ABSTRACT Evaluation of the relationship between soil properties and saffron yield estimation may contribute to agricultural planning in finding suitable lands for the growth of this valuable product. This study aimed to investigate the performance of artificial neural network (ANN), multiple linear regression (MLR), and adaptive neuro-fuzzy inference system (ANFIS) in terms of saffron yield estimation in some lands of Golestan province, Iran. To this end, 100 areas under saffron cultivation were selected. For rapid and low-cost saffron yield estimation, six different models were designed based on soil properties as inputs using MLR, ANN, and ANFIS methods. According to the results, ANN showed the highest accuracy (R2 = 0.58–0.89) in estimating saffron yield as compared to MLR (R2 = 0.41–0.47) and ANFIS (R2 = 0.41–0.69) models. A comparison of the results obtained from the six models defined in these three methods indicated that Model 4 (R2Reg = 0.45, R2ANFIS = 0.57, R2ANN = 0.87), with the inputs, organic phosphorus, potassium, and calcium carbonate, was the best model in terms of accuracy and speed in estimating saffron yield phosphorus. The RI indexes for ANN in the model were 50% and 34% relative to MLR and ANFIS, respectively, demonstrating the higher accuracy of ANN in saffron yield estimation. The study results can be used to identify lands suitable for saffron cultivation in the study area using organic phosphorus and organic matter levels in the soil. 相似文献
Genetic Resources and Crop Evolution - Lodging is one of the most important factors that affect wheat final yield. Emmer [Triticum turgidum subsp. dicoccum (Schrank ex Schübl.) Thell.] is a... 相似文献
Two field experiments were carried out in 2017 and 2018 to evaluate the impacts of salicylic acid (1?mM SA) and putrescine (1?mM Put) on leaf osmolytes, seed reserve and oil accumulation and fatty acid composition of rapeseed (Brassica napus L.) under different watering levels (irrigations after 70 and 150?mm evaporation as normal irrigation and severe drought stress, and 70?→?90?→?110?→?130?→?150 and 70?→?100?→?130→150 as gradual and moderately gradual water deficits, respectively). The experiments were laid out as split plot on the bases of randomized complete block design in three replications. Water stress increased the contents of glycine betaine, proline, soluble sugars, and proteins. Application of SA and Put further enhanced the contents of glycine betaine and soluble sugars, while reduced proline content of leaves. Seed filling duration, seeds per plant, plant biomass and seed yield were decreased with increasing irrigation intervals. Exogenous SA and Put enhanced all of these parameters under different irrigation intervals. Oil accumulation in seeds was diminished as water stress severed. The gradual water deficit considerably reduced the impacts of drought stress on yield related traits and oil content per seed, due to stress acclimation of plants. Oil content of seeds was augmented by SA and Put treatments through prolonging seed filling duration, particularly under limited irrigations. Percentages of palmitic acid and stearic acid (saturated fatty acids) were not affected by water limitation. However, unsaturated fatty acids of linoleic and linolenic acids were reduced, and oleic acid was enhanced due to water shortage. Unsaturation index was improved by SA and Put treatments under severe water stress as a result of decreasing oleic acid and increasing linoleic and linolenic acids contents. The SA spray was the best treatment for improving rapeseed seed and oil production under normal and stressful conditions.
The classification of roundwood is inextricably linked to the measurement of a particular single wood defect. The appearance, location, and number of defects are important in the quality evaluation of logs and sawn timber, and the most important defects are knots. The purpose of this study was to investigate the relationship between the appearance of branch scars and features of the related knot inside oriental beech logs, and to model the relationship between well-defined branch-scar and knot parameters. One hundred and fifty knots in 15 stems of oriental beech trees were studied. Image analysis software was used to measure the branch-scar and knot features. The results showed a significant positive correlation between the branch-scar parameter “moustache length” and the knot length. The ratio of branch-seal length to width was found to be a good estimator of the stem diameter at the time of knot occlusion and the amount of clear wood between the knot occlusion and the bark. The relationship obtained for the oriental beech stem radius at time of knot occlusion confirms relationship reported for European beech (Fagus sylvatica L.). 相似文献
Studying the status of agricultural soils is one of the most important concerns in the agricultural sector. The soil organic carbon (SOC) is one of the main parameters and it plays an important role in improving soil properties. Hence, knowing this parameter is important in soil science. This study applied the pattern recognition (PR) method in predicting the SOC. Also, the ability of this method was compared with different methods such as the Radial Basis Function Network (RBF), Multilayer Perceptron Neural Network (MLP), Multiple Linear Regression (MLR) and Support Vector Regression (SVR). To compare the results, four performance criteria, namely, root mean square errors (RMSE), the Nash–Sutcliffe efficiency (NS), Willmott’s Index of agreement (WI), mean absolute error (MAE) and Taylor diagrams were used. Results indicated that the PR model performed significantly better than the MLP, MLR, SVR, and RBF models for the estimation of the SOC. 相似文献
