Convolutional neural networks have remarkably progressed the performance of distinguishing plant diseases, severity grading, and nutrition deficiency prediction using leaf images. However, these tasks become more challenging in a realistic in-situ field condition. Often, a traditional machine learning model may fail to capture and interpret discriminative characteristics of plant health, growth and diseases due to subtle variations within leaf subcategories. A few deep learning methods have used additional preprocessing stages or network modules to address the problem, whereas several other methods have utilized pre-trained backbone CNNs, most of which are computationally intensive. Therefore, to address the challenge, we propose a lightweight autoencoder using separable convolutional layers in its encoder decoder blocks. A sigmoid gating is applied for refining the prowess of the encoders feature discriminability, which is improved further by the decoder. Finally, the feature maps of the encoder decoder are combined for rich feature representation before classification. The proposed Convolutional Lightweight Autoencoder for Plant disease classification, called CLAP, has been experimented on three public plant datasets consisting of cassava, tomato, maize, groundnut, grapes, etc. for determining plant health conditions. The CLAP has attained improved or competitive accuracies on the Integrated Plant Disease, Groundnut, and CCMT datasets balancing a tradeoff between the performance, and little computational cost requiring 5 million parameters. The training time is 20 milliseconds and inference time is 1 ms per image.
In this study, thermogravimetric analysis (TGA) was employed to investigate the thermal decomposition characteristics and kinetic parameters of eucalyptus sawdust at heating rates of 5, 10, 15, 20, and 30 ℃/min (20–900℃) under a nitrogen atmosphere (99.99 %, 30 mL/min). The pyrolysis process of eucalyptus sawdust was divided into three stages: drying and preheating (DH), fast weight-losing pyrolysis (FP) and carbon growing (CG). Mechanism function fitting determined that the DH and FP stages followed follow n-order reaction and Jander-type 3D diffusion, respectively. The activation energy (E), pre-exponential factor logarithm (lgA) and reaction order (n) of the DH and FP stages are 82.42 and 143.40 kJ/mol, 10.77 and 9.00 s−1, and 2.58 and 2.00, respectively. The results are highly consistent with the E and lgA values estimated by FWO and Friedman methods in the conversion rate (α) range of 0.2–0.8. For the CG stage, a new reaction process fitting equation (α=0.8695β0.1319lnT−5.0275β0.1442) was developed by numerical simulation of fitting α, temperature (T) and heating rate (β). The high coefficient of determination (R2>0.9700) confirms the equation's reliability. This study provides crucial theoretical support for optimizing the pyrolysis process of eucalyptus sawdust and guiding the reactor design.
Coloring is an important external quality of apples. Commercial apple varieties are bagged before the fruits ripen to ensure they grow in a pesticide-free environment, including preventing mechanical damage, reducing sunburn, etc. When the apple reaches the green fruit stage, the bag can be uncovered until it ripens and the peel is colored. There are two color patterns of “Fuji” apple peels (stripe and blush). In this study, the pattern of metabolite accumulation and gene expression was profiled in the striped and blushed apple peels at three ripening periods to elucidate the color formation mechanism within two weeks after removing the bag. The phenotypes presented in the peel coloring process are different between SF (stripe red) and HM (blush red), which have different accumulated levels of metabolites and gene expression during the coloring process. At the green fruit stage, a total of 83 differentially accumulated metabolites and 674 differentially expressed genes were identified between SF and HM. At the color turning stage, 48 DAMs and 880 DEGs were identified, including 20 flavonoids and 17 related genes. At the complete coloring stage, 95 DAMs and 2258 DEGs were identified, including 34 flavonoids and 23 related genes. In this study, a total of 10 kinds of key anthocyanins were found in the apple peel color-turning process. Keracyanin and cyanin were accumulated significantly at the apple color-turning period, while cyanidin-3-O-(6’’-O-malonyl) glucoside was accumulated at the complete coloring period. These key metabolites and related regulatory genes are responsible for the stripe and blush color formation in apple peel after uncovering the fruit bag.
