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The paper and pulp industry (PPI) is one of the largest industries that contribute to the growing economy of the world. While wood remains the primary raw material of the PPIs, the demand for paper has also grown alongside the expanding global population, leading to deforestation and ecological imbalance. Wood-based paper production is associated with enormous utilization of water resources and the release of different wastes and untreated sludge that degrades the quality of the environment and makes it unsafe for living creatures. In line with this, the indigenous handmade paper making from the bark of Daphne papyracea, Wall. ex G. Don by the Monpa tribe of Arunachal Pradesh, India is considered as a potential alternative to non-wood fiber. This study discusses the species distribution modeling of D. papyracea, community-based production of the paper, and glycome profiling of the paper by plant cell wall glycan-directed monoclonal antibodies. The algorithms used for ecological and geographical modeling indicated the maximum predictive distribution of the plant toward the western parts of Arunachal Pradesh. It was also found that the suitable distribution of D. papyracea was largely affected by the precipitation and temperature variables. Plant cell walls are primarily made up of cellulose, hemicellulose, lignin, pectin, and glycoproteins. Non-cellulosic cell wall glycans contribute significantly to various physical properties such as density, crystallinity, and tensile strength of plant cell walls. Therefore, a detailed analysis of non-cellulosic cell wall glycan through glycome profiling and glycosyl residue composition analysis is important for the polymeric composition and commercial processing of D. papyracea paper. ELISA-based glycome profiling results demonstrated that major classes of cell wall glycans such as xylan, arabinogalactans, and rhamnogalacturonan-I were present on D. papyracea paper. The presence of these polymers in the Himalayan Buddhist handmade paper of Arunachal Pradesh is correlated with its high tensile strength. The results of this study imply that non-cellulosic cell wall glycans are required for the production of high-quality paper. To summarize, immediate action is required to strengthen the centuries-old practice of handmade paper, which can be achieved through education, workshops, technical know-how, and effective marketing aid to entrepreneurs.

The production of regenerated cellulosic fibres, such as viscose, modal and lyocell, is based mainly on the use of dissolving wood pulp as raw material. Enzymatic processes are an excellent alternative to conventional chemical routes in the production of dissolving pulp, in terms of energy efficiency, reagent consumption and pulp yield. The two main characteristics of a dissolving pulp are the cellulose purity and the molecular weight, both of which can be controlled with the aid of enzymes. A purification process for paper-grade kraft pulp has been proposed, based on the use of xylanases in combination with hot and cold caustic extraction, without the conventional pre-hydrolysis step before kraft pulping. This enzyme aided purification allowed the production of a dissolving pulp that met the specifications for the manufacture of viscose, < 3% xylan, > 92% ISO brightness and 70% Fock’s reactivity. Endoglucanases (EGs) can efficiently reduce the average molecular weight of the cellulose while simultaneously increasing the pulp reactivity for viscose production. It is shown in this study that lytic polysaccharide monooxygenases act synergistically with EGs in the modification of bleached dissolving pulp.

White mustard (Sinapis alba L.) seed oil is used for cooking, food preservation, body and hair revitalization, biodiesel production, and as a diesel fuel additive and alternative biofuel. This review focuses on biodiesel production from white mustard seed oil as a feedstock. The review starts by outlining the botany and cultivation of white mustard plants, seed harvest, drying and storage, and seed oil composition and properties. This is followed by white mustard seed pretreatments (shelling, preheating, and grinding) and processing techniques for oil recovery (pressing, solvent extraction, and steam distillation) from whole seeds, ground seed or kernels, and press cake. Novel technologies, such as aqueous, enzyme-assisted aqueous, supercritical CO2, and ultrasound-assisted solvent extraction, are also discussed. The main part of the review considers biodiesel production from white mustard seed oil, including fuel properties and performance. The economic, environmental, social, and human health risk/toxicological impacts of white mustard-based biodiesel production and use are also discussed.

Hemp is a sustainable and environmental friendly crop that can provide valuable raw materials to a large number of industrial applications. Traditionally harvested at full flowering for textile destinations, nowadays hemp is mainly harvested at seed maturity for dual-purpose applications and has a great potential as multipurpose crop. However, the European hemp fiber market is stagnating if compared to the growing market of hemp seeds and phytocannabinoids. To support a sustainable growth of the hemp fiber market, agronomic techniques as well as genotypes and post-harvest processing should be optimized to preserve fiber quality during grain ripening, enabling industrial processing and maintaining, or even increasing, actual fiber applications and improving high-added value applications. In this paper, the effect of genotypes, harvest times, retting methods and processing on the yield and quality of long hemp for wet spun yarns was investigated. Conventional green-stem varieties were compared with yellow-stem ones on two harvesting times: at full flower and seed maturity. Scutching was performed on un-retted stems and dew-retted stems, the un-retted scutched fiber bundles were then bio-degummed before hackling. Both scutching and hackling was performed on flax machines. Quality of hackled hemp, with particular reference to its suitability for high performance composites production, was assessed. The results of fiber extraction indicate that yellow-stem varieties are characterized by higher scutching efficiency than green-stem varieties. Composites strength at breaking point, measured on specimens produced with the Impregnated Fiber Bundle Test, was lower with hemp obtained from stems harvested at seed maturity than at full flowering. On average, back-calculated fiber properties, from hackled hemp-epoxy composites, proved the suitability of long hemp fiber bundles for high performance composites applications, having properties comparable to those of high quality long flax. Highlights: - The trait yellow stem in hemp is an indicator of processability. - Yellow stem varieties have finer hackled fiber bundles. - Controlled dew retting increased yield of hackled fiber compared to bio-degumming. - Retting influenced fiber and composite mechanical properties. - Hemp can achieve properties comparable to high quality long flax for high performance composites.

Non-cellulosic polysaccharides constitute approximately one third of usable woody biomass for human exploitation. In contrast to cellulose, these substances are composed of several different types of unit monosaccharides and their backbones are substituted by various groups. Their structural diversity and recent examples of their modification in transgenic plants and mutants suggest they can be targeted for improving wood-processing properties, thereby facilitating conversion of wood in a biorefinery setting. Critical knowledge on their structure-function relationship is slowly emerging, although our understanding of molecular interactions responsible for observed phenomena is still incomplete. This review: (1) provides an overview of structural features of major non-cellulosic polysaccharides of wood, (2) describes the fate of non-cellulosic polysaccharides during biorefinery processing, (3) shows how the non-cellulosic polysaccharides impact lignocellulose processing focused on yields of either sugars or polymers, and (4) discusses outlooks for the improvement of tree species for biorefinery by modifying the structure of non-cellulosic polysaccharides.

Plant secondary metabolites (PSMs) are a key mechanism by which plants defend themselves against potential threats, and changes in the abiotic environment can alter the diversity and abundance of PSMs. While the number of studies investigating the effects of abiotic factors on PSM production is growing, we currently have a limited understanding of how combinations of factors may influence PSM production. The objective of this study was to determine how warming influences PSM production and how the addition of other factors may modulate this effect. We used untargeted metabolomics to evaluate how PSM production in five different woody plant species in northern Minnesota, USA are influenced by varying combinations of temperature, moisture, and light in both experimental and natural conditions. We also analysed changes to the abundances of two compounds from two different species – two resin acids in Abies balsamea and catechin and a terpene acid in Betula papyrifera. We used perMANOVA to compare PSM profiles and phytochemical turnover across treatments and NMDS to visualize treatment-specific changes in PSM profiles. We used linear mixed-effects models to examine changes in phytochemical richness and changes in the abundances of our example compounds. Under closed-canopy, experimental warming led to distinct PSM profiles and induced phytochemical turnover in B. papyrifera. In open-canopy sites, warming had no influence on PSM production. In samples collected across northeastern Minnesota, regional temperature differences had no influence on PSM profiles or phytochemical richness but did induce phytochemical turnover in B. papyrifera and Populus tremuloides. Throughout northeast Minnesota, warmer temperatures combined with open canopy resulted in distinct PSM profiles for all species and induced phytochemical turnover in all but Corylus cornuta. Although neither example compound in A. balsamea was influenced by any of the abiotic conditions, both compounds in B. papyrifera exhibited significant changes in response to warming and canopy. Our results demonstrate that the metabolic response of woody plants to combinations of abiotic factors cannot be extrapolated from that of a single factor. This heterogeneous phytochemical response directly affects interactions between plants and other organisms and may yield unexpected results as plant communities adapt to novel environmental conditions.

