Precision Agriculture
Adel Taherihajivand; kimia shirini; sina samadi Gharehveran
Abstract
Introduction In many countries, on average, more than 50% of people's food comes from grains, and nearly 70% of the cultivated area of one billion hectares of the world is dedicated to grains. A variety of weeds grow along with cereals in the fields, which can reduce crop yield due to competition for ...
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Introduction In many countries, on average, more than 50% of people's food comes from grains, and nearly 70% of the cultivated area of one billion hectares of the world is dedicated to grains. A variety of weeds grow along with cereals in the fields, which can reduce crop yield due to competition for light, water and nutrients. To eliminate weeds accurately and with minimal problems, timely detection with high accuracy and speed is required. be done. In the field of agriculture, it is controlling and eliminating weeds in grain fields. Weeds are one of the most important factors affecting the production of agricultural products, which are their most important competitors in conventional agriculture, they spray the entire field to eliminate weeds, while weeds appear scattered and patchy in the field. which shows the necessity of using precise agriculture to solve this type of heterogeneity. In addition to causing economic damage, the conventional method of fighting can cause pollution of the environment and even the human food chain. Research shows that the losses caused by pests, diseases and weeds can reach 40% of the global crop every year and it is predicted that this percentage will increase significantly in the coming years. Besides, according to the research of Goktoan et al., the annual cost of weeds for The Australian economy is estimated to be around $4 billion as a loss in agricultural income.Materials and Methods Among the new methods in this field is the use of machine vision technology and related methods such as deep learning object detection algorithms and convolutional neural networks (CNN). The steps related to the implementation of the project include preparing data for training and evaluating networks, using new object detection algorithms, using different convolutional neural networks with different characteristics to extract image features in algorithms, and using the Feature Pyramid Network (FPN) method in object detection algorithms. Was. The output of the networks was evaluated in terms of the number of detections, the exact location of detection and the time of detection in the field. ViTs is based on the Transformer architecture that was originally developed for NLP tasks. Transformers use self-awareness mechanisms that allow the model to capture complex relationships between elements in a sequence. In the case of ViTs, sequence elements are image patches. In using the transformer architecture for visual data, it is dividing the image into small and non-interfering parts. Each patch typically consists of a grid of pixels. These patches are considered the "words" of the image sequence. Spatial embeddings are added to image patches to provide spatial information to the model. Spatial embeddings are necessary because transformers do not have built-in notions of order or spatial relationships. ViTs use multi-series self-awareness mechanisms to capture relationships between different image patches, and the representation of each patch is updated by attention to other patches. Data separation is very important in data watch transformers for two reasons a) the model needs data to learn and b) we need data to measure the model because the model may not be able to extract the information correctly.Results and Discussion The best network in terms of positioning accuracy was the transform model (ViTs) with an average accuracy of 0.95. In addition to this, the network considered in this research managed to recognize 503 of the 535 target weeds, and this means that our network is able to recognize 95% of these weeds. The presented method has been able to reach the highest accuracy compared to other existing methods and has been able to detect existing grasses in a much shorter period of time. Compared to other methods, the reset50 algorithm has been able to detect more than 88%, although its execution time is about 2.5 times that of the proposed method.In comparing the efficiency of algorithms, execution time is as important as accuracy. By making comparisons and considering 70% of the data as training data and 30% as test data, the presented algorithm has been able to detect the weeds in the field with an accuracy of over 90% in just 13 seconds.Conclusion: Today, deep learning methods are much more efficient than other methods, so we can use the new methods available in deep learning in the field of agriculture.Keywords: Optimization, accuracy, agriculture, weed, deep learningAll right reserved.
Precision Agriculture
Alireza Dahmardeh; Ali Shahriari; Mohammad reza Pahlavan Rad; Asma Shabani; MARYAM GHOEBANI
Abstract
Introduction Crop yield modeling is an important part of ecological modeling because it makes possible plant production prediction and increase understanding of how it works. In other words, plant and crop growth simulation and yield modeling are mathematical expressions of plant growth stages and processes ...
