The Effect of Temperature and Host Plant Resistance on Population Growth of the Soybean Aphid Biotype 1 (Hemiptera: Aphididae)

The Application of Phytohormones as Biostimulants in Corn Smut Infected Hungarian Sweet and Fodder Corn Hybrids

The main goal of this research was to investigate the effects of corn smut (Ustilago maydis DC. Corda) infection on the morphological (plant height, and stem diameter), and biochemical parameters of Zea mays L. plants. The biochemical parameters included changes in the relative chlorophyll, malondialdehyde (MDA), and photosynthesis pigments' contents, as well as the activities of antioxidant enzymes-ascorbate peroxidase (APX), guaiacol peroxidase (POD), and superoxide dismutase (SOD). The second aim of this study was to evaluate the impact of phytohormones (auxin, cytokinin, gibberellin, and ethylene) on corn smut-infected plants. The parameters were measured 7 and 11 days after corn smut infection (DACSI). Two hybrids were grown in a greenhouse, one fodder (Armagnac) and one a sweet corn (Desszert 73). The relative and the absolute amount of photosynthetic pigments were significantly lower in the infected plants in both hybrids 11 DACSI. Activities of the antioxidant enzymes and MDA content were higher in both infected hybrids. Auxin, cytokinin, and gibberellin application diminished the negative effects of the corn smut infection (CSI) in the sweet corn hybrid. Phytohormones i.e., auxin, gibberellin, and cytokinin can be a new method in protection against corn smut.

Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging

Cotton is a significant economic crop. It is vulnerable to aphids (Aphis gossypii Glovers) during the growth period. Rapid and early detection has become an important means to deal with aphids in cotton. In this study, the visible/near-infrared (Vis/NIR) hyperspectral imaging system (376-1044 nm) and machine learning methods were used to identify aphid infection in cotton leaves. Both tall and short cotton plants (Lumianyan 24) were inoculated with aphids, and the corresponding plants without aphids were used as control. The hyperspectral images (HSIs) were acquired five times at an interval of 5 days. The healthy and infected leaves were used to establish the datasets, with each leaf as a sample. The spectra and RGB images of each cotton leaf were extracted from the hyperspectral images for one-dimensional (1D) and two-dimensional (2D) analysis. The hyperspectral images of each leaf were used for three-dimensional (3D) analysis. Convolutional Neural Networks (CNNs) were used for identification and compared with conventional machine learning methods. For the extracted spectra, 1D CNN had a fine classification performance, and the classification accuracy could reach 98%. For RGB images, 2D CNN had a better classification performance. For HSIs, 3D CNN performed moderately and performed better than 2D CNN. On the whole, CNN performed relatively better than conventional machine learning methods. In the process of 1D, 2D, and 3D CNN visualization, the important wavelength ranges were analyzed in 1D and 3D CNN visualization, and the importance of wavelength ranges and spatial regions were analyzed in 2D and 3D CNN visualization. The overall results in this study illustrated the feasibility of using hyperspectral imaging combined with multi-dimensional CNN to detect aphid infection in cotton leaves, providing a new alternative for pest infection detection in plants.