HIGHLIGHTS Biomass production and cell wall composition are differentially impacted by harvesting year and genotypes, influencing then cellulose conversion in miniaturized assay. Using a high-throughput miniaturized and semi-automated method for performing the pretreatment and saccharification steps at laboratory scale allows for the assessment of these factors on the biomass potential for producing bioethanol before moving to the industrial scale. The large genetic diversity of the perennial grass miscanthus makes it suitable for producing cellulosic ethanol in biorefineries. The saccharification potential and year variability of five genotypes belonging to Miscanthus × giganteus and Miscanthus sinensis were explored using a miniaturized and semi-automated method, allowing the application of a hot water treatment followed by an enzymatic hydrolysis. The studied genotypes highlighted distinct cellulose conversion yields due to their distinct cell wall compositions. An inter-year comparison revealed significant variations in the biomass productivity and cell wall compositions. Compared to the recalcitrant genotypes, more digestible genotypes contained higher amounts of hemicellulosic carbohydrates and lower amounts of cellulose and lignin. In contrast to hemicellulosic carbohydrates, the relationships analysis between the biomass traits and cellulose conversion clearly showed the same negative effect of cellulose and lignin on cellulose digestion. The miniaturized and semi-automated method we developed was usable at the laboratory scale and was reliable for mimicking the saccharification at the pilot scale using a steam explosion pretreatment and enzymatic hydrolysis. Therefore, this miniaturized method will allow the reliable screening of many genotypes for saccharification potential. These findings provide valuable information and tools for breeders to create genotypes combining high yield, suitable biomass composition, and high saccharification yields.

Lignin is a ubiquitous polymer present in cell walls of all vascular plants, where it rigidifies and strengthens the cell wall structure through covalent cross-linkages to cell wall polysaccharides. The presence of lignin makes the cell wall recalcitrant to conversion into fermentable sugars for bioenergy uses. Therefore, reducing lignin content and modifying its linkages have become major targets for bioenergy feedstock development through either biotechnology or traditional plant breeding. In addition, lignin synthesis has long been implicated as an important plant defense mechanism against pathogens, because lignin synthesis is often induced at the site of pathogen attack. This article explores the impact of lignin modifications on the susceptibility of a range of plant species to their associated pathogens, and the implications for development of feedstocks for the second-generation biofuels industry. Surprisingly, there are some instances where plants modified in lignin synthesis may display increased resistance to associated pathogens, which is explored in this article.

Fruit detection and recognition has an important impact on fruit and vegetable harvesting, yield prediction and growth information monitoring in the automation process of modern agriculture, and the actual complex environment of orchards poses some challenges for accurate fruit detection. In order to achieve accurate detection of green fruits in complex orchard environments, this paper proposes an accurate object detection method for green fruits based on optimized YOLOX_m. First, the model extracts features from the input image using the CSPDarkNet backbone network to obtain three effective feature layers at different scales. Then, these effective feature layers are fed into the feature fusion pyramid network for enhanced feature extraction, which combines feature information from different scales, and in this process, the Atrous spatial pyramid pooling (ASPP) module is used to increase the receptive field and enhance the network’s ability to obtain multi-scale contextual information. Finally, the fused features are fed into the head prediction network for classification prediction and regression prediction. In addition, Varifocal loss is used to mitigate the negative impact of unbalanced distribution of positive and negative samples to obtain higher precision. The experimental results show that the model in this paper has improved on both apple and persimmon datasets, with the average precision (AP) reaching 64.3% and 74.7%, respectively. Compared with other models commonly used for detection, the model approach in this study has a higher average precision and has improved in other performance metrics, which can provide a reference for the detection of other fruits and vegetables.

Drone monitoring plays an irreplaceable and significant role in forest firefighting due to its characteristics of wide-range observation and real-time messaging. However, aerial images are often susceptible to different degradation problems before performing high-level visual tasks including but not limited to smoke detection, fire classification, and regional localization. Recently, the majority of image enhancement methods are centered around particular types of degradation, necessitating the memory unit to accommodate different models for distinct scenarios in practical applications. Furthermore, such a paradigm requires wasted computational and storage resources to determine the type of degradation, making it difficult to meet the real-time and lightweight requirements of real-world scenarios. In this paper, we propose an All-in-one Image Enhancement Network (AIENet) that can restore various degraded images in one network. Specifically, we design a new multi-scale receptive field image enhancement block, which can better reconstruct high-resolution details of target regions of different sizes. In particular, this plug-and-play module enables it to be embedded in any learning-based model. And it has better flexibility and generalization in practical applications. This paper takes three challenging image enhancement tasks encountered in drone monitoring as examples, whereby we conduct task-specific and all-in-one image enhancement experiments on a synthetic forest dataset. The results show that the proposed AIENet outperforms the state-of-the-art image enhancement algorithms quantitatively and qualitatively. Furthermore, extra experiments on high-level vision detection also show the promising performance of our method compared with some recent baselines.

High concentrations of heavy metals in the environment will cause serious harm to ecosystems and human health. It is urgent to develop effective methods to control soil heavy metal pollution. Phytoremediation has advantages and potential for soil heavy metal pollution control. However, the current hyperaccumulators have the disadvantages of poor environmental adaptability, single enrichment species and small biomass. Based on the concept of modularity, synthetic biology makes it possible to design a wide range of organisms. In this paper, a comprehensive strategy of “microbial biosensor detection - phytoremediation - heavy metal recovery” for soil heavy metal pollution control was proposed, and the required steps were modified by using synthetic biology methods. This paper summarizes the new experimental methods that promote the discovery of synthetic biological elements and the construction of circuits, and combs the methods of producing transgenic plants to facilitate the transformation of constructed synthetic biological vectors. Finally, the problems that should be paid more attention to in the remediation of soil heavy metal pollution based on synthetic biology were discussed.

With the development and wider application of meta-heuristic optimization algorithms, researchers increasingly apply them to threshold optimization of multi-level image segmentation. This paper explores the performance and effects of Capuchin Search Algorithm (CAPSA) in threshold optimization. To solve problems of uneven distribution in the initial population of Capuchin Search Algorithm, low levels of global search performance and premature falling into local optima, this paper proposes an improved Capuchin Search Algorithm (ICAPSA) through a multi-strategy approach. ICAPSA uses chaotic opposite-based learning strategy to initialize the positions of individual capuchins, and improve the quality of the initial population. In the iterative position updating process, Levy Flight disturbance strategy is introduced to balance the global optimization and local exploitation of the algorithm. Finally, taking Kapur as the objective function, this paper applies ICAPSA to multi-level thresholding in the plant images, and compares its segmentation effects with the original CAPSA, the Fuzzy Artificial Bee Colony algorithm (FABC), the Differential Coyote Optimization Algorithm (DCOA), the Modified Whale Optimization Algorithm (MWOA) and Improved Satin Bowerbird Optimization Algorithm (ISBO). Through comparison, it is found that ICAPSA demonstrates superior segmentation effect, both in the visual effects of image segmentation and in data comparison.