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Introduction Crop yield modeling is an important part of ecological modeling because it makes possible plant production prediction and increase understanding of how it works. In other words, plant and crop growth simulation and yield modeling are mathematical expressions of plant growth stages and processes under the influence of environmental and managerial factors. Wheat is one of the key crops grown worldwide and is a source of nourishment for millions of people around the world. Therefore, studying this strategic crop is very importance. On the other hand, more than 70% of wheat and 84% of barley in Sistan and Baluchestan province were produced in Sistan plain and wheat has the highest area under cultivation among different crops, in this arid region. So, the aim of this study was modeling wheat yield with some soil characteristics and determination of the most important soil factors affecting wheat yield in the Sistan plain.Materials and Methods This research was done in the educational and research farm of University of Zabol. Topsoil (0-30 cm) sampling of 100 soil sample was done randomly. Clay, silt, sand abundances and soil texture class, soil pH, electrical conductivity, apparent electrical conductivity of soil, organic carbon, phosphorus, potassium and nitrogen were measured by conventional methods. Wheat plant samples were taken from a one m2 plot and the grain weight, 1000-grain weight and total weight were measured. Performance modeling was performed by three methods of multi-linear regression (MLR), multi-layer perceptron (MLP) and support vector machines (SVMs) by two kernels types linear(SVM-L) and radial basic function (SVM-RBF). It should be noted, before modeling, 80% of the data were selected for modeling (or training) and 20% for testing (or validation) of the models. These data (training and validation) were the same for all models. Coefficient of determination (R2) and the root mean square error (RMSE) were the criteria for comparing the models. Sensitivity analysis was used to determine the most important soil factors affecting wheat yield.Results and Discussion The results of soil properties analyses showed that the soil of this area is non-saline and alkaline soil, has a medium to coarse soil texture and the soil fertility conditions are poor to moderate. The results of comparing the models showed that the highest R2 and the lowest RMSE in estimating all three wheat yield indices were related to the MLP method (grain weight with R2= 0.61, 1000-grain weight with R2= 0.64 and total yield with R2= 0.76). After MLP, with less difference, the SVMs method with two kernels types of linear (grain weight with R2= 0.54, 1000-grain weight with R2= 0.44 and total yield with R2= 0.65) and radial basic function (grain weight with R2= 0.48, 1000-grain weight with R2= 0.58 and total yield with R2= 0.67) showed the better modeling and finally the MLR (grain weight with R2= 0.20, 1000-grain weight with R2= 0.27 and total yield with R2= 0.40) showed the lowest accuracy in modeling the yield components of wheat. The results of sensitivity analysis of wheat yield components showed that total soil nitrogen, clay, silt and soil organic matter had the highest on wheat yield components (grain weight: nitrogen, clay and organic matter; 1000-grain weight: nitrogen, silt and clay; and total yield: clay, organic matter and nitrogen) and soil pH had the least effect on it, maybe because of its low variation.Conclusion Due to harsh environmental conditions in the arid regions, the study of crops yield is very important for the optimal management of facilities and resources. Investigating the application of several wheat yield modeling methods using some soil characteristics in the arid region of Sistan showed that the perceptron neural network (MLP) performed better in predicting the yield components of wheat than other models. Also, some chemical and physical properties of soil that affect the soil fertility and water storage conditions in the soil (soil nitrogen, organic matter, clay and silt contents), were the most affecting factors on the yield of wheat in this arid region. It is important to note that attention to other soil properties as well as climatic parameters and studies and monitoring wheat yield for several years can can lead to more accurate modeling of this strategic crop and thus optimal farm management.
Precision Agriculture
Seyedeh Arefeh Hosseini; Hassan Masoudi; Seyed Majid Sajjadiyeh; Saman Abdanan Mehdizadeh
Abstract
Introduction Nitrogen is one of the essential elements for plants and is consumed more than other elements in plant nutrition. Nitrogen is an important component of the chlorophyll molecule and is present in the chlorophyll structure as a protein. Without nitrogen, plant growth decreases significantly. ...
