Development and evaluation of an adaptive neuro fuzzy interface models to predict performance of a solar dryer

 This research is carried out to predict energy efficiency of a solar dryer by adaptive neuro-fuzzy inference system (ANFIS) model. In this model, temperatures in the collector inlet, collector outlet and in the dry chamber exit and also absorbed heat energy by collector and necessary energy for evaporation of product moisture were considered as an ANFIS network inputs. To investigate the capability of ANFIS models in prediction of dryer efficiency, empirical model and regression analysis were used and their results were compared by ANFIS models. To evaluate an accuracy ANFIS models, statistical parameters such as mean absolute error, mean squared error, sum squared error, correlation coefficient (R) and probability (P) were calculated. Results indicated that coefficient of determination for ANFIS model was higher than empirical model and regression analysis whereas amounts of SSE and MSE were lower. From the results of this research, it is concluded that ANFIS model represent energy efficiency better than empirical model and regression analysis. Finally, it can be stated that the ANFIS model could be efficient in to determining the energy efficiency in a forced-convection solar dryer

Factor analysis of agricultural mechanization challenges in Iran

A descriptive survey research was undertaken in order to assess challenges facing agricultural mechanization development in Iran.  The research population included agricultural mechanization experts, managers and specialists in private and governmental sections.  Using proportional stratified sampling, a sample of 119 was constituted out of a total population of 809 based on the Cochran formula.  Data were collected using questionnaire on which the statements were collected after literature review of research and interviews with mechanization specialists.  The questionnaire was validated by a panel of experts and its reliability index was established by a Cronbach’s coefficient.  A pilot study was conducted with 30 questionnaires (not included in the sample population) to determine the reliability of the questionnaire.  Computed Cronbach’s alpha score was 75%, which indicated that the questionnaire was highly reliable.  All survey data were analyzed using the Statistical Package for Social Sciences (SPSS 16.0).  The results of factor analysis indicated that 69% of the variances of the challenges could be classified in seven groups, namely: programming, technical, infrastructural, managerial, economical, research and extension, and content area.  From each group the most important challenges facing agricultural mechanization development in Iran include: inefficiency of subside payment methods for buying agricultural machinery, large number of time-worn agricultural machinery, incomplete collection of agricultural equipments for power generator machinery (tractor), slow trend of beneficiaries in accepting new technologies, financial weakness of agricultural beneficiaries, inefficiency of agricultural extension and education methods, and weakness of agricultural machinery producers and operators in protecting their guild benefits.   Keywords: agricultural mechanization, challenge, extension, factor analysis, Ira

Solutions for fast development of precision agriculture in Iran

A descriptive survey method was carried out to assess to effective factors on precision agriculture (PA) adoption and to find out practical solutions for development of PA in Iran.  The research population included 450 people from agricultural specialists, experts, researchers.  A sample of 117 was constituted using proportional stratified sampling based on the Cochran formula.  Data were collected using questionnaire.  The questionnaire was validated by a panel of experts, and the reliability index was established by a Cronbach's coefficient.  Computed Cronbach’s alpha score obtained 81%.  All survey data were analyzed using the Statistical Package for Social Sciences (SPSS 16.0).  The most important solutions for development of PA in Iran were categorized in four fields, namely, economical, managerial, technical and human resource.Keywords: adoption, factor analysis, precision agriculture, solutio

Determination of the most important challenges for agricultural mechanization development in Iran

 Nikrooz Bagheri1, Sayyed Amir Abbas Moazzen2(1. Main Cooperator of the National Project of Agricultural Mechanization Development in Iran; 2. Manager of the National Project of Agricultural Mechanization Development in Iran, Secretary of Agriculture-Jihad Think Tank) Abstract: Development of agricultural mechanization in Iran is an approach to lead to industrial and commercial production.  To develop agricultural mechanization in Iran, it is necessary to find out the mechanization challenges and guidelines to solve the problems.  To determine the most important challenges for agricultural mechanization development in Iran, a practical research was undertaken with survey and documentation methods.  To recognize challenges and gather information, the brainstorming method, interviews and field observations were used.  The sample statistical society was composed from 809 experts in social, economic, planning, management, agricultural engineering and mechanization fields from all provinces of Iran.  The results showed that the most important challenges for mechanization development in Iran were 13 cases, and they were classified into four groups of social, economical, technical, and planning and management.  Furthermore, the study of challenges showed that an important part of the challenges was related to human resources.  Therefore, human resources development is one way of solving a lot of agricultural mechanization challenges.Keywords: agricultural mechanization, development, challenges, Iran  Citation: Nikrooz Bagheri, Sayyed Amir Abbas Moazzen.  Determination of the most important challenges for agricultural mechanization development in Iran.  Agric Eng Int: CIGR Journal, 2010, 12(3): 87-91. &nbsp

Simulation and control of fan speed in a solar dryer for optimization of energy efficiency

