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<jats:p>Panicle number is directly related to rice yield, so panicle detection and counting has always been one of the most important scientific research topics. Panicle counting is a challenging task due to many factors such as high density, high occlusion, and large variation in size, shape, posture et.al. Deep learning provides state-of-the-art performance in object detection and counting. Generally, the large images need to be resized to fit for the video memory. However, small panicles would be missed if the image size of the original field rice image is extremely large. In this paper, we proposed a rice panicle detection and counting method based on deep learning which was especially designed for detecting rice panicles in rice field images with large image size. Different object detectors were compared and YOLOv5 was selected with MAPE of 3.44% and accuracy of 92.77%. Specifically, we proposed a new method for removing repeated detections and proved that the method outperformed the existing NMS methods. The proposed method was proved to be robust and accurate for counting panicles in field rice images of different illumination, rice accessions, and image input size. Also, the proposed method performed well on UAV images. In addition, an open-access and user-friendly web portal was developed for rice researchers to use the proposed method conveniently.</jats:p>

<jats:p>Secondary metabolites are incontestably key specialized molecules with proven health-promoting effects on human beings. Naturally synthesized secondary metabolites are considered an important source of pharmaceuticals, food additives, cosmetics, flavors, etc., Therefore, enhancing the biosynthesis of these relevant metabolites by maintaining natural authenticity is getting more attention. The application of exogenous jasmonates (JAs) is well recognized for its ability to trigger plant growth and development. JAs have a large spectrum of action that covers seed germination, hypocotyl growth regulation, root elongation, petal expansion, and apical hook growth. This hormone is considered as one of the key regulators of the plant’s growth and development when the plant is under biotic or abiotic stress. The JAs regulate signal transduction through cross-talking with other genes in plants and thereby deploy an appropriate metabolism in the normal or stressed conditions. It has also been found to be an effective chemical elicitor for the synthesis of naturally occurring secondary metabolites. This review discusses the significance of JAs in the growth and development of plants and the successful outcomes of jasmonate-driven elicitation of secondary metabolites including flavonoids, anthraquinones, anthocyanin, xanthonoid, and more from various plant species. However, as the enhancement of these metabolites is essentially measured <jats:italic>via in vitro</jats:italic> cell culture or foliar spray, the large-scale production is significantly limited. Recent advancements in the plant cell culture technology lay the possibilities for the large-scale manufacturing of plant-derived secondary metabolites. With the insights about the genetic background of the metabolite biosynthetic pathway, synthetic biology also appears to be a potential avenue for accelerating their production. This review, therefore, also discussed the potential manoeuvres that can be deployed to synthesis plant secondary metabolites at the large-scale using plant cell, tissue, and organ cultures.</jats:p>

<jats:p>Global climate change will cause longer and warmer autumns, thus negatively affecting the quality of cold acclimation (CA) and reducing the freezing tolerance (FT) of winter wheat. Insufficient FT and fluctuating temperatures during winter can accelerate the deacclimation (DEA) process, whereas reacclimation (REA) is possible only while the vernalization requirement is unfulfilled. Six winter wheat genotypes with different winter hardiness profiles were used to evaluate the impact of constant low-temperature (2°C) and prolonged higher low-temperature (28 days at 10°C followed by 2°C until day 49) on shoot biomass and metabolite accumulation patterns in leaf and crown tissues throughout 49 days of CA, 7 days of DEA, and 14 days of REA. The FT of winter wheat was determined as LT<jats:sub>30</jats:sub> values by conducting freezing tests after CA, DEA, and REA. Shoot biomass accumulation, projected as the green leaf area (GLA), was investigated by non-destructive RGB imaging-based phenotyping. Dynamics of carbohydrates, hexose phosphates, organic acids, proteins, and amino acids were assessed in leaf and crown tissues. Results revealed that exposure to higher low-temperature induced higher accumulation of shoot biomass and had a negative impact on FT of winter wheat. Prolonged higher low-temperature negatively affected the accumulation of soluble carbohydrates, protein content and amino acids, and had a positive effect on starch accumulation in leaf and crown tissues after CA, in comparison with the constant low-temperature treatment. DEA resulted in significantly reduced FT. Lower concentrations of glucose-6-phosphate, sucrose and proline, as well as higher concentrations of starch in leaves and crowns were found after DEA. The majority of the genotypes regained FT after REA; higher concentrations of glucose and malate in leaves, and sucrose in crown tissue were observed, whereas starch accumulation was decreased in both tissues. Negative correlations were determined between FT and starch concentration in leaves and crowns, while proline and proteins, accumulated in crowns, showed positive correlations with FT. This study broadens the knowledge regarding the effect of different low-temperature regimes on the dynamics of metabolite accumulation in winter wheat throughout CA, DEA, and REA, and its relationship to biomass accumulation and FT.</jats:p>