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Introduction Nitrogen is one of the essential elements for plants and is consumed more than other elements in plant nutrition. Nitrogen is an important component of the chlorophyll molecule and is present in the chlorophyll structure as a protein. Without nitrogen, plant growth decreases significantly. This research was carried out to estimate the amount of nitrogen and chlorophyll of sugarcane leaves from color indices extracted from digital aerial images taken by a quad-copter at two 5 and 10 m altitudes in the fields of Debal Khozaie sugarcane agro-industry company, Khuzestan, Iran. The images used for this research are from three farms with different growth stages. Materials and Methods The imaging was carried out using a quad-copter, the Phantom 3 professional model, at two heights (5 and 10 meters) from the specified points in the fields. After taking the photos from all marked points by the quad-copter camera, four healthy cane branch - with 45 cm distance from each other - were picked at each point and placed in plastic bags. Then, samples were immediately transferred to the laboratory to measure the leaf chlorophyll value, moisture content and the amount of nitrogen. Using a hand-held chlorophyll meter (SPAD-502 model), the leaf chlorophyll index was measured and recorded at each point. After drying the samples, the nitrogen levels were measured using the manual Kjeldahl method. The designed image processing algorithm, to extract color indices from sugarcane fields' images, had these steps: image transfer, preprocessing, image smoothing, noise, and background removal, extracting and selecting of image attributes. After using the image processing algorithm, the color indices of the fields' images were obtained; then the relationship between color indices and nitrogen and chlorophyll content of sugarcane leaves were determined using multivariate regression. The preparation of the data was done in Excel 2013 software and the development of multiple regression equations in SPSS v.21 software. The student t-test was used to compare the performance of regression models in the prediction of nitrogen and chlorophyll content with real values. Results and Discussion Based on the results of the measurements, the dispersion of nitrogen was not uniform throughout and between the fields. The least nitrogen dispersion was in the first growth period and the greatest one in the second growth period. None of the fields had uniform dispersion in the chlorophyll content. The least dispersion was observed in the second growth period and the highest dispersion in the third growth period. Based on the Pearson correlation statistical analysis - from 48 features extracted by image processing including mean, variance, skewness, and peak value of each image color indices in RGB, HSV, HIS, and Lab color spaces - only 24 features were selected to determine the regressions equations. These indices had a correlation with the amount of nitrogen in sugarcane leaves. In the images of 5 meters height, the obtained regression equation for nitrogen estimation was significant at 1% probability level and had a 74.3% determination factor. The determination factor of the five regression equations presented for the images taken from 10 m height were 71, 74, 77, 79, and 82 percent. Also, all the regression equations were significant at 1% probability level, so these relationships are valid and can be used to estimate the amount of nitrogen in sugarcane from 10 m height. By increasing the number of color indices, the accuracy of the regression model in the estimation of nitrogen levels was increased. Accuracy of the 10 m regression model for estimating the amount of nitrogen in sugarcane was higher than the 5 m regression model. All four regression models presented for estimating the chlorophyll of leaf based on color indices of images taken from 5m height were significant at 1% probability level. The obtained determination coefficients for these models were 26, 45, 55, and 62%. By increasing the number of color indices, the accuracy of the regression model was increased for the estimation of chlorophyll content of the leaf. Also, the presented regression model for the estimation of leaf chlorophyll based on color indices obtained from 10 m height images was significant at 1% probability level. The determination factor for this model was 69%, which is more than the determination factor of the most accurate regression model presented for 5m height images. The regression model presented for estimating the sugarcane nitrogen content from leaf chlorophyll was significant at 1% probability level. The amount of determination factor for this model was 68%, which is very close to the amount reported by the Debal Khozaie sugarcane agro-industry company, Khuzestan, Iran. Conclusion Thecomparison of the results of regression equations with real values showed that nitrogen prediction with regression model for 5 m height images and two regression models for 10 m height images had no significant difference with each other. Also, the results of sugarcane nitrogen estimation using the leaves chlorophyll was not significantly different from the actual nitrogen content of leaves. On the other hand, chlorophyll prediction was performed by two regression models for 5 m height images and the regression model for 10 m height images were not significantly different from the actual amount of leaves chlorophyll. Therefore, the presented regression equations are valid and reliable and using these relationships can help know the state of nitrogen and chlorophyll in sugarcane fields.
