EFFECT OF COMPOST AND SOME NATURAL GROWTH PROMOTING ON CHAMOMILE

A field experiment was carried out during the two successive growing seasons of 2019/2020 and 2020/2021 at the Nursery of Ornamental Plants, Faculty of Agriculture, Minia University to study the effect of compost (0.0, 2.5, 5.0 and 7.5 ton/fed) and foliar spray with ascorbic and salicylic acids, each at 50, 100 and 200 ppm, on growth, productivity of flowers and essential oil of chamomile(Matricaria chamomilla, L.) plants. Results indicated that vegetative growth traits (plant height and number of branches/plant), flowers fresh and dry weights/plant, essential oil (%) and yield/plant as well as photosynthetic pigments were significantly improved as a result of applied compost at the three levels with the highest values were obtained with 7.5 ton/fed treatment. Also, all concentrations of ascorbic and salicylic acids led to significant increases in all previous characters of vegetative growth, flowers and essential oil productivity compared to control. The combination treatment of compost (7.5 ton/fed) with salicylic acid (200 ppm) was superior than the other interaction treatments

RESPONSE OF JOJOBA (SIMMONDSIA CHINENSIS, LINK) PLANTS TO COMPOST AND SOME STIMULATING SUBSTANCES TREATMENTS

This work was done during the two experimental seasons of 2020 and 2021 at the farm of Faculty of Agriculture, Minia University, Egypt to study the reaction of jojoba (Simmondsia chinensis, Link) plants to compost (0.0, 500, 1000 and 1500 g/container) and some stimulating substances (control, vitamin E at 100 ppm, active yeast at 10 g/l and garlic extract at 10%) treatments. Data revealed that increasing the level of compost led to a significantly increased in plant height, main stem thickness, number of branches/plant and aerial parts and root weights (fresh or dry) compared with control. While sprayed plants with all stimulating substances significantly enhanced all previous characters compared with the control. It can be concluded that the high level of compost (1500 g/container) plus yeast at 10 g/l or garlic extract at 10% led to the greatest growth parameters of jojoba plan

EFFECT OF COMPOST AND SOME STIMULATING SUBSTANCES ON LEAVES AND SOME CHEMICAL COMPOSITION OF JOJOBA PLANT

This work was performed in the two experimental seasons of 2020 and 2021 at the farm of the Faculty of Agriculture, Minia University, Egypt to study the effect of compost (0.0, 500, 1000 and 1500 g/container) and some stimulating substances (control, vitamin E at 100 ppm, bread yeast at 10 g/l and garlic extract at 10%) on leaves and some chemical composition of jojoba (Simmondsia chinensis, Link) plants. Data showed that all used levels of compost pronounced increased leaves traits (number, leaf area, leaves weights (fresh or dry) in addition to chemical composition (pigments content, NPK percentages and protein) compared with the control. The 1500 g compost/container was more active than other treatments. Also, sprayed plants with vitamin E, active yeast and garlic extract significantly improved all previous parameters compared with the control. In general, the treatment of active yeast was superior, followed by garlic extract than vitamin E treatment. Therefore, the best interaction treatment was recorded with the high level of compost (1500 g/container) plus bread active yeast (10 g/l) or garlic extract (10 %)

Eye Tracking-Based Diagnosis and Early Detection of Autism Spectrum Disorder Using Machine Learning and Deep Learning Techniques

Eye tracking is a useful technique for detecting autism spectrum disorder (ASD). One of the most important aspects of good learning is the ability to have atypical visual attention. The eye-tracking technique provides useful information about children’s visual behaviour for early and accurate diagnosis. It works by scanning the paths of the eyes to extract a sequence of eye projection points on the image to analyse the behaviour of children with autism. In this study, three artificial-intelligence techniques were developed, namely, machine learning, deep learning, and a hybrid technique between them, for early diagnosis of autism. The first technique, neural networks [feedforward neural networks (FFNNs) and artificial neural networks (ANNs)], is based on feature classification extracted by a hybrid method between local binary pattern (LBP) and grey level co-occurrence matrix (GLCM) algorithms. This technique achieved a high accuracy of 99.8% for FFNNs and ANNs. The second technique used a pre-trained convolutional neural network (CNN) model, such as GoogleNet and ResNet-18, on the basis of deep feature map extraction. The GoogleNet and ResNet-18 models achieved high performances of 93.6% and 97.6%, respectively. The third technique used the hybrid method between deep learning (GoogleNet and ResNet-18) and machine learning (SVM), called GoogleNet + SVM and ResNet-18 + SVM. This technique depends on two blocks. The first block used CNN to extract deep feature maps, whilst the second block used SVM to classify the features extracted from the first block. This technique proved its high diagnostic ability, achieving accuracies of 95.5% and 94.5% for GoogleNet + SVM and ResNet-18 + SVM, respectively