Abstract X. americana L. is an important and multipurpose woody plant tree species. Due to the multipurpose uses of the species, it is over-exploited by the nearby communities for different purposes. Additionally, the species also have poor seed viability, and seed germination as well as insufficient pollination caused by long distances between male and female trees. This research was hypothesized whether there was a good distribution and regeneration status of X. americana or not based on the diameter class distribution. This study aimed to examine the population structure, phenological attributes, and fruit yield potential of X. americana in Quara district, Northwestern Ethiopia. The study was carried out by establishing 30 sample plots (20 m × 20 m) systematically. Further, 10 × 10 m subplots were laid out under the main plot quadrants for sapling and seedling count. Thirty reproductively matured trees with easily visible crowns were selected to record phenological characteristics and fruit yield. Quantitative data were determined by computing density, frequency, dominance, importance value index, and Pearson correlation. The findings revealed that X. americana appeared in the study area at about 488.33 population densities per hectare. An inverted J-shaped diameter class distribution was observed. X. americana flower initiation starts in March and sheds its fruit at the end of June. On average, 12.26 kg of fruits per tree were recorded with a maximum of fruits in mid-diameter size class trees. To ensure the species sustainably in the area, anthropogenic factors like deliberate fire, deforestation, and overgrazing should be properly managed.
IntroductionGrazing is a significant driver of grassland ecosystems changes, but the relationship between plant functional groups’ carbon (C): nitrogen (N): phosphorus (P) stoichiometry ratios and ecosystem C storage under different grazing management patterns remains uncertain.MethodsThis study investigated alpine meadows on the northeastern edge of the Tibetan Plateau, comparing four grazing patterns: banned grazing (BG), growing season rest-grazing (RG), traditional rest-grazing (TG), and continuous grazing (CG). We assessed the effects of these grazing patterns on plant functional group nutrient, C: N: P stoichiometry, and ecosystem C storage. The results provided valuable insights to support sustainable management strategies in alpine meadow ecosystems.Results and discussionThe results indicated that BG and RG enhanced nutrient enrichment, vegetation, soil, and ecosystem C storage. BG, RG, and TG increased aboveground C: N ratios but decreased C: P and N: P ratios. Grazing patterns indirectly influence ecosystem C storage by regulating plant stoichiometry and biomass allocation among functional groups. AGB of grasses is significantly positively correlated with ecosystem C storage (R² = 0.96), serving as the key driver of changes in C storage. In conclusion, maintaining biomass balance among functional groups and the C: N: P stoichiometry of plants is vital for preserving ecosystem C storage under grazing disturbances. It is essential that future management strategies incorporate these aspects to effectively protect and enhance C sequestration in alpine meadows.
Climate change is a major threat to crop potential and is characterized by both long-term shifts in temperature and precipitation patterns as well as increased occurrence of extreme weather events, these extreme weather events are the most immediate and intractable threat to agriculture. Crop resilience in the face of stress depends upon the speed and effectiveness with which plants and cropping systems sense and respond to that stress. A variety of agronomic practices including breeding, exogenous inputs (nutrients, water, biostimulants and others) and shifts in cultivation practice have been used to influence plant stress response to achieve the goal of increased plant and cropping system resilience. Traditional breeding is a powerful tool that has resulted in stable and long-term cultivar improvements but is often too slow and complex to meet the diverse, complex and unpredictable challenges of climate induced stresses. Increased inputs (water, nutrients, pesticides etc.) and management strategies (cropping system choice, soil management etc.) can alleviate stress but are often constrained by cost and availability of inputs. Exogenous biostimulants, microbials and plant hormones have shown great promise as mechanisms to optimize natural plant resilience resulting in immediate but non-permanent improvements in plant responses to climate induced stresses. The failure to modernize regulatory frameworks for the use of biostimulants in agriculture will constrain the development of safe effective tools and deprive growers of means to respond to the vagaries of climate change. Here we discuss the scientific rationale for eliminating the regulatory barriers that constrain the potential for biostimulants or products that modulate plant regulatory networks to address climate change challenges and propose a framework for enabling legislation to strengthen cropping system resilience.
Large language models (LLMs) have the potential of being useful tools that can automate tasks and assist humans. However, these models are more fluent in English and more aligned with Western cultures, norms, and values. Arabic-specific LLMs are being developed to better capture the nuances of the Arabic language, as well as the views of the Arabs. Yet, Arabs are sometimes assumed to share the same culture. In this position paper, I discuss the limitations of this assumption and provide preliminary thoughts for how to build systems that can better represent the cultural diversity within the Arab world. The invalidity of the cultural homogeneity assumption might seem obvious, yet, it is widely adopted in developing multilingual and Arabic-specific LLMs. I hope that this paper will encourage the NLP community to be considerate of the cultural diversity within various communities speaking the same language.