Precision Agriculture
ُseyyed Mohammad Mousavai; Hojat Emami; Gholam Hosain Haghnia
Abstract
Extended abstract Introduction Knowledge about the soil quality in agriculatral lands and natural resources is essential for achievement the best management and maximum economic efficiency. The land use change is the important human activity in environmental ecosystems, which effect on some soil processes ...
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Extended abstract Introduction Knowledge about the soil quality in agriculatral lands and natural resources is essential for achievement the best management and maximum economic efficiency. The land use change is the important human activity in environmental ecosystems, which effect on some soil processes such as microbial activity, mineralization of carbon and nitrogen content. In addition, land use has an important role on temporal and spatial variation of soil properties and quality. Agricultural practices may affect positive or negative effect on soil quality. Intensive cultivation of plants decreases soil physical and quality, as a result of this yield of plants, production efficiency and environment quality decrease. In this research, the effect of three land uses on soil physical, fertility and quality properties were studied. Materials and methods The studied area (Hossein abad) is located 30 km far from the northern Nehbandan town (South Khorasan, Iran).To study the effect of land uses change on soil properties were selected three land uses including pomegranate (Punica granatum ), olive (Olea europaea) and wheat (Triticum aestivum ). The 45 soil samples (15 samples from each land use) were taken from surface soil (0-30 cm). Then some soil physical and fertility properties which affect the soil quality were measured and the effect of land use change from wheat cultivation to olive and pomegranate gardens during the recent 20 years were studied. In addition, soil quality in each land use was determined based on cornel university test. To compare soil properties and quality, the randomized complete block design was applied. Results and discussion The results showed that land use change had a significant effect on organic carbon, mean weight diameter of aggregates (MWD), water stable aggregates (WSA), macro nutrients (N, P, and K), and some micro nutrients (Fe and Mn) (P < 0.001). Comparison of means demonstrated that the difference between organic carbon content in olive and pomegranate land uses was not significant, and the content of OC in both land uses was significantly higher than wheat land use. Olive and pomegranate land uses cause to stability of soil structure increase, probably due to reduction the traffic of wheals and also somewhat increasing the organic carbon as a result of littering. Therefore, the MWD in olive land use was significantly higher than two land uses and the lowest value was obtained in what land use. Also, the value of WSA in three land uses was significantly different (P < 0.05) and their content in olive and wheat land uses were the maximum and minimum, respectively. The concentration of total nitrogen in pomegranate land use was more significant than two other land uses (P < 0.05). But the concentration of phosphorous (P), potassium (K), Fe and Mn in wheat land use was the highest content and significantly greater than other two land uses. Despite the concentrations of P, K, and Fe nutrients in pomegranate land use were the lowest value, but, there were no significant difference between the concentration of them in olive and pomegranate land uses. It seems that this variation especially P and Fe is probably due to pH and the Ca and Mg concentration and creation insoluble component of Fe, Mn and P in these land uses. According to the results of cornel university test, soil quality in garden land uses was decreased and the range of soil quality score was varied from 49.5 (olive) to 61.2 (wheat). Among the soil properties affecting the soil quality, fertility and chemical properties such as electrical conductivity (EC), absorption sodium ratio (SAR) and somewhat pH of soil saturated extract decreased the soil quality in olive land use. Also, OM, Fe, Zn, and Mn decreased the soil quality in 3 land uses, of course in olive and pomegranate land uses, micro nutrients (Fe and Mn) had the more effect on decreasing the soil quality compared to wheat land use. In addition, bulk density (Bd), mean weight diameter of aggregates (MWD), aeration porosity (AC), P, K, and Cu contents increased soil quality in all 3 land uses. Conclusion In general, when wheat land use change to olive and pomegranate land uses decreased some soil properties and quality in arid area of Nehbandan, probably due to low quality of irrigation water.
