Drought is an important abiotic stress factor limiting crop productivity worldwide and its impact is increasing with climate change. Regardless of the plant growth period, drought has a deadly and yield-reducing effect on the plant at every stage of development. As with many environmental stressors, drought-exposed plants trigger a series of molecular, biochemical, and physiological responses to overcome the effect of drought stress. Currently, researchers are trying to determine the complex functioning of drought stress response in plants with different approaches. Plants are more sensitive to drought stress during certain critical stages like germination, seedling formation, flowering, fertilization, and grain formation periods. Plants have high success in reducing the effects of drought stress in vegetative development periods with the activity of tolerance mechanisms. On the other hand, drought stress during the generative period can cause irreversible losses in yield. This review focuses on the progression of molecular, biochemical, and physiological mechanisms involved in the drought stress tolerance in plants and the responses of field crops to drought stress at different development stages.

Salt stress is one of the harmful abiotic stress factors. It makes agricultural lands especially in arid and semi-arid regions useless despite the efforts. More than six percent of total world agricultural lands are on the edge of vanishing due to salt stress. Salinity in soil occurs as a result of the factors such as lack of drainage, improper irrigation, excessive accumulation of soluble salts. Salinity limits the growth of plants. Despite the main results, some results of plants due to these limitations vary from species to species. The negative effects get morphological, biochemical and physiological reactions from plants. Slowed or stopped growth of roots and shoots, closuring of stomata, germination slowing, decreased or stopped development of seedling, deterioration of photosynthetic activity are the main reactions of plants to stress. On the other hand, plants also develop tolerance mechanisms as a result of some auxiliaries for surviving under adverse conditions. Plants have tendency to protect themselves from salinity with osmotic protectants synthesized by them such as sugars, proline, amino acids, glycine betaine. In this review, the responses of plants to salt stress were investigated and gathered.

Sugar beet (Beta vulgaris L.) is cultivated in temperate climates worldwide to produce sugar. The production of sugar beet and other plants is in danger due to the world’s increasingly salinized soils. Although different sugar beet genotypes exist at various ploidy levels, most of them are diploid (2X) with 18 chromosomes. The majority of polyploid plants have different variations, morphologies, and anatomy. Diploid and polyploid plants especially have different morphology, physiology, cellularity, and biochemistry. As a result, polyploidy has been identified as an essential component in determining plant salt tolerance. To evaluate the effects of salt (NaCl) stress on sugar beet genotypes, diploid (2X), triploid (3X), and tetraploid (4X) genotypes were exposed to 0 (control), 50, and 150 mM NaCl concentrations for seven weeks. Under control conditions, the diploid (2X) genotype showed higher growth performance compared to the tetraploid (4X) and triploid (3X) genotypes, respectively. Regarding germination and early-stage growth performance, a reduction of about 50% was observed in the diploid (2X) genotype under salt stress compared to the control condition. The diploid (2X) genotype showed higher germination, a greater salt tolerance index, and better seedling growth performance than the other ploidy levels. Late-stage growth, leaf length, leaf width, leaf area, cytological findings, and total chlorophyll content were all shown to be higher and less reduced (around 30%) under salt stress in diploid (2X) genotypes. Even though all of the findings in this study showed a decrease when plants were exposed to salt (NaCl), the diploid (2X) ploidy level plants displayed more robust growth and development than the triploid (3X) and tetraploid (4X) genotypes.

The experimental population of this work was concerned with the classification of seeds of five proso millet ( Panicum miliaceum L.) genotypes (KY1, KY2, KY3, KY4, DS20), which were produced by single-plant selection using Turkish landraces. It was classified based on the morphometric and color features, which were obtained by using a scanner-based image-processing system, and the nutritional parameters obtained by near-infrared (NIR) spectroscopy, namely their suitability to human consumption. An equal image dataset was assembled, which had 500 seeds in each genotype. This was achieved by scanning the seeds at a resolution of 600 dpi of five technical replicates where each replicate had 100 seeds. To perform the nutritional analysis, 100 spectral measurements of an approximate amount of 300 g of ground sample of each genotype were measured using a calibration model specifically based on cereals, and this was repeated five times. In the classification task, there were ten different machine learning algorithms (J48, kNN, LMT, MLP, NB, REPTree, RF, RT, SL, SMO) and two data mining algorithms (CHAID, CART) carefully tested with ten-fold stratified cross validation. The findings clearly showed that the random forest (RF) algorithm showed the best and the most stable performances in all metrics true positive rate [TPR] = 0.950, F -measure = 0.950, Matthews correlation coefficient [MCC] = 0.938, receiver operating characteristic area under curve [ROC AUC] = 0.996, and precision-recall area under curve [PRC AUC] = 0.985. RF model was next followed by the CART and REPTree algorithms, whereas the kNN algorithm had relatively lower classification success. All these results support the notion that morphometric signatures and decision based on NIR-based compositional data have some synergy in proso millet that can be utilized to offer an applicable, repeatable, and cost-effective decision framework to address critical processes including the control of seed purity, quick verification of variety, and quality classification.

Appearance is one of the important traits in seeds. Appearance-related features such as shape, size, and color are important parameters in distinguishing seeds from each other. Machine learning algorithms are used to distinguishing plant seed species for different purposes. In this study, four faba bean cultivars (Alexia, Alice, Jasmin, and Arabella) were used to distinguishing based on appearance measurements including shape and size features analyzed in pairs. Eleven machine learning algorithms (NB, MLP, SGD, SL, LMT, SMO, kNN, J48, Random Forest, Random Tree, REPTree) were used to assess binary classification performance utilizing red-green-blue (RGB) color channels through a image processing system. Among all pairs, faba bean seeds of the Alexia and Alice cultivars had the greatest classification accuracy of 90.5% using the Random Forest, and 87.5% with the MLP, SGD, and J48 models. The MLP model achieved the highest accuracy rate of 87% for the categorization of Alexia vs Arabella cultivars, followed by the J48 model with an accuracy rate of 84%. The Alice cultivar possesses the greatest values for area (83.80 mm²), perimeter (47.43 mm), width (9.28 mm), and length (12.50 mm). Wilks' lambda results indicated that the variations in external appearance of faba bean varieties are significant (p < 0.01). All of these results indicated that machine learning algorithms can effectively differentiate faba bean seeds based on their physical characteristics.