Resistance in maize (Zea mays) to isolates of Puccinia sorghi from Eastern Australia

The causal agent of maize common rust (CR), Puccinia sorghi Schwein., has increased in incidence and severity in Australia in recent years, prompting the assessment of sources of resistance and a preliminary survey of the diversity of P. sorghi populations. The maize commercial hybrids tested carried no resistance to 14 isolates of P. sorghi and had infection types (IT) comparable to that of a susceptible check. The resistance gene Rp1_D that remained effective in the US for 35 years, was ineffective against seven of the 14 isolates. Maize lines carrying known Rp genes were inoculated with the five isolates considered most diverse based on year of collection (2018/2019), location (Queensland/Victoria), and host from which they were isolated (maize/sweet corn). Lines carrying the resistance genes RpG, Rp5, Rp1_E, Rp1_I, Rp1_L, RpGDJ, RpGJF and Rp5GCJ were resistant to all five isolates and to isolates collected in many agro-ecological regions. These lines were recommended as donors of effective resistance for maize breeding programs in Australia. Lines carrying no known resistance or resistance genes Rp8_A, Rp8_B, Rp1_J, Rp1_M, Rp7 and Rpp9 (conferring resistance to P. polysora Underw.) were susceptible to all five isolates. Differential lines carrying the resistance genes Rp1_B, Rp1_C, Rp1_D, Rp1_F, Rp1_K, Rp3_D, or Rp4_A were either resistant or susceptible depending upon the isolate used, showing that the isolates varied in virulence for these genes. Urediniospore production was reduced on adult compared to juvenile plants, presumably due to changes in plant physiology associated with age or the presence of adult plant resistance.

Induced resistance to common rust (Puccinia sorghi), in maize (Zea mays)

The common rust of maize (Zea mays L.), caused by Puccinia sorghi Schw., develops pustules on the leaves of maize plants, reducing the leaf area and production of the photoassimilates necessary for grain filling. The host possesses genes coding for different proteins related to the defense mechanisms that prevent the establishment of the pathogen. However, there are susceptible plants that are unable of preventing pathogen attack. This condition depend on biotic and abiotic factors known as inducers of resistance which are able of activating the physico-chemical or morphological defense processes to counteract the invasion of the pathogen. The Ceres XR21 maize hybrid is susceptible to P. sorghi. In this work, maize hybrid was evaluated under a split-split- plot design established in two spring-autumn cycles in the years 2016 and 2017, in which five commercial products of biological and chemical origin reported as inducers of resistance, plus a fungicide were compared. The results showed that trifloxystrobin + tebuconazole (Consist Max®), sprayed on the foliage with 1.5X the commercially recommended dose, showed significant better response in most evaluated variables, because it controlled better the pathogen P. sorghi and maize plants increased grain yield.

A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks

Plant leaf diseases can affect plant leaves to a certain extent that the plants can collapse and die completely. These diseases may drastically decrease the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. This study infiltrates through the facilitated principles of the convolutional neural network (CNN) in order to model a network for image recognition and classification of these diseases. Neuroph was used to perform the training of a CNN network that recognised and classified images of the maize leaf diseases that were collected by use of a smart phone camera. A novel way of training and methodology was used to expedite a quick and easy implementation of the system in practice. The developed model was able to recognise three different types of maize leaf diseases out of healthy leaves. The northern corn leaf blight (Exserohilum), common rust (Puccinia sorghi) and gray leaf spot (Cercospora) diseases were chosen for this study as they affect most parts of Southern Africa’s maize fields.