Choosing the Proper Material to Optimize the Front Axle of the Tractor Mf285 Using Finite Element

The tractor axle must have sufficient mechanical strength due to its dynamic and static forces during operation. Before fabrication, mechanical strength can be checked by selecting different materials through engineering software to select the most suitable material. In this paper, three types of material, namely St37, Cm45 and St52 were proposed in order to optimize the front axle of tractor MF285. The mechanical strength of each material was investigated by engineering software. Solid Work software was used for the analysis of 3D modeling of the axle and ANSYS software was used for finite element analysis. In the maximum static loading on the front axle of the tractor, the factor of safety for St37, Cm45 and St52 were 3.61, 7.17, and 5.45 respectively. The results showed that Cm45 and St52 have sufficient mechanical strength to withstand the load applied to the axle due to the cost of fabrication. Finally, according to the condition of the tractor in the field and the possibility of mounting of mechanical digger in front of the tractor, the Cm45 was proposed to optimize the front axle of MF285 tractor produced in Iran Tractor Manufacturing Co

Designing a fruit identification algorithm in orchard conditions to develop robots using video processing and majority voting based on hybrid artificial neural network

The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.European Union (EU) under Erasmus+ project entitled “Fostering Internationalization in Agricultural Engineering in Iran and Russia” [FARmER] with grant number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JPinfo:eu-repo/semantics/publishedVersio

Selection of the Most Appropriate Tillage System Based on TOPSIS Model with Emphasize on Impact of Different Tillage Systems on Yield

Among the various agricultural operations, tillage alone accounts for 60% of the energy consumed in agriculture. Other concerns, such as soil compaction, time management, economic issues, porosity reduction, moisture storage capacity, as well as a 25% increase in water and wind erosion, has further fueled efforts to improve tillage methods. In this regard, conservation tillage is more considered by experts. This study was conducted to evaluate important indices of wheat production in different tillage methods. Two plots located in Moghan Agro Co. were selected and were divided into four equal 2.8 hectares. Experiments were performed in randomized complete block design (RCBD) with four tillage systems including conventional, tillage1, tillage2 and direct tillage in which two common wheat cultivars were planted. The results implied that the effect of all four tillage methods was significant at the probability level of 0.001 and the indices such as fuel consumption, efficiency, the number of traffic on farm, land preparation time and its cost per hectare, crop yield, plant density and tiller number were improved using the no-tillage and low tillage2 methods. The results were also re-evaluated using TOPSIS method and the tillage system with CL of 0.98 was selected as the best method. Therefore, direct cultivation can be an appropriate alternative to conventional tillage in sustainable wheat productio

Nondestructive estimation of three apple fruit properties at various ripening levels with optimal Vis-NIR spectral wavelength regression data

Producción CientíficaNondestructive estimation of fruit properties during their ripening stages ensures the best value for producers and vendors. Among common quality measurement methods, spectroscopy is popular and enables physicochemical properties to be nondestructively estimated. The current study aims to nondestructively predict tissue firmness (kgf/cm), acidity (pH level) and starch content index (%) in apples (Malus M. pumila) samples (Fuji var.) at various ripening stages using visible/near infrared (Vis-NIR) spectral data in 400–1000 nm wavelength range. Results show that non-linear regression done by an artificial neural network-cultural algorithm (ANN-CA) was able to properly estimate the investigated fruit properties. Moreover, the performance of the proposed method was evaluated for Vis-NIR data based on optimal NIR wavelength values selected by a genetic optimization tool.Agencia Estatal de Investigación - Fondo Europeo de Desarrollo Regional (project RTI2018-098958-B-I00

Agroclimatic Zoning for Cultivation of Saffron Using AHP Approach in SARAB

According to agricultural experts, saffron is one of the crops that can be a good solution to the problem of drought in a water crisis situation. A plant which uses low water consumption and high economic incomes is a good alternative to wheat, a water-consuming crop. Saffron does not need water at all in the summer, and in the fall and winter, rain will irrigate this crop and require very little water during the year. This study was conducted to select the optimum location of saffron cultivation and its comparative study in Sarab with regard to the role of important factors in locating. For this purpose, climatic criteria including (mean temperature, maximum temperature, minimum temperature, sunshine hours and precipitation), geology criteria (soil), topography criteria (elevation, slope) and socio-economic criteria (land use) were used. Due to the diversity of information, the AHP approach was used for the spatial analyzes of the criteria required for saffron cultivation, and then the layers were overlaid. To determine the potential of different areas of Sarab for saffron cultivation, after investigating the data normality, geo-statistical models were applied to the data. Then, based on AHP model, effective factors were evaluated. At the end, the final result is presented as a zoning map of suitable locations for saffron cultivation. Results revealed that the eastern and western parts of the city (46.5%) had high potential for saffron cultivation. In the northern and southern parts of Sarab, due to the high slope and consequently high erosion, and also the presence of volcanic structures in these areas, it was caused the presence of more volcanic rocks. Thus they got low potential for cultivating saffro

A computer vision system for the automatic classification of five varieties of tree leaf images