IntroductionThe purpose of this paper is to effectively and accurately identify weed species in crop fields in complex environments. There are many kinds of weeds in the detection area, which are densely distributed.MethodsThe paper proposes the use of local variance pre-processing method for background segmentation and data enhancement, which effectively removes the complex background and redundant information from the data, and prevents the experiment from overfitting, which can improve the accuracy rate significantly. Then, based on the optimization improvement of DenseNet network, Efficient Channel Attention (ECA) mechanism is introduced after the convolutional layer to increase the weight of important features, strengthen the weed features and suppress the background features.ResultsUsing the processed images to train the model, the accuracy rate reaches 97.98%, which is a great improvement, and the comprehensive performance is higher than that of DenseNet, VGGNet-16, VGGNet-19, ResNet-50, DANet, DNANet, and U-Net models.DiscussionThe experimental data show that the model and method we designed are well suited to solve the problem of accurate identification of crop and weed species in complex environments, laying a solid technical foundation for the development of intelligent weeding robots.

IntroductionIn order to promote sugarcane pre-cut seed good seed and good method planting technology, we combine the development of sugarcane pre-cut seed intelligent 0p99oposeed cutting machine to realize the accurate and fast identification and cutting of sugarcane stem nodes.MethodsIn this paper, we proposed an algorithm to improve YOLOv4-Tiny for sugarcane stem node recognition. Based on the original YOLOv4-Tiny network, the three maximum pooling layers of the original YOLOv4-tiny network were replaced with SPP (Spatial Pyramid Pooling) modules, which fuse the local and global features of the images and enhance the accurate localization ability of the network. And a 1×1 convolution module was added to each feature layer to reduce the parameters of the network and improve the prediction speed of the network.ResultsOn the sugarcane dataset, compared with the Faster-RCNN algorithm and YOLOv4 algorithm, the improved algorithm yielded an mean accuracy precision (MAP) of 99.11%, a detection accuracy of 97.07%, and a transmission frame per second (fps) of 30, which can quickly and accurately detect and identify sugarcane stem nodes.DiscussionIn this paper, the improved algorithm is deployed in the sugarcane stem node fast identification and dynamic cutting system to achieve accurate and fast sugarcane stem node identification and cutting in real time. It improves the seed cutting quality and cutting efficiency and reduces the labor intensity.

Plants are exposed to increasingly severe drought events and roots play vital roles in maintaining plant survival, growth, and reproduction. A large body of literature has investigated the adaptive responses of root traits in various plants to water stress and these studies have been reviewed in certain groups of plant species at a certain scale. Nevertheless, these responses have not been synthesized at multiple levels. This paper screened over 2000 literatures for studies of typical root traits including root growth angle, root depth, root length, root diameter, root dry weight, root-to-shoot ratio, root hair length and density and integrates their drought responses at genetic and morphological scales. The genes, quantitative trait loci (QTLs) and hormones that are involved in the regulation of drought response of the root traits were summarized. We then statistically analyzed the drought responses of root traits and discussed the underlying mechanisms. Moreover, we highlighted the drought response of 1-D and 2-D root length density (RLD) distribution in the soil profile. This paper will provide a framework for an integrated understanding of root adaptive responses to water deficit at multiple scales and such insights may provide a basis for selection and breeding of drought tolerant crop lines.

In this paper we show that metabolic engineering in Cucurbita pepo hairy roots can be used to both effectively increase and modify cucurbitacins. Cucurbitacins are highly-oxygenated triterpenoids originally described in the Cucurbitaceae family, but have since been found in 15 taxonomically distant plant families. Cucurbitacin B, D, E and I are the most widespread amongst the Cucurbitaceae and they have both important biological and pharmacological activities. In this study C. pepo hairy roots were used as a platform to boost production and alter the structures of the afore mentioned cucurbitacins by metabolic engineering to potentially provide new or more desirable bioactivities. We report that the ability to induce cucurbitacin biosynthesis by basic Helix-Loop-Helix transcription factors is partially conserved within the Cucurbitaceae and therefore can potentially be used as a biotechnological tool to increase cucurbitacins in several genera of this family. Additionally, overexpression of a novel acyltransferase from cucurbitacin producing Iberis amara generates a hitherto undescribed acetylation at the C3-hydroxyl group of the cucurbitadienol backbone. While overexpression of the cytochromes P450 CsCYP88L2 and McCYP88L7 from Cucumis sativus and Momordica charantia (respectively), results in accumulation of new spectral feature as revealed by High resolution liquid chromatography mass spectroscopy analysis; the m/z of the new peak supports it might be a cucurbitacin hydroxylated at the C19 position in C. pepo hairy roots. Finally, this paper is a case study of how hairy roots can be used to metabolically engineer and introduce novel modifications in metabolic pathways that have not been fully elucidated.

Named Entity Recognition (NER) is a crucial step in mining information from massive agricultural texts, which is required in the construction of many knowledge-based agricultural support systems, such as agricultural technology question answering systems. The vital domain characteristics of Chinese agricultural text cause the Chinese NER (CNER) in kiwifruit diseases and pests to suffer from the insensitivity of common word segmentation tools to kiwifruit-related texts and the feature extraction capability of the sequence encoding layer being challenged. In order to alleviate the above problems, effectively mine information from kiwifruit-related texts to provide support for agricultural support systems such as agricultural question answering systems, this study constructed a novel Chinese agricultural NER (CANER) model KIWINER by statistics-based new word detection and two novel modules, AttSoftlexicon (Criss-cross attention-based Softlexicon) and PCAT (Parallel connection criss-cross attention), proposed in this paper. Specifically, new words were detected to improve the adaptability of word segmentation tools to kiwifruit-related texts, thereby constructing a kiwifruit lexicon. The AttSoftlexicon integrates word information into the model and makes full use of the word information with the help of Criss-cross attention network (CCNet). And the PCAT improves the feature extraction ability of sequence encoding layer through CCNet and parallel connection structure. The performance of KIWINER was evaluated on four datasets, namely KIWID (Self-annotated), Boson, ClueNER, and People’s Daily, which achieved optimal F1-scores of 88.94%, 85.13%, 80.52%, and 92.82%, respectively. Experimental results in many aspects illustrated that methods proposed in this paper can effectively improve the recognition effect of kiwifruit diseases and pests named entities, especially for diseases and pests with strong domain characteristics

Global climate change is expected to further increase the frequency and severity of extreme events, such as high temperature/heat waves as well as drought in the future. Thus, how plant responds to high temperature and drought has become a key research topic. In this study, we extracted data from Web of Science Core Collections database, and synthesized plant responses to high temperature and drought based on bibliometric methods using software of R and VOSviewer. The results showed that a stabilized increasing trend of the publications (1199 papers) was found during the period of 2008 to 2014, and then showed a rapid increase (2583 papers) from year 2015 to 2021. Secondly, the top five dominant research fields of plant responses to high temperature and drought were Plant Science, Agroforestry Science, Environmental Science, Biochemistry, and Molecular Biology, respectively. The largest amount of published article has been found in the Frontiers in Plant Science journal, which has the highest global total citations and H-index. We also found that the journal of Plant Physiology has the highest local citations. From the most cited papers and references, the most important research focus was the improvement of crop yield and vegetation stress resistance. Furthermore, “drought” has been the most prominent keyword over the last 14 years, and more attention has been paid to “climate change” over the last 5 years. Under future climate change, how to regulate growth and development of food crops subjected to high temperature and drought stress may become a hotspot, and increasing research is critical to provide more insights into plant responses to high temperature and drought by linking plant above-below ground components. To summarize, this research will contribute to a comprehensive understanding of the past, present, and future research on plant responses to high temperature and drought.

