Arbuscular mycorrhizal fungi (AMF) are found to be causing the most abundant symbioses between fungi and roots of terrestrial plants. AMF act as a biofertiliser that stimulate plant growth and increase plant productivity under poor soil fertility. In addition, unbalanced application of zinc (Zn) and the antagonistic relationship with phosphorus (P) also play an imperative role in decreasing crop productivity. It is necessary to synchronise Zn application rate with applied inorganic P and AMF to achieve optimum crop yield. For tha purpose, a pot trial was conducted on Zn-deficient soil with five application rates of Zn, i.e. 0, 30, 60, 90, 120 and 150 mg kg−1. Two levels of inorganic P [0 (P0) and 21 (P1) mg kg−1] were applied with and without AMF inoculation. Results showed that more AMF colonisation was observed under deficient Zn and P conditions. Higher soil Zn (Zn120 and Zn150) significantly decreased the germination rate and plant growth. However, a significant improvement in germination, plant height, biomass, transpiration rate and 100-grain weight validated the productive functioning of AMF over no AMF. AMF inoculation alleviated P-induced Zn deficiency and Zn-induced P deficiency. Application of P0Zn60 and P0Zn30 with and without AMF is a better treatment to maximise wheat growth, yield and gas-exchange attributes in Zn-deficient conditions. It is also recommended to apply low Zn, (30 or 60 mg kg−1 Zn) when AMF is used, with 21 mg kg−1 P, or half of the recommended dose of P.
The goal of this research is to provide a useful technique for better facial emotion recognition, especially across cultural boundaries. Although people communicate both verbally and nonverbally, face expressions are crucial in determining verbal communication. The previous human-computer interface did not take into account thus much nonverbal communication. We need a system that can recognise and comprehend the intentions and feelings expressed by social and cultural cues. In this article, we present a technique for categorising facial photos into six different categories of expressions. Three phases make up the approach; in the first, we used viola Jones to edit off all but the face from the original image and create new ones. Then a HOG histogram was used to extract gradient characteristics. Last but not least, we used SVM to classify picture characteristics and got encouraging results. Comparing the outcomes of the suggested method to other cutting-edge approaches, they are astounding. With regard to combined cross-cultural datasets, it offers accuracy of 99.97%.
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