Producción CientíficaA computer vision system for automatic recognition and classification of five varieties of plant leaves under controlled laboratory imaging conditions, comprising: 1–Cydonia oblonga (quince), 2–Eucalyptus camaldulensis dehn (river red gum), 3–Malus pumila (apple), 4–Pistacia atlantica (mt. Atlas mastic tree) and 5–Prunus armeniaca (apricot), is proposed. 516 tree leaves images were taken and 285 features computed from each object including shape features, color features, texture features based on the gray level co-occurrence matrix, texture descriptors based on histogram and moment invariants. Seven discriminant features were selected and input for classification purposes using three classifiers: hybrid artificial neural network–ant bee colony (ANN–ABC), hybrid artificial neural network–biogeography based optimization (ANN–BBO) and Fisher linear discriminant analysis (LDA). Mean correct classification rates (CCR), resulted in 94.04%, 89.23%, and 93.99%, for hybrid ANN–ABC; hybrid ANN–BBO; and LDA classifiers, respectively. Best classifier mean area under curve (AUC), mean sensitivity, and mean specificity, were computed for the five tree varieties under study, resulting in: 1–Cydonia oblonga (quince) 0.991 (ANN–ABC), 95.89% (ANN–ABC), 95.91% (ANN–ABC); 2–Eucalyptus camaldulensis dehn (river red gum) 1.00 (LDA), 100% (LDA), 100% (LDA); 3–Malus pumila (apple) 0.996 (LDA), 96.63% (LDA), 94.99% (LDA); 4–Pistacia atlantica (mt. Atlas mastic tree) 0.979 (LDA), 91.71% (LDA), 82.57% (LDA); and 5–Prunus armeniaca (apricot) 0.994 (LDA), 88.67% (LDA), 94.65% (LDA), respectively.Unión Europea (project 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JP

Determination of the most effective wavelengths for prediction of Fuji Apple starch and total soluble solids properties

Proper physical properties and standard chemical properties are among the criteria that consumers use to select fruits. Recently, researchers attempted to develop non-destructive methods for measuring properties, among which the near-infrared (NIR) spectroscopy is of great use. Fuji apples were collected in three di erent growth stages, and then starch and soluble solids were extracted. Spectral data in the range of 800 to 900 nm were used to predict the amount of starch content and 920 to 980 nm to estimate total soluble solids (TSS). Reflectance spectra were pre-processed and the most e ective wavelengths of each property were selected using hybrid artificial neural network-simulated annealing (ANN-SA). Non-destructive estimation of physicochemical properties was conducted using spectral data of the most e ective wavelengths using a hybrid artificial neural network-biogeography-based optimization algorithm (ANN-BBO). The results indicated that the regression coe cient of the best state of training for predicting starch was 0.97 and of TSS was 0.96, while R2 was 0.92 for both. The most e ective wavelengths were 852.58, 855.54, 849.03, 855.83, 853.47, 844.90 nm for starch and 967.86, 966.67, 964.90, 958.40, 957.22, 963.97 nm for TSS.FEDER ALG-01-0247-FEDER-037303; AGNETICS funded the APCinfo:eu-repo/semantics/publishedVersio

Identifying the Suitable Areas for Establishment of Agricultural Machinery Repair Center Using GIS in Rudsar

With the increase in mechanization, the need for the agricultural machinery repair centers is increasing. However, there are only a limited number of tractor repair centers in Rudsar. The existence of repair centers will increase the life of the equipment; on the other hand, the presence of machinery repairmen will lead to quick determination of the precision location of the failure and timely repair. The purpose of this study was to determine suitable locations for the establishment of machinery repair and helping to meet the needs of farmers in the Rudsar district. Therefore, spatial information was collected from various relevant organizations, including the Mapping Organization of the country, the GIS Department of the Ministry of Interior, the Agriculture Organization. Descriptive information was also provided from the census report, statistics, documents, field studies, etc., and recorded and stored in the relevant tables. To prepare the database, first, maps and digitalized data were entered into the GIS software. The collected descriptive data was then digitally stored and connected to spatial data. After entering the information into the computer and storing in different layers in the GIS database, information was extracted as a map. According to the results of the final map, a part of Rudsar, small part of Kelachay and Chaboksar and part of the Chini Jan, are suitable for the establishment of repair shops

A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties

Producción CientíficaSince different varieties of crops have specific applications, it is therefore important to properly identify each cultivar, in order to avoid fake varieties being sold as genuine, i.e., fraud. Despite that properly trained human experts might accurately identify and classify crop varieties, computer vision systems are needed since conditions such as fatigue, reproducibility, and so on, can influence the expert’s judgment and assessment. Chickpea (Cicer arietinum L.) is an important legume at the world-level and has several varieties. Three chickpea varieties with a rather similar visual appearance were studied here: Adel, Arman, and Azad chickpeas. The purpose of this paper is to present a computer vision system for the automatic classification of those chickpea varieties. First, segmentation was performed using an Hue Saturation Intensity (HSI) color space threshold. Next, color and textural (from the gray level co-occurrence matrix, GLCM) properties (features) were extracted from the chickpea sample images. Then, using the hybrid artificial neural network-cultural algorithm (ANN-CA), the sub-optimal combination of the five most effective properties (mean of the RGB color space components, mean of the HSI color space components, entropy of GLCM matrix at 90°, standard deviation of GLCM matrix at 0°, and mean third component in YCbCr color space) were selected as discriminant features. Finally, an ANN-PSO/ACO/HS majority voting (MV) ensemble methodology merging three different classifier outputs, namely the hybrid artificial neural network-particle swarm optimization (ANN-PSO), hybrid artificial neural network-ant colony optimization (ANN-ACO), and hybrid artificial neural network-harmonic search (ANN-HS), was used. Results showed that the ensemble ANN-PSO/ACO/HS-MV classifier approach reached an average classification accuracy of 99.10 ± 0.75% over the test set, after averaging 1000 random iterations.Unión Europea (project 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JP