The taro harvesting process is affected by a complex system composed of particle mechanics system and multi-body dynamics system. The discrete element method(DEM) can effectively solve the nonlinear problem of the interaction between harvesting components and working materials. Therefore, the discrete element model of taro tiller plants is of great importance for taro harvesting. This paper proposes a simulation method to establish a discrete element flexible plant model and dynamic clamping and pulling process of taro tiller plant. Discrete Element models of taro corm and flexible tiller petiole and leaf were established using DEM method, and the discrete element flexible model of the taro plant was established. Taro clamping and pulling force testing platform was designed and built. The single factor and Plackett-Burman experiments were used to determine the simulation parameters and optimize the taro plant model by taking the correlation coefficient of clamping force and correlation coefficient of pulling force collected from the simulation and the bench experiment as the experiment index. The parameter calibration results of discrete element model of taro plant are as follows: petiole-petiole method/tangential contact stiffness was 8.15×109 N·m-3, and normal/tangential critical stress was 6.65×106 Pa. The contact stiffness of pseudostem- corm method was 1.22×109 N·m-3, the critical stress of normal/tangential was 1.18×105 Pa, and the energy of soil surface was 4.15×106J·m-3. When the pulling speed is 0.1, 0.2, 0.3, 0.4 and 0.5 m·s-1, the correlation coefficients between the simulation experiment and the bench experiment are 0.812, 0.850, 0.770, 0.697 and 0.652, respectively. The average value of correlation coefficient is 0.756, indicating that the simulated discrete element plant model is close to the real plant model. The discrete element model of taro plant established in this paper has high reliability. The final purpose of this paper is to provide a model reference for the design and optimization of taro harvester by discrete element method.

Latex-bearing plants have been in the research spotlight for the past couple of decades. Since ancient times their extracts have been used in folk medicine to treat various illnesses. Currently they serve as promising candidates for cancer treatment. Up to date there have been several in vitro and in vivo studies related to the topic of cytotoxicity and anticancer activity of extracts from latex-bearing plants towards various cell types. The number of clinical studies still remains scarce, however, over the years the number is systematically increasing. To the best of our knowledge, the scientific community is still lacking in a recent review summarizing the research on the topic of cytotoxicity and anticancer activity of latex-bearing plant extracts. Therefore, the aim of this paper is to review the current knowledge on in vitro and in vivo studies, which focus on the cytotoxicity and anticancer activities of latex-bearing plants. The vast majority of the studies are in vitro, however, the interest in this topic has resulted in the substantial growth of the number of in vivo studies, leading to a promising number of plant species whose latex can potentially be tested in clinical trials. The paper is divided into sections, each of them focuses on specific latex-bearing plant family representatives and their potential anticancer activity, which in some instances is comparable to that induced by commonly used therapeutics currently available on the market. The cytotoxic effect of the plant’s crude latex, its fractions or isolated compounds, is analyzed, along with a study of cell apoptosis, chromatin condensation, DNA damage, changes in gene regulation and morphology changes, which can be observed in cell post plant extract addition. The in vivo studies go beyond the molecular level by showing significant reduction of the tumor growth and volume in animal models. Additionally, we present data regarding plant-mediated biosynthesis of nanoparticles, which is regarded as a new branch in plant latex research. It is solely based on the green-synthesis approach, which presents an interesting alternative to chemical-based nanoparticle synthesis. We have analyzed the cytotoxic effect of these particles on cells. Data regarding the cytotoxicity of such particles raises their potential to be involved in the design of novel cancer therapies, which further underlines the significance of latex-bearing plants in biotechnology. Throughout the course of this review, we concluded that plant latex is a rich source of many compounds, which can be further investigated and applied in the design of anticancer pharmaceuticals. The molecules, to which this cytotoxic effect can be attributed, include alkaloids, flavonoids, tannins, terpenoids, proteases, nucleases and many novel compounds, which still remain to be characterized. They have been studied extensively in both in vitro and in vivo studies, which provide an excellent starting point for their rapid transfer to clinical studies in the near future. The comprehensive study of molecules from latex-bearing plants can result in finding a promising alternative to several pharmaceuticals on the market and help unravel the molecular mode of action of latex-based preparations.

Flavonoids from Actinidia arguta Sieb. Zucc. can reduce uric acid in mice. However, the molecular basis of its biosynthesis is still unclear. In this paper, we used a combination of extensively targeted metabolomics and transcriptomics analysis to determine the types and differences of flavonoids in the fruit ripening period (August to September) of two main cultivated varieties in northern China. The ethanol extract was prepared, and the potential flavonoids of Chrysin (Flavone1), Rutin (Flavone2), and Daidzein (Flavone3) in Actinidia arguta Sieb. Zucc. were separated and purified by HPD600 macroporous adsorption resin and preparative liquid chromatography. The structure was identified by MS-HPLC, and the serum uric acid index of male Kunming mice was determined by an animal model test.125 flavonoids and 50 differentially regulated genes were identified. The contents of UA (uric acid), BUN (urea nitrogen), Cr (creatinine), and GAPDH in mouse serum and mouse liver glycogen decreased or increased in varying degrees. This paper reveals the biosynthetic pathway of uric acid-reducing flavonoids in the fruit of Actinidia arguta Sieb. Zucc., a major cultivar in northern China, provides valuable information for the development of food and drug homologous functional foods.

Texture synthesis technology is an important realistic rendering technology. Texture synthesis technology also has a good application prospect in image rendering and other fields. Convolutional neural network is a very popular technology in recent years. Convolutional neural network model can learn the features in data and realize intelligent processing through the feature learning in data. Later, with the rapid improvement of convolutional neural network, texture synthesis technology based on neural network came into being. The purpose of this paper is to study the texture synthesis method of ecological plant protection image based on convolutional neural network. By studying the context and research implications, the definition of textures as well as texture synthesis methods, convolutional neural networks, and based on convolutional neural network. In the experiment, the experimental environment is established, and the subjective evaluation and objective evaluation of the image texture synthesis method experiment are investigated and studied by using swap algorithm. The experimental results show that the method used in this paper is superior to other methods.

Guvermectin is a recently discovered microbial N9-glucoside cytokinin compound extracted from Streptomyces sanjiangensis NEAU6. Although some research has reported that N9-glucoside cytokinin compounds do not have the activity of cytokinin, it has been noted that guvermectin can promote growth and antifungal activity in Arabidopsis. Maize is an important food crop in the world and exploring the effect of guvermectin on this crop could help its cultivation in regions with adverse environmental conditions such as a high temperature. Here, we investigated the effects of guvermectin seed soaking treatment on the growth of maize at the seedlings stage and its yield attributes with different temperature stresses. The maize (cv. Zhengdan 958) with guvermectin seed soaking treatment were in two systems: paper roll culture and field conditions. Guvermectin seed soaking treated plants had increased plant height, root length, and mesocotyl length at the seedlings stage, and spike weight at maturity in the field. But only root length was increased at the paper roll culture by guvermectin seed soaking treatment. Guvermectin seed soaking treatment reduced the adverse effects on maize seedling when grow at a high temperature. Further experiments showed that, in high temperature conditions, guvermectin treatment promoted the accumulation of heat shock protein (HSP) 17.0, HSP 17.4 and HSP 17.9 in maize roots. Comparative transcriptomic profiling showed there were 33 common differentially expressed genes (DEGs) in guvermectin treated plants under high temperature and room temperature conditions. The DEGs suggested that guvermectin treatment led to the differential modulation of several transcripts mainly related with plant defense, stress response, and terpenoid biosynthesis. Taken together, these results suggested that the guvermectin treatment promoted the growth and tolerance of high temperature stresses, possibly by activation of related pathways. These results show that guvermectin is a novel plant growth regulator and could be developed as an application to maize seeds to promote growth in high temperature environments.

Fruit tree diseases are one of the major agricultural disasters in China. With the popularity of smartphones, there is a trend to use mobile devices to identify agricultural pests and diseases. In order to identify leaf diseases of apples more easily and efficiently, this paper proposes a cascade backbone network-based (CBNet) disease identification method to detect leaf diseases of apple trees in the field. The method first replaces traditional convolutional blocks with MobileViT-based convolutional blocks particularly for feature extraction. Compared with the traditional convolutional block, the MobileViT-based convolutional block is able to mine feature information in the image better. In order to refine the mined feature information, a feature refinement module is proposed in this paper. At the same time, this paper proposes a cascaded backbone network for effective fusion of features using a pyramidal cascaded multiplication operation. The results conducted on field datasets collected using mobile devices showed that the network proposed in this paper can achieve 96.76% accuracy and 96.71% F1-score. To the best of our knowledge, this paper is the first to introduce Transformer into apple leaf disease identification, and the results are promising.

