinery given that these machines will often be operated by persons with minimum skills. Therefore, farm machinery should be made simple to operate and as free from hazards as possible.
Materials and methods: Firstly, the anthropometric data for 250 users 20-60 years old was calculated. The drivers were selected randomly. Then the specifications of 4 tractors including: Ferguson 285, Ferguson 399, Valtra and New Holland were compared with the anthropometric data of user in 5th and 95th percentile value and their adaptation was studied. Anthropometric Data of subjects consisted of: standing height, full hand length, popliteal length, seat pan width, seat pan depth, elbow height, seat back support height, hand pan width, hand grip and full-leg length. Getting on the tractor is the first contact of an operator with a tractor. In assessing the suitability of the provision made for getting on the tractor, an experiment was arranged in which operators tried to get on 3 tractors. The mode of getting on the tractors, the agony on the operator’s face, the muscular reactions and individual opinion on the difficulties or comfort while undertaking the task were observed and recorded. The specifications of tractors compared with Anthropometric Data were measured, whiles the tractors were positioned on a level ground for measurements after the tires had been ganged. These specifications were tractor height, steering wheel height, footrest height, foot set height; tractor seat geometry as seat pan width, seat pan depth and seat pan support height; steering geometry as distance of steering wheel from seat reference point, steering wheel radius, steering wheel thickness, steering wheel inclination to the horizontal and seat reach adjustment; levers and pedals distance from seat reference point as gear lever, parking brake lever, hydraulic control lever, clutch pedal, accelerator pedal, brake pedal, front panel, workspace width and workspace length.
Results and Discussion: The experiment that was conducted with operators attempting to get on the tractors indicated that tractor steps heights were higher than the desirable limit. Therefore, based on the ideas of the researcher and tractors drivers, getting on all tractors is difficult. With an increase in the number of steps or a decrease in their heights, the desirable condition can be created. Seat depth of new Holland and Valtra tractors were great for drivers in the 5th percentile value. A variable thickness pad can solve this problem. Results of studies indicated that Seat depth of Massey Ferguson 285 and 399 was shorter than driver’s leg length of 95th percentile value. To solve the problem an increase of 10 centimeters to pad height of these tractors was suggested. Seat pad of tractors were short for drivers in the 95th percentile value. Distance of steering wheel from seat reference point (SRP) in Massey Ferguson 285 and Valtra was further than drivers hand length in the 5th percentile value. Therefore, the drivers hand is short for driver’s good operation. Surveys indicated that drivers had problems for gear lever access in Massey Ferguson 399. Therefore, for good access on gear lever we must increase seat stroke range by 5-10 centimeters. The record available from Meteorological Organization indicated that air temperature and rain throughout the year in the study area is between 20 to 40degrees Celsius and 100to 300mm change. Valtra and New Holland tractors having a driver cabs with heating and cooling equipment have the ideal space for the performance of their driver. Massey Ferguson 285 tractor does not have any driver cab and Roll Over Protection Structure (ROPS).
Conclusions: This research was conducted in five provinces of Iran to assess ergonomics of some commonly and new used tractors in Iran containing Ferguson 285, Ferguson 399, Valtra and New Holland. As there was no data base with required details, data was collected directly by personal contact with tractor users. A questionnaire was filled out for each person and anthropometric data was calculated in all provinces for 250 users 20-60 year old that were selected at random. Then relevant specifications of 4 tractors were measured and compared with the relevant anthropometric data of users in the 5th and 95th percentile value and their adaptation was studied. The results indicated that tractor steps, Seat depth, distance of steering wheel and distance of some levers and pedals from seat reference point should be amended.
M. A. Rostami; M. H. Raoufat; A. A. Jafari; M. Loghavi; M. Kasraei; S. M. J. Nazemsadat
Abstract
Local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. The current methods for assessing crop residue coverage and tillage intensity such as residue weighing methods, line-transect ...
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Local information about tillage intensity and ground residue coverage is useful for policies in agricultural extension, tillage implement design and upgrading management methods. The current methods for assessing crop residue coverage and tillage intensity such as residue weighing methods, line-transect and photo comparison methods are tedious and time-consuming. The present study was devoted to investigate accurate methods for monitoring residue management and tillage practices. The satellite imagery technique was used as a rapid and spatially explicit method for delineating crop residue coverage and as an estimator of conservation tillage adoption and intensity. The potential of multispectral high-spatial resolution WorldView-2 local data was evaluated using the total of eleven satellite spectral indices and Linear Spectral Unmixing Analysis (LSUA). The total of ninety locations was selected for this study and for each location the residue coverage was measured by the image processing method and recorded as ground control. The output of indices and LSUA method were individually correlated to the control and the relevant R2 was calculated. Results indicated that crop residue cover was related to IPVI, RVI1, RVI2 and GNDVI spectral indices and satisfactory correlations were established (0.74 - 0.81). The crop residue coverage estimated from the LSUA approach was found to be correlated with the ground residue data (0.75). Two effective indices named as Infrared Percentage Vegetation Index (IPVI) and Ratio Vegetation Index (RVI) with maximum R2 were considered for classification of tillage intensity. Results indicated that the classification accuracy with IPVI and RVI indices in different conditions varied from 78-100 percent and therefore in good agreement with ground measurement, observations and field records.