Precision Agriculture
Mojtaba Naderi-Boldaji; Abbas Hemmat; T. Keller
Abstract
Introduction Soil compaction is a serious concern in modern agriculture. Field traffic using machines with high axle loads is likely to compact the soil below the plough layer. Compaction of the subsoil should be avoided since soil productivity is at risk of being reduced, and because the effects are ...
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Introduction Soil compaction is a serious concern in modern agriculture. Field traffic using machines with high axle loads is likely to compact the soil below the plough layer. Compaction of the subsoil should be avoided since soil productivity is at risk of being reduced, and because the effects are very persistent, perhaps even permanent. Precompression stress is widely applied as a border between elastic and plastic soil deformations under stress application. As long as the stress does not exceed the precompression stress, soil deformation is expected to be recoverable after the removal of stress (i.e. passage of the tire). The precompression stress is conventionally determined using the standard procedure of Casagrande from the log stress – void ratio curve resulting from confined uniaxial compression of intact soil samples taken at the field. With advances in the technology of precision agriculture, site specific management of machinery traffic is under focus by researchers. Field mapping of soil precompression stress would allow for site specific modifying the machine parameters (e.g. the tire inflation pressure) to control the applied stress below the precompression stress. For example, where the soil moisture is high, a decrease in tire inflation pressure increases the soil-tire contact area and thus decreases the severity of stress propagation in soil. However, the conventional method of estimating the precompression stress is time-consuming, labor intensive thus not suitable for mapping applications. Horizontal penetrometer is a popular device for on-the-go measuring of soil strength. Horizontal penetrometer resistance is an attractive measurement because it is relatively simple, fast and cheap, can be carried out on-the-go, thus yielding spatial information with a high resolution. A multi-tip horizontal penetrometer would also allow for discrete-depth measuring the soil strength. As penetrometer resistance and precompression stress are both measures of soil strength, it was hypothesized that they are highly correlated. Therefore, the relationship between precompression stress (σpc) and horizontal penetrometer resistance (PR) was investigated in a wide range of soil textures. This would suggest an alternative for on-the-go measurement of σpc for field mapping and site-specific management of soil trafficability. Materials and Methods Field measurements were conducted in different soil textures in Switzerland. The clay content of the soils varied from 189 to 584 g kg-1. Horizontal penetrometer resistance was measured at 0.25 m depth at a traveling speed of 0.25 m s-1. Cylindrical core samples were taken at the local minima and maxima of PR along the transects. Cone index measurements were also performed to a depth of 0.5 m at the points of core samples. The samples were subjected to stepwise compression stresses by an Oedometer and the resulting deformation was recorded. The void ratio was calculated with particle density and bulk density at the end of each stress step. The precompression stress was estimated at the point of maximum curvature of log stress- void ratio with fitting Gompertz function. Correlation and regression analyses were performed in SAS software. Results and Discussion The results showed that the Gompertz function explains well the void ratio versus log of stress for different soil textures. The Gompertz parameters were characterized with respect to soil physical characteristics. Precompression stress decreased with increasing soil moisture, soil clay and organic matter content. A relatively strong correlation was found between σpc and PR (R2= 0.47, RMSE= 15.4 kPa) which was significantly improved (R2= 0.59, RMSE= 13.7 kPa) with the effect of soil water content. The high scatter of the relationship between precompression stress and horizontal penetrometer resistance was discussed to be likely due to the difference between the soil failure mechanisms around a penetrating tip and under uniaxial compression. A comparison of different compaction tests (e.g. semi-confined and plate sinkage) with PR may suggest stronger correlations. A strong correlation (R2= 0.6) was also found between PR and cone index (CI). CI was found to be larger than PR for all the soils. Conclusion It was concluded that the soil compaction characteristic (log stress versus void ratio) is strongly governed by the soil initial void ratio with an increase of precompression stress with decreasing the initial void ratio. In the range of variations tested, the relationship between PR and σpc was not affected by soil texture (clay content). The study suggests that measurement of PR can be a fast alternative for mapping of soil precompression stress by compensating for the effect of soil moisture (by e.g. a dielectric sensor). therefore, a combined horizontal penetrometer needs to be employed.