The complex environments and weak infrastructure constructions of hilly mountainous areas complicate the effective path planning for plant protection operations. Therefore, with the aim of improving the current status of complicated tea plant protections in hills and slopes, an unmanned aerial vehicle (UAV) multi-tea field plant protection route planning algorithm is developed in this paper and integrated with a full-coverage spraying route method for a single region. By optimizing the crossover and mutation operators of the genetic algorithm (GA), the crossover and mutation probabilities are automatically adjusted with the individual fitness and a dynamic genetic algorithm (DGA) is proposed. The iteration period and reinforcement concepts are then introduced in the pheromone update rule of the ant colony optimization (ACO) to improve the convergence accuracy and global optimization capability, and an ant colony binary iteration optimization (ACBIO) is proposed. Serial fusion is subsequently employed on the two algorithms to optimize the route planning for multi-regional operations. Simulation tests reveal that the dynamic genetic algorithm with ant colony binary iterative optimization (DGA-ACBIO) proposed in this study shortens the optimal flight range by 715.8 m, 428.3 m, 589 m, and 287.6 m compared to the dynamic genetic algorithm, ant colony binary iterative algorithm, artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO), respectively, for multiple tea field scheduling route planning. Moreover, the search time is reduced by more than half compared to other bionic algorithms. The proposed algorithm maintains advantages in performance and stability when solving standard traveling salesman problems with more complex objectives, as well as the planning accuracy and search speed. In this paper, the research on the planning algorithm of plant protection route for multi-tea field scheduling helps to shorten the inter-regional scheduling range and thus reduces the cost of plant protection.

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life.

The fungal genus Colletotrichum includes plant pathogens that cause substantial economic damage to horticultural, ornamental, and fruit tree crops worldwide. Here, we conducted a systematic literature review to retrieve and analyze the metadata on the influence of temperature on four biological processes: (i) mycelial growth, (ii) conidial germination, (iii) infection by conidia, and (iv) sporulation. The literature review considered 118 papers (selected from a total of 1,641 papers found with the literature search), 19 Colletotrichum species belonging to eight clades (acutatum, graminicola, destructivum, coccodes, dematium, gloeosporioides, and orbiculare), and 27 host plants (alfalfa, almond, apple, azalea, banana, barley, bathurst burr, blueberry, celery, chilli, coffee, corn, cotton, cowpea, grape, guava, jointvetch, lentil, lupin, olive, onion, snap bean, spinach, strawberry, tomato, watermelon, and white bean). We used the metadata to develop temperature-dependent equations representing the effect of temperature on the biological processes for the different clades and species. Inter- and intra-clades similarities and differences are analyzed and discussed. A multi-factor cluster analysis identified four groups of clades with similar temperature dependencies. The results should facilitate further research on the biology and epidemiology of Colletotrichum species and should also contribute to the development of models for the management of anthracnose diseases.

Aiming at the problem that it is difficult to identify two types of weeds, grass weeds and broadleaf weeds, in complex field environments, this paper proposes a semantic segmentation method with an improved UNet structure and an embedded channel attention mechanism SE module. First, to eliminate the semantic gap between low-dimensional semantic features and high-dimensional semantic features, the UNet model structure is modified according to the characteristics of different types of weeds, and the feature maps after the first five down sampling tasks are restored to the same original image through the deconvolution layer. Hence, the final feature map used for prediction is obtained by the fusion of the upsampling feature map and the feature maps containing more low-dimensional semantic information in the first five layers. In addition, ResNet34 is used as the backbone network, and the channel attention mechanism SE module is embedded to improve useful features. The channel weight is determined, noise is suppressed, soybean and grass weeds are identified, and broadleaf weeds are extracted through digital image morphological processing, and segmented images of soybean plants, grass weeds and broadleaf weeds are generated. Moreover, compared with the standard semantic segmentation models, FCN, UNet, and SegNet, the experimental results show that the overall performance of the model in this paper is the best. The average intersection ratio and average pixel recognition rate in a complex field environment are 0.9282 and 96.11%, respectively. On the basis of weed classification, the identified weeds are further refined into two types of weeds to provide technical support for intelligent precision variable weed spraying.

Peach diseases seriously affect peach yield and people’s health. The precise identification of peach diseases and the segmentation of the diseased areas can provide the basis for disease control and treatment. However, the complex background and imbalanced samples bring certain challenges to the segmentation and recognition of lesion area, and the hard samples and imbalance samples can lead to a decline in classification of foreground class and background class. In this paper we applied deep network models (Mask R-CNN and Mask Scoring R-CNN) for segmentation and recognition of peach diseases. Mask R-CNN and Mask Scoring R-CNN are classic instance segmentation models. Using instance segmentation model can obtain the disease names, disease location and disease segmentation, and the foreground area is the basic feature for next segmentation. Focal Loss can solve the problems caused by difficult samples and imbalance samples, and was used for this dataset to improve segmentation accuracy. Experimental results show that Mask Scoring R-CNN with Focal Loss function can improve recognition rate and segmentation accuracy comparing to Mask Scoring R-CNN with CE loss or comparing to Mask R-CNN. When ResNet50 is used as the backbone network based on Mask R-CNN, the segmentation accuracy of segm_mAP_50 increased from 0.236 to 0.254. When ResNetx101 is used as the backbone network, the segmentation accuracy of segm_mAP_50 increased from 0.452 to 0.463. In summary, this paper used Focal Loss on Mask R-CNN and Mask Scoring R-CNN to generate better mAP of segmentation and output more detailed information about peach diseases.

The precision detection of dense small targets in orchards is critical for the visual perception of agricultural picking robots. At present, the visual detection algorithms for plums still have a poor recognition effect due to the characteristics of small plum shapes and dense growth. Thus, this paper proposed a lightweight model based on the improved You Only Look Once version 4 (YOLOv4) to detect dense plums in orchards. First, we employed a data augmentation method based on category balance to alleviate the imbalance in the number of plums of different maturity levels and insufficient data quantity. Second, we abandoned Center and Scale Prediction Darknet53 (CSPDarknet53) and chose a lighter MobilenetV3 on selecting backbone feature extraction networks. In the feature fusion stage, we used depthwise separable convolution (DSC) instead of standard convolution to achieve the purpose of reducing model parameters. To solve the insufficient feature extraction problem of dense targets, this model achieved fine-grained detection by introducing a 152 × 152 feature layer. The Focal loss and complete intersection over union (CIOU) loss were joined to balance the contribution of hard-to-classify and easy-to-classify samples to the total loss. Then, the improved model was trained through transfer learning at different stages. Finally, several groups of detection experiments were designed to evaluate the performance of the improved model. The results showed that the improved YOLOv4 model had the best mean average precision (mAP) performance than YOLOv4, YOLOv4-tiny, and MobileNet-Single Shot Multibox Detector (MobileNet-SSD). Compared with some results from the YOLOv4 model, the model size of the improved model is compressed by 77.85%, the parameters are only 17.92% of the original model parameters, and the detection speed is accelerated by 112%. In addition, the influence of the automatic data balance algorithm on the accuracy of the model and the detection effect of the improved model under different illumination angles, different intensity levels, and different types of occlusions were discussed in this paper. It is indicated that the improved detection model has strong robustness and high accuracy under the real natural environment, which can provide data reference for the subsequent orchard yield estimation and engineering applications of robot picking work.