<jats:p>The allotetraploid Solanaceae plant <jats:italic>Nicotiana benthamiana</jats:italic> contains two closely related receptor-like proteins (RLPs), NbEIX2 and NbRXEG1, which regulate the recognition of VdEIX3 and PsXEG1, respectively. VdEIX3, PsXEG1, and their homologs represent two types of microbe-associated molecular patterns (MAMPs) that are widespread in diverse pathogens. Here, we report that NbRXEG1 also participates in VdEIX3 recognition. Both <jats:italic>eix2</jats:italic> and <jats:italic>rxeg1</jats:italic> single mutants exhibited significantly impaired but not abolished ability to mediate VdEIX3-triggered immune responses, which are nearly abolished in <jats:italic>eix2 rxeg1</jats:italic> double mutants. Moreover, a dominant negative mutant of <jats:italic>eix2</jats:italic> that contains a 60 bp deletion failed to respond to VdEIX3 and could suppress VdEIX3-induced cell death in the wild-type <jats:italic>N. benthamiana</jats:italic>. Further phylogenetic analyses showed that NbEIX2 and NbRXEG1 are obtained from different diploid ancestors by hybridization. These results demonstrate that the allotetraploid <jats:italic>N. benthamiana</jats:italic> recognizes two types of MAMPs by two homologous but diverged RLPs, which provides a model in which an allopolyploid plant probably exhibits defense hybrid vigor by acquiring divergent immune receptors from different ancestors.</jats:p>

<jats:p>Melon (<jats:italic>Cucumis melo</jats:italic> L.) seed germination significantly affects its economic value. Cultivation of melon varieties with high germination ability and seedling vigor is beneficial in large-scale melon propagation. In this study, two melon genotypes differing in their germination ability, P5 with low and P10 with high germination ability, were used to identify the optimal seed germination conditions by evaluating different water immersion times and germination temperatures. The germination rate of the P5 and P10 parental genotypes and their segregating population, consisting of 358 F<jats:sub>2:3</jats:sub> families, were evaluated for 2 years to identify their genetic basis. QTL analysis was performed on a high-density genetic map constructed using specific-locus amplified fragment sequencing (SLAF-seq). The germination rate of F<jats:sub>1</jats:sub> and F<jats:sub>2</jats:sub> populations treated with water immersion for 8 h at 28°C and measured at 48 h showed a normal distribution Genetic mapping carried out using the high-density genetic map revealed eight QTLs in chromosomes 2, 4, 5, 6, and 8 that control melon seed germination, of which 2020/2021-<jats:italic>qsg5.1</jats:italic> was consistently significant in both years of experimentation. <jats:italic>qsg5.1</jats:italic> explained 15.13% of the phenotypic variance with a LOD of 4.1. To fine map the candidate region of <jats:italic>qsg5.1</jats:italic>, eight cleaved amplified polymorphism sequence (CAPS) markers were used to construct a genetic map with another 421 F<jats:sub>2</jats:sub> individual fruits. The major QTL <jats:italic>qsg5.1</jats:italic> was located between SNP53 and SNP54 within a 55.96 Kb interval containing four genes. qRT-PCR gene expression analysis of the candidate genes showed that <jats:italic>MELO3C031219.2</jats:italic> (<jats:italic>Phosphorus transporter PHO-5</jats:italic>) exhibited a significant difference in gene expression between the parental lines at 24, 32, and 48 h after germination, potentially being the underlying gene controlling melon seed germination. These results provide a theoretical basis for the molecular mechanisms controlling melon seed germination and can practically contribute to further improving germination to increase the propagation efficiency of commercial melon cultivars.</jats:p>