Soil Genesis and Classification
R. Taghizadeh-Mehrjardi; F. Sarmadian; M. Omid; N. Toomanian; M.J. Rousta; M.H. Rahimian
Volume 37, Issue 2 , March 2015, , Pages 101-115
Abstract
In recent years, there has been a great development in the digital soil mapping which has led to production of maps for countries and the continents. Although many studies have been conducted all over the world, few Iranian soil scientists have shown interests in digital mapping. Therefore, in the present ...
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In recent years, there has been a great development in the digital soil mapping which has led to production of maps for countries and the continents. Although many studies have been conducted all over the world, few Iranian soil scientists have shown interests in digital mapping. Therefore, in the present research, different data mining techniques (i.e. regression logistic, artificial neural network, genetic algorithm, decision tree and discriminant analysis) were applied to spatial prediction of great group soils in the area covering of 72000 ha in Ardakan. In this area, by using the conditioned Latin hypercube sampling method, location of 187 soil profiles was selected, which was then described, sampled, analyzed and allocated in taxonomic classes according to soil taxonomy of America. Auxiliary data used in this study to represent predictive soil forming factors were terrain attributes, Landsat 7 ETM+ data and a geomorphologic surfaces map. Results showed that decision tree model had the highest accuracy while it could increase the accuracy of prediction up to 44% in comparison with discriminant analysis technique. Results also indicated using the taxonomic distances led to improving the overall accuracy of decision tree up to 3%. Results confirmed capability of decision tree, artificial neural networks, genetic algorithm, logistic regression, and discriminant analysis with 70%, 65%, 65%, 55%, and 47% accuracy, respectively. Moreover, results showed that decision tree model could predict soil classes in sub-great group with the overall accuracy of 84.2%.
Soil Chemistry and Pollution
K. Dalvand; A. Eftekhari
Volume 37, Issue 1 , September 2014, , Pages 67-75
Abstract
Cadmium (Cd) is a heavy metal that is uptaken by plants, accumulates in edible parts of plants and negatively impacts human health. This study was conducted to investigate the uptake and accumulation of Cd in different parts of reddish. The experimental design was a factorial with complete block design ...
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Cadmium (Cd) is a heavy metal that is uptaken by plants, accumulates in edible parts of plants and negatively impacts human health. This study was conducted to investigate the uptake and accumulation of Cd in different parts of reddish. The experimental design was a factorial with complete block design using three levels of Cd (0, 30 and 60 mgkg-1) and two dates of harvesting (commercial maturity and one week after commercial maturity, called 1st and 2nd dates of harvesting) with three replications. The experiment was carried out using pots which were inserted at research farm of Shahid Chamran University of Ahwaz. Results indicated Cd accumulation in different parts of reddish as the Cd concentration rates increased. The highest Cd accumulation was in the roots (79.35 mg kg-1) at the 2nd date of harvesting. The maximum Cd accumulated in hypocotyls (36.0 mg kg-1) at the 1st date of harvesting, hypocotyls skin (45.0 mg kg-1) at the 1st date of harvesting, and leaves (95.4 mg kg-1) at the 2nd date of harvesting when 60 mg kg-1 of Cd was applied. The results also showed that Cd treatment maximizes Cd at the 1st date of harvesting and increases over the second time of harvesting in reddish organs. The order of Cd accumulation from the highest to the lowest concentration was leaves, roots, petioles, hypocotyls skin and hypocotyls.