Abstract Background The leaf angle distribution (LAD) is an important structural parameter of agricultural crops that influences light interception, radiation fluxes and consequently plant performance. Therefore, LAD and its parametrized form, the Beta distribution, is used in many photosynthesis models. However, in field cultivations, these parameters are difficult to assess and cereal crops in particular pose challenges since their leaves are thin, flexible, and often bent and twisted around their own axis. To our knowledge, there is only a very limited set of methods currently available to calculate LADs of field-grown cereal crops that explicitly takes these special morphological properties into account. Results In this study, a new processing pipeline is introduced that allows for the generation of realistic leaf surface models and the analysis of LADs of field-grown cereal crops from 3D point clouds. The data acquisition is based on a convenient stereo imaging setup. The approach was validated with different artificial targets and results on the accuracy of the 3D reconstruction, leaf surface modeling and calculated LAD are given. The mean error of the 3D reconstruction was below 1 mm for an inclination angle range between 0° and 75° and the leaf surface could be quantified with an average accuracy of 90%. The concordance correlation coefficient (CCC) of 99.6% (p-value = $$1.5* {10}^{-29}$$ 1.5 ∗ 10 - 29 ) indicated a high correlation between the reconstructed inclination angle and the identity line. The LADs for bent leaves were reconstructed with a mean error of 0.21° and a standard deviation of 1.55°. As an additional parameter, the insertion angle was reconstructed for the artificial leaf model with an average error < 5°. Finally, the method was tested with images of field-grown cereal crops and Beta functions were approximated from the calculated LADs. The mean CCC between reconstructed LAD and calculated Beta function was 0.66. According to Cohen, this indicates a high correlation. Conclusion This study shows that our image processing pipeline can reconstruct the complex leaf shape of cereal crops from stereo images. The high accuracy of the approach was demonstrated with several validation experiments including artificial leaf targets. The derived leaf models were used to calculate LADs for artificial leaves and naturally grown cereal crops. This helps to better understand the influence of the canopy structure on light absorption and plant performance and allows for a more precise parametrization of photosynthesis models via the derived Beta distributions.
Luciellen da Costa Ferreira, Ian Carlos Bispo Carvalho, Lúcio André de Castro Jorge
et al.
Food security, a critical concern amid global population growth, faces challenges in sustainable agricultural production due to significant yield losses caused by plant diseases, with a multitude of them caused by seedborne plant pathogen. With the expansion of the international seed market with global movement of this propagative plant material, and considering that about 90% of economically important crops grown from seeds, seed pathology emerged as an important discipline. Seed health testing is presently part of quality analysis and carried out by seed enterprises and governmental institutions looking forward to exclude a new pathogen in a country or site. The development of seedborne pathogens detection methods has been following the plant pathogen detection and diagnosis advances, from the use of cultivation on semi-selective media, to antibodies and DNA-based techniques. Hyperspectral imaging (HSI) associated with artificial intelligence can be considered the new frontier for seedborne pathogen detection with high accuracy in discriminating infected from healthy seeds. The development of the process consists of standardization of methods and protocols with the validation of spectral signatures for presence and incidence of contamined seeds. Concurrently, epidemiological studies correlating this information with disease outbreaks would help in determining the acceptable thresholds of seed contamination. Despite the high costs of equipment and the necessity for interdisciplinary collaboration, it is anticipated that health seed certifying programs and seed suppliers will benefit from the adoption of HSI techniques in the near future.
This paper is an invitation to the process systems engineering community to change the paradigm for process plants. The goal is to achieve much easier convergence while retaining accuracy on par with the rigorous models. Accurate plant models of existing plants can be linear or much less nonlinear if they are based on mass component flows and stream properties per unit mass properties instead of molar flows and mole fractions. Accurate stream properties per unit mass can be calculated at stream specific conditions by linear approximations which in many instances eliminates mole fraction-based flash calculations. Hybrid data-driven node models fit naturally in this paradigm, since they used measured data, which is either in mass or in volumetric units, but never in moles. Instantiation of models at all levels of abstraction (planning, scheduling, optimization, and control models) from the same plant topology representation will ensure inheritance of solutions from mass-only to mass-and-energy to mass-and-energy-and-stream-properties, thereby ensuring consistency of solutions between these models. None of the existing software provides inheritance between different levels of plant abstraction (i.e. inheritance between models for different business applications) or different levels of abstractions per plant sections or per time periods, which motivates this exposition.