<jats:p>Climate-resilient crops with improved adaptation to the changing climate are urgently needed to feed the growing population. Hence, developing high-yielding crop varieties with better agronomic traits is one of the most critical issues in agricultural research. These are vital to enhancing yield as well as resistance to harsh conditions, both of which help farmers over time. The majority of agronomic traits are quantitative and are subject to intricate genetic control, thereby obstructing crop improvement. Plant epibreeding is the utilisation of epigenetic variation for crop development, and has a wide range of applications in the field of crop improvement. Epigenetics refers to changes in gene expression that are heritable and induced by methylation of DNA, post-translational modifications of histones or RNA interference rather than an alteration in the underlying sequence of DNA. The epigenetic modifications influence gene expression by changing the state of chromatin, which underpins plant growth and dictates phenotypic responsiveness for extrinsic and intrinsic inputs. Epigenetic modifications, in addition to DNA sequence variation, improve breeding by giving useful markers. Also, it takes epigenome diversity into account to predict plant performance and increase crop production. In this review, emphasis has been given for summarising the role of epigenetic changes in epibreeding for crop improvement.</jats:p>

<jats:p>The <jats:italic>ZmCCT</jats:italic> locus underlies both stalk-rot resistance and photoperiod sensitivity in maize (<jats:italic>Zea mays</jats:italic> L.). We previously introduced nine resistant <jats:italic>ZmCCT</jats:italic> haplotypes into seven elite but susceptible maize inbred lines (containing the haplotype <jats:italic>H1</jats:italic>) to generate 63 backcross families. Here, we continued backcrossing, followed by selfing, to develop 63 near-isogenic lines (NILs). We evaluated 22 of these NILs for stalk-rot resistance and flowering time under long-day conditions. Lines harboring the haplotype <jats:italic>H5</jats:italic> outperformed the others, steadily reducing disease severity, while showing less photoperiod sensitivity. To demonstrate the value of haplotype <jats:italic>H5</jats:italic> for maize production, we selected two pairs of NILs, 83B28<jats:italic><jats:sup>H1</jats:sup></jats:italic>/83B28<jats:italic><jats:sup>H5</jats:sup></jats:italic> and A5302<jats:italic><jats:sup>H1</jats:sup></jats:italic>/A5302<jats:italic><jats:sup>H5</jats:sup></jats:italic>, and generated F<jats:sub>1</jats:sub> hybrids with the same genetic backgrounds but different <jats:italic>ZmCCT</jats:italic> alleles: 83B28<jats:italic><jats:sup>H1</jats:sup></jats:italic> × A5302<jats:italic><jats:sup>H1</jats:sup></jats:italic>, 83B28<jats:italic><jats:sup>H1</jats:sup></jats:italic> × A5302<jats:italic><jats:sup>H5</jats:sup></jats:italic>, 83B28<jats:italic><jats:sup>H5</jats:sup></jats:italic> × A5302<jats:italic><jats:sup>H1</jats:sup></jats:italic>, and 83B28<jats:italic><jats:sup>H5</jats:sup></jats:italic> × A5302<jats:italic><jats:sup>H5</jats:sup></jats:italic>. We performed field trials to investigate yield/yield-related traits, stalk-rot resistance, flowering time, and drought/salt tolerance in these four hybrids. 83B28<jats:italic><jats:sup>H5</jats:sup></jats:italic> × A5302<jats:italic><jats:sup>H1</jats:sup></jats:italic> performed the best, with significantly improved yield, stalk-rot resistance, and drought tolerance compared to the control (83B28<jats:italic><jats:sup>H1</jats:sup></jats:italic> × A5302<jats:italic><jats:sup>H1</jats:sup></jats:italic>). Therefore, the <jats:italic>ZmCCT</jats:italic> haplotype <jats:italic>H5</jats:italic> has great value for breeding maize varieties with high yield potential, stalk-rot resistance, and drought tolerance.</jats:p>

