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The final yield of agricultural products depends on the effective factors during the growing season of plants, especially the size of soil aggregates and proper size distribution of aggregates. Therefore, it is very important to select appropriate tillage implement and to provide a suitable seedbed in terms of aggregate size with the least energy consumption. It is a new idea to use paraplough and winged-paraplough as tools for seedbed preparation. To measure and determine the factors affecting the mean weight diameter (MWD) of aggregates and percentage of crop residues on the soil surface, a series of field trials were performed in a randomized complete block design (RCBD) with five replications. The trials were conducted at three forward speeds of 2, 5 and 7 km·h−1 and three operating depths of 0–10; 0–20; 0–30 cm using the mouldboard plough and the paraplough with different wing configurations (without wings, with forward wings, with backward wings). The main impacts of implemented type, forward speed and operating depth and their mutual binary effects were significant at P

The aim of this study was to make predictions for soil cone index using artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and a regression model. Field tests were conducted on three soil textures and obtained results were analyzed by application of a factorial experiment based on a Randomized Complete Block Design with five replications. The four independent variables of percentage of soil moisture content, soil bulk density, electrical conductivity and sampling depth were used to predict soil cone index by ANNs, ANFIS and a regression model. The ANNs design was that of back propagation multilayer networks. Predictions of soil cone index with ANFIS were made using the hybrid learning model. Comparison of results acquired from ANNs, ANFIS and regression models showed that the ANFIS model could predict soil cone index values more accurately than ANNs and regression models. Considering the ANFIS model, a novel result on soil compaction modeling, relative error (ε), and regression coefficient (R2) were calculated at 2.54% and 0.979, respectively.

Among agricultural plants, wheat, with valuable foodstuffs such as proteins, vitamins, and minerals, provides about 25% of the world’s food calories. Hence, providing its health conditions and quality is of great importance. One of the most important wheat diseases that causes a lot of damages to this product is Fusarium head blight (FHB). In most areas, the causal agent of disease is Fusarium graminearum. This disease not only decreases product quality and efficiency but also has harmful effects on humans and animals by mycotoxin production. FHB discrimination requires experimental work in special conditions and also experts, but these facilities may not be available at customs and other related grain health testing centers. In this study, discriminating healthy wheat grains and the grains infected with F. graminearum was performed with an image-processing technique, an accurate, rapid, and nondestructive method. First, healthy and infected wheat grains were selected, and then digital images of samples were prepared in randomized mass method using cameras and lightening chamber. Then using the image-processing technique, a total of 21 texture characteristics were obtained for each grain. Discrimination and classification of healthy and infected grains were done with 100% accuracy using extracted texture characteristics and two techniques mentioned above. The results of this research could be helpful in the development of automatic devices for rapid discrimination of healthy grains and grains infected with F. graminearum, one of the most destructive wheat diseases.

Soil friction and soil adhesion increase the implement draft force and energy consumption particularly in the tools that have larger contact area with soil. The main ways of lowering the total draft force of the tillage tools include the use of proper materials in tools structures as well as application of the tools in appropriate soil moisture content condition. This paper investigates the effects of soil moisture content, contact surface material and soil texture on soil friction and soil adhesion coefficients. To measure the coefficients of soil friction and soil adhesion, a measurement system was developed at the University of Mohaghegh Ardabili. Experiments for each soil texture were performed at five levels of soil moisture content and four contact materials of steel, cast iron, rubber, and teflon with three replications. Results have shown that in all soil types, the effects of soil moisture content and contact materials had a significant effect on the coefficient of both soil friction and soil adhesion at the probability level of 1%. The coefficient of friction increased with soil moisture content increment and reached its maximum and then had a drop in the fluid phase. Results have shown that the mean values of soil friction and soil adhesion coefficients were significantly different from the studied soils.

The energy crisis and depleting fossil fuel resources have always been the focus of researchers. Fuel consumption of agricultural tractors is not an exception. Researchers have used different methods to predict fuel consumption. With the development of artificial intelligence in the last decade, all re-searchers' attention has been directed towards it. Deep learning is a subset of machine learning, which was inspired by the data processing patterns in the human brain. The deep learning method has been used in research due to the advantages of high accuracy and generalization. So far, no research has used this method to predict fuel consumption. In this research, field experiments were carried out in sandy clay loam and clay soils to model the temporal fuel consumption and specific fuel consumption of an agricultural tractor using a convolutional neural network (CNN), while having some parameters such as the soil type, soil conditions, tool parameters, and operation pa-rameters. The experiments were conducted within each soil texture in a factorial manner based on the randomized complete block design (RCBD) with three replicates. For each soil texture, various moisture levels (8–17% for dry and 18–40% for moist soils), tractor forward speeds (1.2, 1.6, 1.8, and 2.2 km h−1), working depths (30 and 50 cm), the number of passes (2 and 6), and tire inflation pressure (20 and 25 psi) were selected, and cone index, dynamic load, and moisture content were measured in each experimental section. The designed networks used to predict the instant fuel consumption were of a CNN type. The results indicated that the network developed based on the Sgdm algorithm outperformed the others, and thus it was selected for modeling purposes. The network was evaluated based on R2 and MSE criteria. For the temporal fuel consumption, the best results were obtained while using 8-510-510-1 architecture with R2 = 0.9729 and MSE = 0.0049. The 8-100-95-1 architecture also led to the best prediction of the specific fuel consumption with R2 of 0.9737 and MSE of 0.0054. The high prediction accuracy and low error in this research compared to previous studies indicate the superiority of this method in order to predict fuel consumption. It was also observed from the results that the input parameters, which include soil, tool, and operational parameters, are all effective on fuel consumption. Proper management of some parameters, such as working depth, tire inflation pressure, and forward speed, can help to optimize fuel consumption. [ABSTRACT FROM AUTHOR]

Currently, destructive methods are often used to measure the quality parameters of agricultural products. These methods are often complex, time consuming and costly. Recently, studying to find a solution to the disadvantages of destructive methods has become a major challenge for researchers. Non-destructive methods can be useful for the rapid detection of the quality parameters of agricultural products. In this study, hyperspectral imaging was used to evaluate the non-destructive quality parameters of Red Delicious (Red Delicious) and Golden Delicious (Golden Delicious) apples, including pH, soluble solids content (SSC), titratable acid (TA) and total phenol (TP). In order to predict the quality characteristics of apples, the partial least squares (PLS) method with different pre-processing was used. The developed models were evaluated using the root mean square parameters of RMSECV validation error, correlation coefficient (Rcv) and standard deviation ratio (SDR). The results showed that in Red Delicious, for pH, TA, SSC and TP the best forecasting methods were SNV, SNV, MSC and normalized pre-processing with the regression coefficient values of 0.9919, 0.9939, 0.9909 and 0.9899, respectively. In Golden Delicious (Golden Delicious), for pH, TA, SSC and TP, the first derivative, (smoothing and second derivative), normalize (and SNV and normalize) preprocessors were selected as the best prediction models, with values of 0.9989, 0.9989, 0.9999 and 0.9989, respectively. The results related to an artificial neural network also showed that in hyperspectral imaging, the best state of the feed-forward network structure with the LM training algorithm was R = 0.93, Performance = 0.005 and RMSE = 0.03 in 325 inputs, 5 outputs and 2 hidden layers. The results showed that hyperspectral imaging has different predictive capabilities for the qualitative characteristics studied in this study with high accuracy. [ABSTRACT FROM AUTHOR]