BL and UV Ceti are a nearby (2.7 pc) binary system with similar masses, spectral types, and rapid rotation rates, but very different magnetic activity. UV Ceti's much stronger large-scale magnetic field may cause this difference, highlighting key unanswered questions about dynamo processes in fully convective objects. Here we present multi-epoch characterization of the radio spectrum of UV Ceti spanning 1-105 GHz, exhibiting flared emission similar to coronal activity, auroral-like emission analogous to planetary magnetospheres, and slowly-varying persistent emission. Radio observations are a powerful means to probe the role that the large-scale magnetic field of UV Ceti has in non-thermal particle acceleration, because radio-frequency phenomena result from both the activity of small-scale field features as well as large-scale auroral current systems. We find temporal variability at all bands observed, and a hint of rotational modulation in the degree of circular polarization up to 40 GHz. The persistent component of the emission is fairly constant from 1-105 GHz, making optically thick emission or optically thin gyrosynchrotron from electrons with an isotropic pitch angle distribution unlikely. We discuss the possibility of emission mechanisms analogous to Jupiter's radiation belts.
As a predominant phenolic compound in apple fruits, chlorogenic acid (CGA) benefits human health due to its various antioxidant properties. However, little has been known regarding the molecular mechanism underlying the CGA accumulation in apple fruits. In this study, we measured the CGA content and relative enzymes’ activities during fruit development in two different flesh-colored cultivars ‘Huashuo’ and ‘Red Love’. The CGA content in both cultivars decreased sharply from 30 days after full bloom (DAFB) to 60 DAFB. Notably, the CGA content in fruit flesh was relatively higher than that in the peel. Further, the activities of C3H and HCT enzymes downstream of the CGA biosynthesis showed the similar changing trend as CGA content. Based on the transcriptome data of ‘Huashuo’ fruit at 30 DAFB and 60 DAFB, 23 differentially expressed CGA synthesis-related genes were screened. Gene expression analyses further showed that <i>MdHCT1/2/4/5/6</i> and <i>MdC3H1/2/3</i> were positively correlated to the variation of CGA content in two cultivars. These findings establish a theoretical foundation for further mechanism study on CGA biosynthesis and provide guidance for nutrient improvement in apple breeding programs.
Marwa S. Goda, Safwat A. Ahmed, Fadia El Sherif
et al.
<i>Achillea fragrantissima</i> Forssk. (Family: Asteraceae) has been used as a natural remedy in the Arabian region for its antihyperglycemic activity. As a result of the intensive demand for this plant in folk medicinal uses, its scarcity has become problematic. This study has explored methods that produce an efficient in vitro culture protocol for the conservation of this plant as well as the enhancement of its hypoglycemic activity. <i>A. fragrantissima</i> cultures on Murashige and Skoog (MS) medium supplemented with 3.6 µM/L of 6-benzyl aminopurine (BAP) for a two month period resulted in maximum in vitro shoot proliferation (12.33 shoots/explant) while MS medium supplemented with 2.4 µM/L 1-naphthalene acetic acid (NAA) provided maximum in vitro adventitious root formation (2.46 roots/shoot tip explant). Callus induction was favored by leaf explants cultured on MS medium and supplemented with 3 µM/L BAP and 3 µM/L IAA media in dark conditions. Further in vivo study of some selected feedings determined that the best hypoglycemic activity was obtained in either indole-3-butyric acid (IBA)-fed plants (24%) or NAA-fed plants (22%). Both treatments enhanced insulin-like activity in STZ-treated diabetic Sprague-Dawley rats when compared with the wild plant (10%). Moreover, the IBA-fed plants showed significant antioxidant activity while the NAA-fed plants inhibited salivary alpha amylase. The framework of this study provides in vitro culture methods that can sustain the cultivation of this over-exploited <i>A. fragrantissima</i> plant as well as increase its antioxidant and insulin-like activities.
Zachary D. Eldred, Shufang Tian, Gary E. Vallad
et al.
This new 7-page article provides a concise overview of some influential high-tunnel tomato soilborne and foliar diseases and their cultural management techniques. Written by Zachary D. Eldred, Shufang Tian, Gary E. Vallad, Xin Zhao, Mathews Paret, and Nicholas S. Dufault and published by the UF/IFAS Plant Pathology Department.
https://edis.ifas.ufl.edu/pp368
We consider stabilization of linear time-invariant (LTI) and single input single output (SISO) plants in the frequency domain from a fresh perspective. Compensators that are themselves stable are sometimes preferred because they make starting the system easier. Such starting remains easy if there is a stable compensator in parallel with the plant rather than in a feedback loop. In such an arrangement, we explain why it is possible to stabilize all plants whose transfer functions are proper rational functions of the Laplace variable $s$. In our proposed architecture we have (i) an optional compensator $C_s(s)$ in series with the plant $P(s)$, (ii) a necessary compensator $C_p(s)$ in parallel with $C_s(s)P(s)$, along with (iii) a feedback gain $K$ for the combined new plant $C_s(s)P(s)+C_p(s)$. We show that stabilization with stable $C_s(s)$ and $C_p(s)$ is always possible. In our proposed solution the closed-loop plant is biproper and has all its zeros in the left half plane, so there is a $K_0$ such that the plant is stable for $K>K_0$. We are not aware of prior use of parallel compensators with such a goal. Our proposed architecture works even for plants that are impossible to stabilize with stable compensators in the usual single-loop feedback architecture. Several examples are provided.
