Dean Diepeveen

Optimising Nitrogen fertiliser management and using cultivars with high Nitrogen uptake and utilisation efficiency is a good strategy to improve the Nitrogen use efficiency (NUE). This study investigates the determinants of genetic variation in NUE and develops associations with NAM - A1 and NAM - B1 alleles in Australian wheat cultivars grown under different N treatments in Westonia Australia. The functional NAM - B1 allele improves nitrogen remobilization from the leaf tissue, which led to increasing grain protein content but reduces the grain yield. NAM - A1a allele has the same functional of NAM - B1, but with more influence on the period of grain filling. The Nitrogen fertiliser treatments were applied at rates (0, 50, and 100 kg N ha −1) at three stages (Mid Tillering, Booting, and Flowering) of plant growth. The following parameters were analysed: grain yield, number of head plant - 1, number of seed head - 1, thousand grain weight, Root dry weight, total NAM gene expression, grain protein content, residual N in straw, and Nitrogen content in leaf tissue to determine the Nitrogen accumulation and translocation from the leaves to the grain, and overall NUE and its component. The increase of Nitrogen application also resulted in increased grain yield, number of head plant - 1, and greater root dry weight, which was highest at 100 kg N ha −1. The timing of Nitrogen application had no clear effect on either grain yield or grain protein content. Application of Nitrogen at the late stage (booting and flowering) increased the only number of seed head - 1 and thousand grain weight. The duration of grain filling was critical on Australian wheat cultivars and had a strong impact on grain yield and grain protein content. Our results indicated that NAM - A1 alleles are significantly related to the length of the grain filling period. NAM - A1 allele a is associated with early and early to mid grain filling duration, while NAM - A1 alleles c and d associated with mid and mid to late grain filling duration.

This paper hypothesizes that there is value in combining soil, climate and plant tissue data to give more reliable advice on nitrogen top-ups in-season when compared with models that are currently available. The benefit of soil and climate data is to factor in N mineralisation and potential yield while plant test data is a more direct approach of yield estimates when considering firstly plant N uptake from the whole soil profile and secondly biomass (important yield component). Plant test data are closer to yield in time and space than soil test data, shortening the time period for any yield prognosis by about 2-3 months, depending when plant testing occurred. A positive side-effect of plant testing is to check whether any other nutrients, apart from nitrogen, are limiting yield or an N response. Secondly, this paper explores an AI method as a comparison to the traditional modelling technique to further improve the accuracy and to turn the model into a self-calibrating model. Unlike a statistical autoregression technique, the tested AI method has dynamic functions that can be used not only on time series data but also on data such as used here.

This article describes the use of Cellular Neural Networks (a class of Ordinary Differential Equation (ODE)) , Fourier Descriptors (FD) and NaiveBayes Classifier (NBC) for automatic identification of images of plant leaves. The novelty of this article is seen in the use of CNN for image segmentation and a combination FDs with NBC. The main advantage of the segmentation method is the computation speed compared with other edge operators such as canny, sobel, Laplacian of Gaussian (LoG). The results herein show the potential of the methods in this paper for examining different agricultural images and distinguishing between different crops and weeds in the agricultural system.