High throughput image-based plant phenotyping facilitates the extraction of morphological and biophysical traits of a large number of plants non-invasively in a relatively short time. It facilitates the computation of advanced phenotypes by considering the plant as a single object (holistic phenotypes) or its components, i.e., leaves and the stem (component phenotypes). The architectural complexity of plants increases over time due to variations in self-occlusions and phyllotaxy, i.e., arrangements of leaves around the stem. One of the central challenges to computing phenotypes from 2-dimensional (2D) single view images of plants, especially at the advanced vegetative stage in presence of self-occluding leaves, is that the information captured in 2D images is incomplete, and hence, the computed phenotypes are inaccurate. We introduce a novel algorithm to compute 3-dimensional (3D) plant phenotypes from multiview images using voxel-grid reconstruction of the plant (3DPhenoMV). The paper also presents a novel method to reliably detect and separate the individual leaves and the stem from the 3D voxel-grid of the plant using voxel overlapping consistency check and point cloud clustering techniques. To evaluate the performance of the proposed algorithm, we introduce the University of Nebraska-Lincoln 3D Plant Phenotyping Dataset (UNL-3DPPD). A generic taxonomy of 3D image-based plant phenotypes are also presented to promote 3D plant phenotyping research. A subset of these phenotypes are computed using computer vision algorithms with discussion of their significance in the context of plant science. The central contributions of the paper are (a) an algorithm for 3D voxel-grid reconstruction of maize plants at the advanced vegetative stages using images from multiple 2D views; (b) a generic taxonomy of 3D image-based plant phenotypes and a public benchmark dataset, i.e., UNL-3DPPD, to promote the development of 3D image-based plant phenotyping research; and (c) novel voxel overlapping consistency check and point cloud clustering techniques to detect and isolate individual leaves and stem of the maize plants to compute the component phenotypes. Detailed experimental analyses demonstrate the efficacy of the proposed method, and also show the potential of 3D phenotypes to explain the morphological characteristics of plants regulated by genetic and environmental interactions.

The use of RNA interference (RNAi) enables the silencing of target genes in plants or plant-dwelling organisms, through the production of double stranded RNA (dsRNA) resulting in altered plant characteristics. Expression of properly synthesized dsRNAs in plants can lead to improved crop quality characteristics or exploit new mechanisms with activity against plant pests and pathogens. Genetically modified (GM) crops exhibiting resistance to viruses or insects via expression of dsRNA have received authorization for cultivation outside Europe. Some products derived from RNAi plants have received a favourable opinion from the European Food Safety Authority (EFSA) for import and processing in the European Union (EU). The authorization process in the EU requires applicants to produce a risk assessment considering food/feed and environmental safety aspects of living organisms or their derived food and feed products. The present paper discusses the main aspects of the safety assessment (comparative assessment, molecular characterization, toxicological assessment, nutritional assessment, gene transfer, interaction with target and non-target organisms) for GM plants expressing dsRNA, according to the guidelines of EFSA. Food/feed safety assessment of products from RNAi plants is expected to be simplified, in the light of the consideration that no novel proteins are produced. Therefore, some of the data requirements for risk assessment do not apply to these cases, and the comparative compositional analysis becomes the main source of evidence for food/feed safety of RNAi plants. During environmental risk assessment, the analysis of dsRNA expression levels of the GM trait, and the data concerning the observable effects on non-target organisms (NTO) will provide the necessary evidence for ensuring safety of species exposed to RNAi plants. Bioinformatics may provide support to risk assessment by selecting target gene sequences with low similarity to the genome of NTOs possibly exposed to dsRNA. The analysis of these topics in risk assessment indicates that the science-based regulatory process in Europe is considered to be applicable to GM RNAi plants, therefore the evaluation of their safety can be effectively conducted without further modifications. Outcomes from the present paper offer suggestions for consideration in future updates of the EFSA Guidance documents on risk assessment of GM organisms.

Cultivated for the crispy petioles and round, fleshy, and flavored hypocotyl celery and celeriac have over two centuries of breeding history in Europe. In this review paper we summarized the most recent advances touching when necessary the historical context of celery and celeriac breeding. In the post genomic era of research, the genome sequence of celery is only partially available. We comprised however in this paper the most important aspects of celery genetics that are available today and have applicability in celery modern cultivars development. We discussed the problems and traits that drive the main celery and celeriac breeding goals, like hybrid seed production, disease resistance, and interesting enlarged hypocotyl and petiole characteristics. Besides the classical breeding traits we covered the potential of integration of existing cultivars as sources for consumer oriented traits like nutraceuticals and health promoting substances. Sustainability is a subject that is continuously growing in popularity and we looked at the genetic base of celery and celeriac that makes them sources for abiotic stress resistance and candidates for phytoremediation. We explored the fundamental concepts gained in various fields of celery and related species research, as resources for future improvement of celery and celeriac germplasm. We forecast what the next years will bring to Apium breeding.

Enhanced grain yield has been achieved in bread wheat (Triticum aestivum L.) through development and cultivation of superior genotypes incorporating yield-related agronomic and physiological traits derived from genetically diverse and complementary genetic pool. Despite significant breeding progress, yield levels in wheat have remained relatively low and stagnant under marginal growing environments. There is a need for genetic improvement of wheat using yield-promoting morpho-physiological attributes and desired genotypes under the target production environments to meet the demand for food and feed. This review presents breeding progress in wheat for yield gains using agronomic and physiological traits. Further, the paper discusses globally available wheat genetic resources to identify and select promising genotypes possessing useful agronomic and physiological traits to enhance water, nutrient-, and radiation-use efficiency to improve grain yield potential and tolerance to abiotic stresses (i.e. elevated CO2, high temperature, and drought stresses). Finally, the paper highlights quantitative trait loci (QTL) linked to agronomic and physiological traits to aid breeding of high-performing wheat genotypes.

Arabidopsis is used as a model species in numerous physiological and genetic studies. Most of them employ parafilm-wrapped sterile culture. Here we demonstrate that this method is prone to potential artifacts and can lead to erroneous conclusions. We compared the effect of different sealing methods including air-permeable paper tape and traditional parafilm on Arabidopsis seedling growth, root development and gene expression network. Although seedlings grown in Petri dishes after 1 week sealed with paper tape showed a similar growth phenotype to that of parafilm-sealed seedlings, more than 700 differentially expressed genes (DEG) were found, including stress and nutrition-responsive genes. In addition, more H2O2 was accumulated in the tissues of parafilm-sealed plants. After 14 days of growth, paper tape-sealed plants grew much better than parafilm-sealed ones and accumulated higher chlorophyll content, with 490 DEGs found. After 3 weeks of growth, paper tape-sealed plants had higher chlorophyll and better growth compared to parafilm-sealed ones; and only 10 DEGs were found at this stage. Thus, the obvious phenotype observed at the latter stage was a result of differential gene expression at earlier time points, mostly of defense, abiotic stress, nutrition, and phytohormone-responsive genes. More O2 content was detected inside paper tape-sealed Petri dishes at early growth stage (7 days), and distinct difference in the CO2 content was observed between parafilm-sealed and paper tape-sealed Petri dishes. Furthermore, the carbon source also influenced seedlings growth with different sealing methods. In conclusion, conventional sealing using parafilm was not the optimal choice, most likely because of the limited gas exchange and a consequent stress caused to plants.

Elevated atmospheric CO2 can stimulate plant growth by providing additional C (fertilization effect), and is observed to mitigate abiotic stress impact. Although, the mechanisms underlying the stress mitigating effect are not yet clear, increased antioxidant defenses, have been held primarily responsible (antioxidant hypothesis). A systematic literature analysis, including all papers [Web of Science (WoS)-cited], addressing elevated CO2 effects on abiotic stress responses and antioxidants (105 papers), confirms the frequent occurrence of the stress mitigation effect. However, it also demonstrates that, in stress conditions, elevated CO2 is reported to increase antioxidants, only in about 22% of the observations (e.g., for polyphenols, peroxidases, superoxide dismutase, monodehydroascorbate reductase). In most observations, under stress and elevated CO2 the levels of key antioxidants and antioxidant enzymes are reported to remain unchanged (50%, e.g., ascorbate peroxidase, catalase, ascorbate), or even decreased (28%, e.g., glutathione peroxidase). Moreover, increases in antioxidants are not specific for a species group, growth facility, or stress type. It seems therefore unlikely that increased antioxidant defense is the major mechanism underlying CO2-mediated stress impact mitigation. Alternative processes, probably decreasing the oxidative challenge by reducing ROS production (e.g., photorespiration), are therefore likely to play important roles in elevated CO2 (relaxation hypothesis). Such parameters are however rarely investigated in connection with abiotic stress relief. Understanding the effect of elevated CO2 on plant growth and stress responses is imperative to understand the impact of climate changes on plant productivity.

