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This study investigated the effects of substrates composed of various ratios of wood fiber and peat (0, 25, 50, 75, and 100% peat (v/v)) mixed with different amounts of lime (0, 2, 4, 6, and 8 g L−1) and start fertilizer (0, 2, and 4 g L−1 Multimix) on the growth and biomass accumulation of petunia (Petunia x hybrida Vilm 'Finity F1 Purple') and basil (Ocimum basilicum L. 'Marian') in an ebb-and-flow greenhouse system. Growth parameters included plant height, weight, canopy diameter, and chlorosis symptoms for petunia, along with substrate pH and EC measurements. Petunia showed optimal growth in substrates with higher peat content, while basil produced satisfactory biomass across a pH range of 5–7 regardless of substrate type. Optimal petunia cultivation in 100% wood fiber required a significant dose of start fertilizer without lime. Monitoring pH and EC using pour-through and press methods revealed a pH decrease in substrates with added start fertilizer, while substrates with higher wood fiber content were less acidic. Substrates with over 50% (v/v) wood fiber without lime showed a rapid pH increase over five weeks. The pour-through method generally underestimated EC values compared to the press method. These findings contribute to optimizing the wood fiber/peat blends for sustainable horticulture. [ABSTRACT FROM AUTHOR]

Raman spectroscopy provides detailed information about the molecular composition of a sample. The classical identification of components in a multi-component sample typically involves comparing the preprocessed spectrum with a known reference stored in a database using various spectral matching or machine-learning techniques or relies on universal models based on a two-step analysis including first, the component identification, and then the decomposition of the mixed signal. However, although large databases and universal models cover a wide range of target materials, they may be not optimized to the variability required in a specific application. In this study, we propose a single-step method using deep learning (DL) modeling to decompose a simulated mixture of real measurements of Raman scattering into relevant individual components regardless of noise, baseline and the number of components involved and quantify their ratios. We hypothesize that training a custom DL model for applications with a fixed set of expected components may yield better results than applying a universal quantification model. To test this hypothesis, we simulated 12,000 Raman spectra by assigning random ratios to each component spectrum within a library containing 13 measured spectra of organic solvent samples. One of the DL methods, a fully connected network (FCN), was designed to work on the raw spectra directly and output the contribution of each component of the library to the input spectrum in form of a component ratio. The developed model was evaluated on 3600 testing spectra, which were simulated similarly to the training dataset. The average component identification accuracy of the FCN was 99.7%, which was significantly higher than that of the universal custom trained DeepRaman model, which was 83.1%. The average mean absolute error for component ratio quantification was 0.000562, over one order of magnitude smaller than that of a well-established non-negative elastic net (NN-EN), which was 0.00677. The predicted non-zero ratio values were further used for component identification. Under the assumption that the components of a mixture are from a fixed library, the proposed method preprocesses and decomposes the raw data in a single step, quantifying every component in a multicomponent mixture, accurately. Notably, the single-step FCN approach has not been implemented in the previously reported DL studies. [ABSTRACT FROM AUTHOR]

Introduction: Production of strawberries in greenhouses and polytunnels is gaining popularity worldwide. This study investigated the effect of reuse of coir and peat, two substrates commonly adapted to soilless strawberry production, as well as stand-alone wood fiber from Norway spruce, a promising substrate candidate. Methods: The experiment was performed in a polytunnel at NIBIO Apelsvoll, Norway, and evaluated both virgin substrates, as well as spent materials that were used in one or two years. Yield, berry quality and plant architecture of the strawberry cultivar 'Malling Centenary' were registered. In addition, chemical and physical properties of virgin and reused substrates were investigated. Results: While plants grown in peat and wood fiber had highest yield in the first year of production, the berry yield was slightly reduced when these substrates were utilized for the second and third time. However, yield was comparable to the yield level attained in new and reused coir. Interestingly, berries grown in wood fiber had a tendency to a higher sugar accumulation. This substrate also produced the highest plants. Stand-alone wood fiber was the substrate with the highest accumulation of nitrogen during the three consecutive production cycles. All three investigated materials revealed a trend for decreased potassium accumulation. Wood fiber is characterized by the highest percentage of cellulose, however after three years of production the cellulose content was reducedto the same levels as for coir and peat. Discussion: Implementation of wood fiber as a growing medium, as well as general practice of substrate reuse can be therefore an achievable strategy for more sustainable berry production. [ABSTRACT FROM AUTHOR]

Cultivation of strawberries in greenhouses and polytunnels is increasing, and new sustainable growing media are needed to replace peat and coconut coir. This study investigated the effect of wood fiber and compost as growing media on hydroponically cultivated strawberries. Two experiments were conducted, where the everbearing cultivar 'Murano' was grown in mixtures of wood fiber and compost (Experiment 1) and the seasonal flowering cultivar 'Malling Centenary' was grown in mixtures of wood fiber and peat (Experiment 2). Additionally, in Experiment 2, the effect of adding start fertilizer was assessed. The yield potential of 'Murano' plants was maintained in all substrates compared to the coconut coir control. However, a mixture of 75% wood fiber and 25% compost produced the highest yield, suggesting that mixtures of nutritious materials with wood fiber may improve plant performance. The chemical composition of the berries was not affected by the substrate composition; however, berries from plants grown in the best performing blend had a lower firmness than those grown in coconut coir. 'Malling Centenary' plants produced higher yields in substrates enriched with start fertilizer. Generally, the productivity of 'Malling Centenary' plants was maintained in blends containing up to 75% of wood fiber mixture even without start fertilizer. [ABSTRACT FROM AUTHOR]

It has been long known that thermal imaging may be used to detect stress (e.g. water and nutrient deficiency) in growing crops. Developments in microbolometer thermal cameras, such as the introduction of imaging arrays that may operate without costly active temperature stabilization, have vitalized the interest in thermal imaging for crop measurements. In this study, we have focused on the challenges occurring when temperature stabilization is omitted, including the effects of focal-plane-array (FPA) temperature, camera settings and the environment in which the measurements are performed. Further, we have designed and tested models for providing thermal response from an analog LWIR video signal (typical output from low-cost microbolometer thermal cameras). Finally, we have illustrated and discussed challenges which typically occur under practical use of thermal imaging of crops, by means of three cereal showcases, including proximal and remotely based (UAV) data acquisition. The results showed that changing FPA temperature greatly affected the measurements, and that wind and irradiance also appeared to affect the temperature dynamics considerably. Further, we found that adequate settings of camera gain and offset were crucial for obtaining a reliable result. The model which was considered best in terms of transforming video signals into thermal response data included information on camera FPA temperature, and was based on a priori calibrations using a black-body radiation source under controlled conditions. Very good calibration (r2>0.99, RMSE=0.32°C, n=96) was obtained for a target temperature range of 15-35°C, covering typical daytime crop temperatures in the growing season. However, the three showcases illustrated, that under practical conditions, more factors than FPA temperature may need to be corrected for. In conclusion, this study shows that thermal data acquisition by means of an analog, uncooled thermal camera may represent a possible, cost-efficient method for the detection of crop stress, but appropriate corrections of disturbing factors are required in order to obtain sufficient accuracy. [ABSTRACT FROM AUTHOR]