In a forced convection solar dryer, the dryer efficiency is continuously changing during the drying process due to changes of solar radiation and temperature. So, it is important to use a control system to optimize energy efficiency based on changing drying factors. For this reason, a controller was designed, simulated and evaluated. In this research fan speed was simulated and controlled based on changing system variables accordingly to maintain the optimized efficiency. Fan speed was simulated by SIMULINK toolbar of MATLAB software. The dryer efficiency was determined by considering the mathematical relations and monitoring the air temperature in 3 positions: inlet and outlet of collector and outlet of drying chamber. All experiments were carried out in three replications. The current and optimized dryer efficiencies were calculated by using the control program. Results showed that the simulated model was capable of modeling fan speed. So, statistical analysis showed that the control system highly improved the dryer efficiency throughout its operation at probability level of 1%

Sensoriamento remoto multiespectral no manejo sítio‑específico da adubação nitrogenada

The objective of this work was to evaluate the use of multispectral remote sensing for site‑specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn‑planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil‑adjusted vegetation index (Savi), optimized soil‑adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R2=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0–2.5 kg), medium (2.5–3 kg), and high (3–3.3 kg).O objetivo deste trabalho foi avaliar o uso de sensoriamento remoto multiespectral no manejo sítio‑específico da adubação nitrogenada. Imagens de satélite do “advanced spaceborne thermal emission e reflection radiometer” (Aster) foram obtidas em uma área de 23 ha cultivados com milho, no Irã. Para a coleta das amostras de campo, foi feita a seleção de 53 pixels, por meio do método de amostragem aleatória sistemática. Avaliou-se o teor de nitrogênio total nos tecidos foliares do milho, nesses pixels. Para estimar o teor de nitrogênio da parte aérea do milho, foram utilizados diferentes índices de vegetação, como “normalized difference vegetation index” (NDVI), “soil‑adjusted vegetation index” (Savi), “optimized soil‑adjusted vegetation index” (Osavi), “modified chlorophyll absorption ratio index 2” (MCARI2) e “modified triangle vegetation index 2” (MTVI2). Utilizou-se a técnica de classificação supervisionada com classificador “spectral angle mapper” (SAM) para a geração do mapa de adubação nitrogenada. O MTVI2 apresentou maior correlação (R2=0,87) e é um bom previsor do conteúdo de nitrogênio no estágio V13, 60 dias após o cultivo. Imagens Aster podem ser utilizadas para prever o status de nitrogênio na parte aérea do milho. Os resultados de classificação indicam três níveis de nitrogênio requerido por pixel: baixo (0–2,5 kg), médio (2,5–3 kg) e alto (3–3,3 kg)

Performance evaluation of a variable rate sprayer by artificial neural network models

To evaluate the performance of a variable rate boom sprayer, an artificial neural network (ANN) was employed. To model output flow of nozzles, 727 nets by four neural net models, namely, linear, MLP, RBF and GRNN were tested. For each nozzle, 45, 22 and 23 experimental data were used for train, verification and test, respectively. The results indicated that RBF model was selected as the best by regression ratio of 0.198 and R2 of 0.98. To investigate the capability of RBF model in prediction of nozzles flow, statistical analysis was used. Based on the results, average value of R2 for statistical and RBF models were 0.98 and 0.99, respectively. So, the average value of CV for RBF and statistical models were 18.96% and 19.05%, respectively. From the results, it is concluded that ANN model could be a good predictor to evaluate the performance of a variable rate application system

Multispectral remote sensing for site-specific nitrogen fertilizer management

The objective of this work was to evaluate the use of multispectral remote sensing for site-specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn-planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (Savi), optimized soil-adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R²=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0-2.5 kg), medium (2.5-3 kg), and high (3-3.3 kg)

Detection of Fire Blight disease in pear trees by hyperspectral data

Rapid and early detection of Fire Blight as the most destructive bacterial disease of apple and pear trees is very important to avoid product loss. The objective of this research was to evaluate the usefulness of visible near-infrared spectrometry for early detection of Fire Blight . Three kinds of samples were selected: healthy leaves (H) from healthy trees and symptomatic (S) and non-symptomatic diseased (MS) leaves from infected trees. For spectral analysis, different preprocessing and processing techniques were carried out. Linear discriminant analysis, quadratic discriminant analysis, Mahalanobis discriminant analysis, soft independent modeling of class analogy (SIMCA) and partial least square-discrimination analysis were applied as classification techniques. Laboratory test by selective culture method was used to detect bacteria. Based on analyses, hyperspectral wavelengths for detection of H, MS and S leaves were obtained. SIMCA proved to be the strongest among all classifiers to discriminate healthy leaves from diseased leaves. The results indicated that structure intensive pigment index and modified simple ratio were sensitive to discriminate H–S, H–MS and S–MS leaves. Randomized difference vegetation index showed potential to classify H–S and S–MS samples. Anthocyanin reflectance index showed potential to discriminate H–MS samples. Finally, modified triangular vegetation index1 and modified chlorophyll absorption ratio index1 were identified and considered as spectral indices to discriminate S–MS samples. Based on these results, this technique is reliable for detecting non-symptomatic diseased leaves and is capable of early detection of Fire Blight before spreading