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Bioactive levels of Zn, Pb, Cu, Cd and Mg, Fe in pollution sensitive and tolerant Scots pines needles – Is survival mineral-dependent?
Impact of simulated acid rains on leakage of mineral elements from foliage of conifers and monocotyledons – adaptability matters
Opracowanie innowacyjnych środków wspomagających uprawę i ochronę roślin przy jednoczesnej poprawie ich zdrowotności i jakości gleb poprzez wykonanie badań przemysłowych i eksperymentalnych prac rozwojowych
Sustainable Solutions for Arid Regions: Harnessing Aquaponics Water to Enhance Soil Quality in Egypt
Dual use of water for fish and crop production could be a promising approach to improve irrigation under arid conditions. A watercress pot study was carried out to assess the effects of irrigation by catfish and tilapia aquaculture water on the sandy soil properties as well as the growth parameters of watercress with various combinations of artificial NPK fertilizers at El-Minia Governorate of Egypt (28°18′16″ N latitude and 30°34′38″ E longitude). Catfish aquaculture water had the greatest phytoplankton abundance at 83,762 units (×104/L), while the minimum number of phytoplankton existed in tilapia aquaculture water, recorded at 14,873 units (×104/L). There were significant average changes that varied from 120 to 237 (×104 cfu/mL−1) in total bacterial counts in tilapia and catfish waters. Watercress growth quality parameters closely paralleled at all NPK application rates, indicating that the highest quality plants were produced in pots receiving 25% of the recommended levels and irrigated with catfish aquaculture water. Nitrate concentrations of watercress plants were determined under pollution levels established by the European Commission for leafy and tuber vegetables. In conclusion, the use of microbial and phytoplankton-rich aquaculture water to irrigate vegetables and as fertilizer can maintain a balanced soil ecosystem.
Dynamics of Eucalyptus and Sorghum biomass growth and nitrogen assessment at a Saharan sandy soil irrigated with treated wastewater
Potential Efficiency of Wild Plant Species (Pluchea dioscoridis (L.) DC.) for Phytoremediation of Trace Elements on Contaminated Locations
The current study outlines the potential of wild plant species (Pluchea dioscoridis (L.) DC.) for the phytoremediation of trace elements (TEs) such as Pb, Cd, Zn, Mn, and Cu at various contaminated locations: an industrial area (S1); a residential area with a high traffic load (S2); and a rural area (S3). Data showed that the photosynthetic pigments and flavonoids decreased significantly at S1, at which TEs accumulated with high concentrations. This drop in chlorophyll concentration reflects foliar damage caused by TE contamination. The carotenoids/chlorophyll index (Car/Chl) ratio showed non-significant variations for all studied spheres. High values of chlorophyll ratio (a/b) were also recorded in plant leaves which faced TE stress. The translocation factors (TF); enrichment coefficient for root (ECR); and shoot (ECS) varied clearly among the TEs as well as the studied sites, proving the ability of the plant to carry out phytoremediation of Pb, Cd, and Zn. The highest values of the metal accumulation index (MAI) were recorded at S1. Significant positive correlations for the pairs Cd and Pb in soil versus P. dioscoridis tissues indicated its usefulness as a phytoextraction strategy for these elements. The management of residential and rural areas should be exploiting the natural wild phytoremediation potential of this plant.
Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits (Phoenix dactylifera L.)
The popularity and demand for high-quality date palm fruits (Phoenix dactylifera L.) have been growing, and their quality largely depends on the type of handling, storage, and processing methods. The current methods of geometric evaluation and classification of date palm fruits are characterised by high labour intensity and are usually performed mechanically, which may cause additional damage and reduce the quality and value of the product. Therefore, non-contact methods are being sought based on image analysis, with digital solutions controlling the evaluation and classification processes. The main objective of this paper is to develop an automatic classification model for varieties of date palm fruits using a convolutional neural network (CNN) based on two fundamental criteria, i.e., colour difference and evaluation of geometric parameters of dates. A CNN with a fixed architecture was built, marked as DateNET, consisting of a system of five alternating Conv2D, MaxPooling2D, and Dropout classes. The validation accuracy of the model presented in this study depended on the selection of classification criteria. It was 85.24% for fruit colour-based classification and 87.62% for the geometric parameters only; however, it increased considerably to 93.41% when both the colour and geometry of dates were considered.