Our recent paper (Barry et al., 2015) sought to understand factors limiting the growth of Eucalyptus obliqua seedlings in temperate native forest regeneration. A commentary from Neyland and Grove (2015) followed, that stated that while the main focus of our paper was not contentious, they disagreed with our concluding “Forest Management Implications” section. This provides an opportunity to expand and clarify some points in our original article. In disagreeing, Neyland and Grove (2015) cited several empirical studies that have shown “… the fundamental importance of burnt soil as part of the regeneration cycle … ”. We do not dispute those studies. Instead, the intent of our work was to move toward a mechanistic understanding of biological processes. That is, why do seedlings fail to establish on unburnt litter? We aimed to discern factors—particularly mineral nutrition and soil–fungi interactions—related to eucalypt establishment that might help to explain which components of fire regeneration are critical and which are not. We used a pot-based study to attempt to examine these factors in isolation. Using pot-trial results as a basis to extrapolate to forest ecological processes is well-established in a large volume of scientific literature. Neyland and Grove (2015) state that we confounded the term stocking with seedling density when interpreting the results of Neyland et al. (2009). The section of Neyland et al. (2009) on which we based our comments stated “Of the coupes burnt at lower intensities, only WR1B, which had a very poor burn but a very high rate of natural seedfall, achieved a commercially acceptable seedling density.” While the density was lower than those coupes with high intensity burns, the seedling density did reach the “desired commercial minimum” stated as 2500 stems ha−1, by year 3 (Neyland et al., 2009). We interpreted this result as showing that in spite of its poor burn, under some suite of conditions which prevailed in coupe WR1B, seedling density could reach the “levels considered necessary for future development of a productive regrowth eucalypt forest,” not that it always would do so. We agree that the results of this one coupe alone are not sufficient to suggest that non-burn alternatives will be commercially viable, however it does provide a biological basis for further study. Neyland and Grove (2015) expressed aversion to our suggestion of removal of woody debris in order to control competing vegetation, which is likely inhibitory to regenerating eucalypts. They used the acronym “CWD” which refers to coarse woody debris. We did not suggest removal of CWD or all woody debris. Our mention of “repeated re-clearing” meant clearing (cutting) of living material to reduce competition, in the same way that “clearfell” is the process of harvesting, which is followed by debris management. We submit that mechanical methods to remove debris could be trialed and designed for retention of the same piece size-distribution that occurs with best-practice burning for habitat conservation and also to ensure that the full suite of understory plant species return. Whether, re-clearing to minimize competition could be managed practically and economically is a separate issue from whether it is a biologically feasible alternative to burning that might achieve similar regeneration outcomes. Investigating alternatives to burning (even those likely to be unprofitable) in field trials would help expand understanding of the biological limits to eucalypt seedling growth and might reveal alternatives that are both environmentally and economically sustainable. Alternatives to burning are being trialed for fuel reduction management, and a recent announcement by the Australian federal government of a funded program to explore new forest fire fuel reduction methods has been supported by the (Australian Forest Productions Association (AFPA), 2015). This trial, which will examine bushfire prevention through mechanical fuel removal across Victoria, is based on studies in California that have shown “mechanical methods are not causing ecological harm… they're actually doing some real ecological work and sometimes doing things economically” (Grindley, 2015). It will demonstrate to what degree the Californian experience is applicable to southern Australia and the trials will have relevance to native forest harvesting. There is significant public interest in ensuring best practice forest management is undertaken in Tasmania, such that the Regional Forest Agreement (Commonwealth of Australia State of Tasmania, 1997) led to programs seeking alternatives to clearfell, burn and sow (CBS). CBS silviculture involves extreme mechanical disturbance, and is predicted to lead to species losses of forest flora and fauna (Baker and Read, 2011). While variable retention was investigated as an alternative to clearfell (Forestry Tasmania, 2009), alternatives to burning have not been investigated, despite emerging understanding of potential public health risks associated with smoke exposure (Henderson and Johnston, 2012; Johnston et al., 2012). Reduction of post-logging burning would reduce greenhouse gas emissions and enable some woody residues to be used for energy production (Bradshaw et al., 2013). Past plans in Tasmania to produce electricity from wood waste were prevented because residues were not included in Australia's Renewable Energy Target (RET). However, residues from native forests harvested for high-value solid timber are now accepted as biomass fuels under the revised RET (Brown and Coote, 2015). A pertinent model exists in Sweden, where about one-quarter of domestic energy production is from forest-based bioenergy and the ash thereby produced is returned to harvested sites to help restore the mineral nutrient balance (Levin and Eriksson, 2010). The potential for use of renewable forest biomass for energy in Tasmania was favorably assessed by Rothe (2013). While we acknowledge the research on eucalypt silviculture conducted by the forest industry to improve biodiversity conservation and sustainable outcomes, we hope to stimulate further investigations of eucalypt regeneration biology so that alternative silvicultural practices can be explored in field trials in the near future. We contend that the biological limitations of eucalypt regeneration should be understood independently of economic viability of alternatives, even though the latter will determine forestry practice.

Wild rice (Zizania spp.), an aquatic grass belonging to the subfamily Gramineae, has a high economic value. Zizania provides food (such as grains and vegetables), a habitat for wild animals, and paper-making pulps, possesses certain medicinal values, and helps control water eutrophication. Zizania is an ideal resource for expanding and enriching a rice breeding gene bank to naturally preserve valuable characteristics lost during domestication. With the Z. latifolia and Z. palustris genomes completely sequenced, fundamental achievements have been made toward understanding the origin and domestication, as well as the genetic basis of important agronomic traits of this genus, substantially accelerating the domestication of this wild plant. The present review summarizes the research results on the edible history, economic value, domestication, breeding, omics research, and important genes of Z. latifolia and Z. palustris over the past decades. These findings broaden the collective understanding of Zizania domestication and breeding, furthering human domestication, improvement, and long-term sustainability of wild plant cultivation.

Plants are a rich source of bioactive compounds and a number of plant-derived antiplasmodial compounds have been developed into pharmaceutical drugs for the prevention and treatment of malaria, a major public health challenge. However, identifying plants with antiplasmodial potential can be time-consuming and costly. One approach for selecting plants to investigate is based on ethnobotanical knowledge which, though having provided some major successes, is restricted to a relatively small group of plant species. Machine learning, incorporating ethnobotanical and plant trait data, provides a promising approach to improve the identification of antiplasmodial plants and accelerate the search for new plant-derived antiplasmodial compounds. In this paper we present a novel dataset on antiplasmodial activity for three flowering plant families – Apocynaceae, Loganiaceae and Rubiaceae (together comprising c. 21,100 species) – and demonstrate the ability of machine learning algorithms to predict the antiplasmodial potential of plant species. We evaluate the predictive capability of a variety of algorithms – Support Vector Machines, Logistic Regression, Gradient Boosted Trees and Bayesian Neural Networks – and compare these to two ethnobotanical selection approaches – based on usage as an antimalarial and general usage as a medicine. We evaluate the approaches using the given data and when the given samples are reweighted to correct for sampling biases. In both evaluation settings each of the machine learning models have a higher precision than the ethnobotanical approaches. In the bias-corrected scenario, the Support Vector classifier performs best – attaining a mean precision of 0.67 compared to the best performing ethnobotanical approach with a mean precision of 0.46. We also use the bias correction method and the Support Vector classifier to estimate the potential of plants to provide novel antiplasmodial compounds. We estimate that 7677 species in Apocynaceae, Loganiaceae and Rubiaceae warrant further investigation and that at least 1300 active antiplasmodial species are highly unlikely to be investigated by conventional approaches. While traditional and Indigenous knowledge remains vital to our understanding of people-plant relationships and an invaluable source of information, these results indicate a vast and relatively untapped source in the search for new plant-derived antiplasmodial compounds.