<jats:p>The temperature during the bloom period leading up to fruit set is a key determinant of reproductive success in plants and of harvest yield in crop plants. However, it is often unclear whether differences in yield components result from temperature effects on the whole plant or specifically on the flower or fruit sinks. We used a forced-convection, free-air cooling and heating system to manipulate the inflorescence temperature of field-grown Cabernet Sauvignon grapevines during the bloom period. Temperature regimes included cooling (ambient −7.5°C), heating (ambient +7.5°C), an ambient control, and a convective control. Cooling significantly retarded the time to fruit set and subsequent berry development, and heating shortened the time to fruit set and accelerated berry development relative to the two controls. Fruit set was decreased in cooled inflorescences, but although the cooling regime resulted in the lowest berry number per cluster, it also decreased seed and berry weight at harvest while not affecting seed number. Cooling inflorescences slightly decreased fruit soluble solids and pH, and increased titratable acidity, but did not affect color density. The inflorescence temperature did not impact leaf gas exchange and shoot growth, and shoot periderm formation occurred independently of the timing of fruit ripening. These results suggest that the temperature experienced by grape flowers during bloom time impacts fruit set and subsequent seed and berry development. Suboptimal temperatures not only reduce the proportion of flowers that set fruit but also limit the sink strength of the berries that do develop after fruit set. Shoot vigor and maturation, and leaf physiology, on the other hand, may be rather insensitive to temperature-induced changes in reproductive development.</jats:p>

<jats:p>There is an increasing interest in using the Internet of Things (IoT) in the agriculture sector to acquire soil- and crop-related parameters that provide helpful information to manage farms more efficiently. One example of this technology is using IoT soil moisture sensors for scheduling irrigation. Soil moisture sensors are usually deployed in nodes. A more significant number of sensors/nodes is recommended in larger fields, such as those found in broadacre agriculture, to better account for soil heterogeneity. However, this comes at a higher and often limiting cost for farmers (purchase, labour costs from installation and removal, and maintenance). Methodologies that enable maintaining the monitoring capability/intensity with a reduced number of in-field sensors would be valuable for the sector and of great interest. In this study, sensor data analysis conducted across two irrigation seasons in three cotton fields from two cotton-growing areas of Australia, identified a relationship between soil matric potential and cumulative satellite-derived crop evapotranspiration (ET<jats:sub>cn</jats:sub>) between irrigation events. A second-degree function represents this relationship, which is affected by the crop development stage, rainfall, irrigation events and the transition between saturated and non-saturated soil. Two machine learning models [a Dense Multilayer Perceptron (DMP) and Support Vector Regression (SVR) algorithms] were studied to explore these second-degree function properties and assess whether the models were capable of learning the pattern of the soil matric potential-ET<jats:sub>cn</jats:sub> relation to estimate soil moisture from satellite-derived ET<jats:sub>c</jats:sub> measurements. The algorithms performance evaluation in predicting soil matric potential applied the k-fold method in each farm individually and combining data from all fields and seasons. The latter approach made it possible to avoid the influence of farm consultants’ decisions regarding when to irrigate the crop in the training process. Both algorithms accurately estimated soil matric potential for individual (up to 90% of predicted values within ±10 kPa) and combined datasets (73% of predicted values within ±10 kPa). The technique presented here can accurately monitor soil matric potential in the root zone of cotton plants with reduced in-field sensor equipment and offers promising applications for its use in irrigation-decision systems.</jats:p>