Browsing by Subject "Plant"

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    Open Access
    Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions
    (Elsevier, 2021-10-15) Sabzi, Sajad; Pourdarbani, Razieh; Rohban, Mohammad H.; García Mateos, Ginés; Arribas, J. I.; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
    In recent years, farmers have often mistakenly resorted to overuse of chemical fertilizers to increase crop yield. However, excessive consumption of fertilizers might lead to severe food poisoning. If nutritional deficiencies are detected early, it can help farmers to design better fertigation practices before the problem becomes unsolvable. The aim of this study is to predict the amount of nitrogen (N) content in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods: a hybrid artificial neural networks-particle swarm optimization (ANN-PSO); partial least squares regression (PLSR); and unidimensional deep learning convolutional neural networks (CNN). Cucumber plant seeds were planted in 20 different pots. After growing the plants, pots were categorized and three levels of nitrogen overdose were applied to each category: 30%, 60% and 90% excesses, called N30%, N60%, N90%, respectively. HSI images of plant leaves were captured before and after the application of nitrogen excess. A prediction regression model was developed for each individual category. Results showed that mean regression coefficients (R) for ANN-PSO were inside 0.937–0.965, PLSR 0.975–0.997, and CNN 0.965–0.985 ranges, test set. We conclude that regression models have a remarkable ability to accurately predict the amount of nitrogen content in cucumber plants from hyperspectral leaf images in a non-destructive way, being PLSR slightly ahead of CNN and ANN-PSO methods.
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    Open Access
    Microcystin influence on soil-plant microbiota: unraveling microbiota modulations and assembly processes in the rhizosphere of Vicia faba
    (Elsevier, 2024-02-06) Redouane, El Mahdi; Núñez, Andrés; Achouak, Wafa; Barakat, Mohamed; Alex, Anoop; Martins, José Carlos; Tazart, Zakaria; Mugani, Richard; Zerrifi, Soukaina El Amrani; Haida, Mohammed; García, Ana M.; Campos, Alexandre; Lahrouni, Majida; Oufdou, Khalid; Vasconcelos, Vitor; Oudra, Brahim; Genética y Microbiología
    Microcystins (MCs) are frequently detected in cyanobacterial bloom-impacted waterbodies and introduced into agroecosystems via irrigation water. They are widely known as phytotoxic cyanotoxins, which impair the growth and physiological functions of crop plants. However, their impact on the plant-associated microbiota is scarcely tackled and poorly understood. Therefore, we aimed to investigate the effect of MCs on microbiota-inhabiting bulk soil (BS), root adhering soil (RAS), and root tissue (RT) of Vicia faba when exposed to 100 μg L−1 MCs in a greenhouse pot experiment. Under MC exposure, the structure, co-occurrence network, and assembly processes of the bacterial microbiota were modulated with the greatest impact on RT-inhabiting bacteria, followed by BS and, to a lesser extent, RAS. The analyses revealed a significant decrease in the abundances of several Actinobacteriota-related taxa within the RT microbiota, including the most abundant and known genus of Streptomyces. Furthermore, MCs significantly increased the abundance of methylotrophic bacteria (Methylobacillus, Methylotenera) and other Proteobacteria-affiliated genera (e.g., Paucibacter), which are supposed to degrade MCs. The co-occurrence network of the bacterial community in the presence of MCs was less complex than the control network. In MC-exposed RT, the turnover in community composition was more strongly driven by deterministic processes, as proven by the beta-nearest taxon index. Whereas in MC-treated BS and RAS, both deterministic and stochastic processes can influence community assembly to some extent, with a relative dominance of deterministic processes. Altogether, these results suggest that MCs may reshape the structure of the microbiota in the soil-plant system by reducing bacterial taxa with potential phytobeneficial traits and increasing other taxa with the potential capacity to degrade MCs.