The 'equal-compartment-agar-method' was employed to evaluate four allelopathic activity wheat cultivars on whole plant, root and shoot length and plant dry weight of four weed species. Wheat cultivars were included Niknejad and Shiraz (more competitive cultivars); Tabasi and Roshan (less competitive cultivars). In this study used four weed species (Secale cereale L., Avena ludoviciana L.: monocotyledon, Convolvulus arvensis L. and Vicia villosa L.: dicotyledon). Results showed that the allelopathic activity of wheat was associated with number of wheat seedlings and wheat cultivars. Results demonstrated that the whole plant and root length of weed species were significantly reduced in the presence of wheat cultivars. The degree of weed growth inhibition was depended on the number of wheat seedlings. All of the cultivars and densities caused promotion of dicot shoot length. Results indicated that the length of whole plant (-30.22%) and root (-57.74%) of C. arvensis and shoot length (-13.24%) of S. cereale had the highest sensitivity. None of factors had significant effect on plant dry weight of weed species.

This study shows the ability of Artificial Neural Network (ANN) technology to be used for the prediction of the correlation between common lambsquarters (Chenopodium album L.) population, corn (Zea mays L.) population and planting pattern in different days after planting (as inputs) with common lambsquarters biomass production (as output). The number of patterns used in this study was 60 which were randomly divided into 45 and 15 data sets for training and testing the neural network, respectively. The results showed that a very good performance of the neural network is achieved. Some explanation of the predicted results is given. The multi layer perceptrons with training algorithm of backpropagation (BP) was the best one for creating nonlinear mapping between input and output parameters. The mean training of root mean square error (RMSE) was equal to 0.0156. ANN model predicted the common lambsquarters biomass with maximum RMSE, t-value, average prediction error and correlation coefficient of 0.0091, 0.985, 2.6% and 0.989, respectively. The ANN model, predicted common lambsquarters biomass within +/- 5% of the measured biomass for 59.8% of the samples indicates that the ANN can potentially be used to estimate plant biomass. Adjusting ANN parameters such as learning rate, momentum, number of patterns and number of hidden nodes/layers affected the accuracy of biomass production predictions.