Cucumber belongs to the family Cucurbitaceae (melon genus) and is an annual herbaceous vegetable crop. Cucumber is an important cash crop that is grown all over the world. From morphology to cytology, from canonical genetics to molecular biology, researchers have performed much research on sex differentiation and its regulatory mechanism in cucumber, mainly in terms of cucumber sex determination genes, environmental conditions, and the effects of plant hormones, revealing its genetic basis to improve the number of female flowers in cucumber, thus greatly improving the yield of cucumber. This paper reviews the research progress of sex differentiation in cucumber in recent years, mainly focusing on sex-determining genes, environmental conditions, and the influence of phytohormones in cucumber, and provides a theoretical basis and technical support for the realization of high and stable yield cultivation and molecular breeding of cucumber crop traits.

Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds.

The content of nicotine, a critical component of tobacco, significantly influences the quality of tobacco leaves. Near-infrared (NIR) spectroscopy is a widely used technique for rapid, non-destructive, and environmentally friendly analysis of nicotine levels in tobacco. In this paper, we propose a novel regression model, Lightweight one-dimensional convolutional neural network (1D-CNN), for predicting nicotine content in tobacco leaves using one-dimensional (1D) NIR spectral data and a deep learning approach with convolutional neural network (CNN). This study employed Savitzky–Golay (SG) smoothing to preprocess NIR spectra and randomly generate representative training and test datasets. Batch normalization was used in network regularization to reduce overfitting and improve the generalization performance of the Lightweight 1D-CNN model under a limited training dataset. The network structure of this CNN model consists of four convolutional layers to extract high-level features from the input data. The output of these layers is then fed into a fully connected layer, which uses a linear activation function to output the predicted numerical value of nicotine. After the comparison of the performance of multiple regression models, including support vector regression (SVR), partial least squares regression (PLSR), 1D-CNN, and Lightweight 1D-CNN, under the preprocessing method of SG smoothing, we found that the Lightweight 1D-CNN regression model with batch normalization achieved root mean square error (RMSE) of 0.14, coefficient of determination (R2) of 0.95, and residual prediction deviation (RPD) of 5.09. These results demonstrate that the Lightweight 1D-CNN model is objective and robust and outperforms existing methods in terms of accuracy, which has the potential to significantly improve quality control processes in the tobacco industry by accurately and rapidly analyzing the nicotine content.

The potential yield of maize (Zea mays L.) and other major crops is curtailed by several biotic, abiotic, and socio-economic constraints. Parasitic weeds, Striga spp., are major constraints to cereal and legume crop production in sub-Saharan Africa (SSA). Yield losses reaching 100% are reported in maize under severe Striga infestation. Breeding for Striga resistance has been shown to be the most economical, feasible, and sustainable approach for resource-poor farmers and for being environmentally friendly. Knowledge of the genetic and genomic resources and components of Striga resistance is vital to guide genetic analysis and precision breeding of maize varieties with desirable product profiles under Striga infestation. This review aims to present the genetic and genomic resources, research progress, and opportunities in the genetic analysis of Striga resistance and yield components in maize for breeding. The paper outlines the vital genetic resources of maize for Striga resistance, including landraces, wild relatives, mutants, and synthetic varieties, followed by breeding technologies and genomic resources. Integrating conventional breeding, mutation breeding, and genomic-assisted breeding [i.e., marker-assisted selection, quantitative trait loci (QTL) analysis, next-generation sequencing, and genome editing] will enhance genetic gains in Striga resistance breeding programs. This review may guide new variety designs for Striga-resistance and desirable product profiles in maize.

Root phenotypic parameters are the important basis for studying the growth state of plants, and root researchers obtain root phenotypic parameters mainly by analyzing root images. With the development of image processing technology, automatic analysis of root phenotypic parameters has become possible. And the automatic segmentation of roots in images is the basis for the automatic analysis of root phenotypic parameters. We collected high-resolution images of cotton roots in a real soil environment using minirhizotrons. The background noise of the minirhizotron images is extremely complex and affects the accuracy of the automatic segmentation of the roots. In order to reduce the influence of the background noise, we improved OCRNet by adding a Global Attention Mechanism (GAM) module to OCRNet to enhance the focus of the model on the root targets. The improved OCRNet model in this paper achieved automatic segmentation of roots in the soil and performed well in the root segmentation of the high-resolution minirhizotron images, achieving an accuracy of 0.9866, a recall of 0.9419, a precision of 0.8887, an F1 score of 0.9146 and an Intersection over Union (IoU) of 0.8426. The method provided a new approach to automatic and accurate root segmentation of high-resolution minirhizotron images.

The wine sector faces important challenges related to sustainability issues and the impact of climate change. More frequent extreme climate conditions (high temperatures coupled with severe drought periods) have become a matter of concern for the wine sector of typically dry and warm regions, such as the Mediterranean European countries. Soil is a natural resource crucial to sustaining the equilibrium of ecosystems, economic growth and people’s prosperity worldwide. In viticulture, soils have a great influence on crop performance (growth, yield and berry composition) and wine quality, as the soil is a central component of the terroir. Soil temperature (ST) affects multiple physical, chemical and biological processes occurring in the soil as well as in plants growing on it. Moreover, the impact of ST is stronger in row crops such as grapevine, since it favors soil exposition to radiation and favors evapotranspiration. The role of ST on crop performance remains poorly described, especially under more extreme climatic conditions. Therefore, a better understanding of the impact of ST in vineyards (vine plants, weeds, microbiota) can help to better manage and predict vineyards’ performance, plant-soil relations and soil microbiome under more extreme climate conditions. In addition, soil and plant thermal data can be integrated into Decision Support Systems (DSS) to support vineyard management. In this paper, the role of ST in Mediterranean vineyards is reviewed namely in terms of its effect on vines’ ecophysiological and agronomical performance and its relation with soil properties and soil management strategies. The potential use of imaging approaches, e.g. thermography, is discussed as an alternative or complementary tool to assess ST and vertical canopy temperature profiles/gradients in vineyards. Soil management strategies to mitigate the negative impact of climate change, optimize ST variation and crop thermal microclimate (leaf and berry) are proposed and discussed, with emphasis on Mediterranean systems.

The high-elevation peatlands of the páramos of the northern Andes constitute a diverse environment that harbors large numbers of species and several types of plant communities along altitudinal, latitudinal, and environmental gradients. However, little is known about the structure and functioning of these ecosystems, including peatland vegetation types and their relative contribution to the production and accumulation of peat soils. In this paper we characterized the structure of peatland plant communities of the humid páramos of northern Ecuador by describing the distribution of plant growth-forms and their aboveground biomass patterns. Along an elevation gradient of 640 m we sampled vegetation in 16 peatlands and aboveground biomass in four peatlands. Three distinct peatland vegetation types were identified: High elevation Cushion peatlands, dominated by Plantago rigida and Distichia muscoides, Sedge and rush peatlands dominated by Carex spp. and Juncus spp., and Herbaceous and shrubby peatlands, with a more heterogenous and structurally complex vegetation. In terms of aboveground biomass, we found an 8-fold reduction in the higher peatlands compared to the lower sites, suggesting that the steep elevational gradients characteristic of Andean environments might be crucial in structuring the physiognomy and composition of peatland vegetation, either through its effects on temperature and other environmental factors, or through its effects on the age and development of soils. Additional studies are needed to evaluate the potential effects of temperature, hydrology, micro-topography, geological setting, and land-use, which are likely to influence vegetation patters in these peatlands.