Culture shapes people's behavior, both online and offline. Surprisingly, there is sparse research on how cultural context affects network formation and content consumption on social media. We analyzed the friendship networks and dyadic relations between content producers and consumers across 73 countries through a cultural lens in a closed-network setting. Closed networks allow for intimate bonds and self-expression, providing a natural setting to study cultural differences in behavior. We studied three theoretical frameworks of culture - individualism, relational mobility, and tightness. We found that friendship networks formed across different cultures differ in egocentricity, meaning the connectedness between a user's friends. Individualism, mobility, and looseness also significantly negatively impact how tie strength affects content consumption. Our findings show how culture affects social media behavior, and we outline how researchers can incorporate this in their work. Our work has implications for content recommendations and can improve content engagement.
Yong Cao, Yova Kementchedjhieva, Ruixiang Cui
et al.
Building upon the considerable advances in Large Language Models (LLMs), we are now equipped to address more sophisticated tasks demanding a nuanced understanding of cross-cultural contexts. A key example is recipe adaptation, which goes beyond simple translation to include a grasp of ingredients, culinary techniques, and dietary preferences specific to a given culture. We introduce a new task involving the translation and cultural adaptation of recipes between Chinese and English-speaking cuisines. To support this investigation, we present CulturalRecipes, a unique dataset comprised of automatically paired recipes written in Mandarin Chinese and English. This dataset is further enriched with a human-written and curated test set. In this intricate task of cross-cultural recipe adaptation, we evaluate the performance of various methods, including GPT-4 and other LLMs, traditional machine translation, and information retrieval techniques. Our comprehensive analysis includes both automatic and human evaluation metrics. While GPT-4 exhibits impressive abilities in adapting Chinese recipes into English, it still lags behind human expertise when translating English recipes into Chinese. This underscores the multifaceted nature of cultural adaptations. We anticipate that these insights will significantly contribute to future research on culturally-aware language models and their practical application in culturally diverse contexts.
Plant diseases remain a considerable threat to food security and agricultural sustainability. Rapid and early identification of these diseases has become a significant concern motivating several studies to rely on the increasing global digitalization and the recent advances in computer vision based on deep learning. In fact, plant disease classification based on deep convolutional neural networks has shown impressive performance. However, these methods have yet to be adopted globally due to concerns regarding their robustness, transparency, and the lack of explainability compared with their human experts counterparts. Methods such as saliency-based approaches associating the network output to perturbations of the input pixels have been proposed to give insights into these algorithms. Still, they are not easily comprehensible and not intuitive for human users and are threatened by bias. In this work, we deploy a method called Testing with Concept Activation Vectors (TCAV) that shifts the focus from pixels to user-defined concepts. To the best of our knowledge, our paper is the first to employ this method in the field of plant disease classification. Important concepts such as color, texture and disease related concepts were analyzed. The results suggest that concept-based explanation methods can significantly benefit automated plant disease identification.
In an incineration plant, remote operation from a centralized control room is now possible, but inspection and cleaning of equipment still require a worker to visit the site. When the plant owner reduces the number of workers due to operation costs, it will be standard for a single worker to visit the site. Therefore, it is necessary to monitor the location of workers in real-time to detect unexpected human accidents quickly. Conventional methods use radio waves, such as Wi-Fi and Bluetooth, but there is little demand for communication equipment in the incineration plant. However, there is not enough demand for communication facilities in the incineration plant. It is too large to bear the cost of installing wireless access points, and Bluetooth Low Energy (BLE) beacons just for positioning. Therefore, we are focusing on magnetism using for indoor positioning method. In addition, the incineration plant has a lot of types of equipment that contains a wide range of magnetized metals, large motors, and generators. We could observe the magnetic peculiarity at each point. Based on these assumptions, we have developed a new indoor positioning method at the incineration plant. This paper describes the development of an indoor positioning system for an incineration plant. And we propose three methods for fingerprinting matching: Point matching, Path matching, and DTW matching. The average positioning errors of these methods are 6.89 m, 0.05 m, and 0.06 m, respectively.