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This study is intended for forest owners considering options to increase the efficiency of the production of forest seedlings in automated nurseries. In the short rotation technology of the Scots pine (Pinus sylvestris L.), the production of seedlings was integrated in the process of grading seeds by spectrometric features, followed by the fall outplanting of containerized seedlings to a restorable post-pyrogenic site. There are studies of the spectrometric effect grading viable seeds, but a negligible amount of testing has been conducted on the seedling's field performance from these seeds. It was very important for us to evaluate the morphogenesis of juvenile Scots pine trees in relation to the change in morphological features—increase in root collar diameter (RCD-increment) and increase in stem height (SH-increment)—in the second and third growing season from the field outplanting. To determine the nature of the interaction between independent (RCD increase), dependent (SH-increment), and categorical (seed spectrometric categories and timespans) variables, a regression analysis was performed using the R statistical software. All coefficients of the linear models are statistically significant at the 1% significance level, and all are positively associated with an increase in seedling height. On average, a RCD increment by 0.1 cm causes a SH-increment by 0.72 ± 0.16 cm (p = 9.779 × 10−6). In addition, in 2020, compared to 2019, the SH-increment was 5.46 ± 0.37 cm (p < 0.001). The seeds' differentiation into spectrometric categories is strongly correlated with the stem height increment (p < 0.001). Thus, in order to improve the plant propagation protocol, it seems advisable to first condition the seeds in accordance with the spectrometric feature, and to then apply a coating (by pelleting or encapsulating) in order to improve the storage and seeding conditions. For a medium-term assessment of the short rotation technology with an integrated seed spectrometric grading in the future, the morphogenesis of Scots pine trees at 5, 7 and 10 growing seasons from the seedlings field outplanting is controlled. [ABSTRACT FROM AUTHOR]

The automated grading of Scots pine seeds in the near-infrared wavelength region (NIR grading) is a starting point for further actions, such as coating and priming. This reduces the time and financial costs and increases the accuracy of seed viability classification compared to invasive techniques. The NIR-based wave reflected from each pine seed must be detected and processed with sufficient accuracy. To focus the reflected beam, we used fiber-optic Bragg grating, a Bragg mirror, and diffraction grating. For each focusing option based on the DOE matrix, one experiment of 20 runs (n = 20) and three replicas (m = 3) in each run was conducted. In each replica, we used 100 conditioned and 100 non-conditioned seeds (NC + NNC = 200) selected randomly from five samples weighing 50 g from a seedlot weighing 1 kg extracted from cones collected from a natural tree stand. Three experiments were conducted on the NIR grading of Scots pine seeds using an optoelectronic device. An adequate DOE regression model of the grading efficiency function was obtained. The functions included the following arguments: angle of incidence of the optical beam, NIR wavelength reflected from the seed, and height of the seed pipeline. The influence of the inclination angle of the light source relative to the plane of pine seed movement on the grading quality prevails over other factors. The NIR grading of Scots pine seeds allows the separation of seeds according to the viability index, which is important, since dead petrified seeds (possibly up to 25%) may occur in the seed batch, which cannot be eliminated by either seed size or mass. The peak of NIR grading is achieved by combining the average grader engineering parameters: 968–973 nm for the wavelength and 44–46 degrees for the inclination angle of the reflected beam at a seed pipe size of 0.18–0.23 m. [ABSTRACT FROM AUTHOR]

Modern techniques that enable high-precision and rapid identification/elimination of wheat seeds infected by Fusarium head blight (FHB) can help to prevent human and animal health risks while improving agricultural sustainability. Robust pattern-recognition methods, such as deep learning, can achieve higher precision in detecting infected seeds using more accessible solutions, such as ordinary RGB cameras. This study used different deep-learning approaches based on RGB images, combining hyperparameter optimization, and fine-tuning strategies with different pretrained convolutional neural networks (convnets) to discriminate wheat seeds of the TBIO Toruk cultivar infected by FHB. The models achieved an accuracy of 97% using a low-complexity design architecture with hyperparameter optimization and 99% accuracy in detecting FHB in seeds. These findings suggest the potential of low-cost imaging technology and deep-learning models for the accurate classification of wheat seeds infected by FHB. However, FHB symptoms are genotype-dependent, and therefore the accuracy of the detection method may vary depending on phenotypic variations among wheat cultivars. [ABSTRACT FROM AUTHOR]