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Livestock dung, discarded crop straws, and residual plastic film are the primary agricultural non-point sources of pollution. For livestock dung and discarded crop straw, the general treatment focuses on compost, animal fodder, industrial raw material, and new energy. The development of degradable mulch film is the main way to solve pollution from residual plastic film. However, an effective way to solve the above three types of pollution simultaneously and use them for ecological circular agriculture has been less studied. In this study, using cow dung and flax straw wastes as raw materials, we prepared natural, fibre-based paper mulch using the rapid-Kothen method and analysed the film-forming mechanism. Based on the Van Soest method, the cow dung and flax straw waste contain abundant cellulose fibres: 36.75% and 54.69%, respectively. The tensile strength and tear strength of fibre paper mulch are 1.87 kN/m and 19.91 N/mm, respectively. To enhance the adaptability of the fibre paper mulch in humid environments, the surface of the mulch was treated with alkyl ketene dimer (AKD). The AKD-coated fibre paper mulch displays hydrophobic properties, indicated by a contact angle of 128° ± 2°. It has a wet tensile strength of 0.64 kN/m and a wet tear strength of 8.23 N/mm. Additionally, it exhibits a dry tensile strength and a tear strength of 2.13 kN/m and 16.43 N/mm, respectively. Notably, the dry tensile strength is increased by 16.31%. In this way, the livestock dung and discarded crop straw can be reused, reducing dung pollution and straw burning in livestock farms, and the final products can alleviate the residual film pollution simultaneously. [ABSTRACT FROM AUTHOR]

European paper mills are currently facing the question of whether recovered paper, their main raw material, will be available in sufficient quantities and an acceptable quality in the future. An alternative to recovered paper or wood fiber is the use of agricultural residues such as wheat straw. Sodium carbonate-based straw pulping processes have the advantage of not requiring recausticizing for chemical recovery, which reduces investment and operating costs. With the addition of oxygen, delignification can be significantly improved to provide pulps suitable for bleaching. This study compares the pulping of wheat straw using sodium carbonate, sodium carbonate + oxygen, and sodium carbonate + sodium hydroxide + oxygen. Pulping parameters such as temperature, retention time, and chemical charge were varied, and their influence on pulp properties was studied. The use of sodium carbonate alone produced pulps with high yields of up to 72% and comparably high burst and compressive strength. The addition of oxygen and small amounts of sodium hydroxide produced pulps with a high initial brightness of 42 %ISO and a low kappa number (18), still at high pulp yields of 62%. These pulps were two-stage bleached to achieve brightness levels of up to 73 %ISO. [ABSTRACT FROM AUTHOR]

Mulch has been considered effective for saving water and promoting plant growth. However, little has been investigated about the effect of mulch from recycled shredded paper under different soil moisture conditions on spinach growth. A pot-grown Komatsuna spinach experiment with a factorial design was conducted under two main conditions, with and without shredded paper mulch. Each of these conditions was treated with three different soil moisture levels (SWC) 20%, 25%, and 30%. The smallest evapotranspiration amount was in the mulch-only, no-plant treatment with SWC 20% (92.88 mm), and the largest was in the plant-only, no-mulch treatment (226.19 mm). All biomass yield attributions were negatively influenced by increasing the frequency of irrigation and SWC levels. SWC 20% with shredded mulch resulted in the largest dry matter, although it was statistically non-significant. But it resulted in taller plant heights and a larger leaf area index (LAI) compared to soil with no shredded mulch (p < 0.05). For SWC 25% and 30% treatments, Komatsuna spinach with no shredded mulch resulted in slightly superior plant dry matter and plant height, compared to mulched plants. These results suggest that SWC 20% with shredded paper mulch has the highest potential for saving water among all treatments for growing spinach under limited water availability. [ABSTRACT FROM AUTHOR]

Hibiscus cannabinus (kenaf) is an annual fiber crop grown in warm seasons and known for its remarkable productivity; it has been cultivated worldwide for thousands of years as a fiber source. While every part of the plant can be utilized for some purpose, its primary significance lies in the diverse applications of its cellulosic fiber. Kenaf features a blend of long bast and short core fibers, rendering it suitable for various industrial uses. Initially utilized for cordage and livestock feed, kenaf's applications have expanded over the last century to encompass its utilization as paper pulp, biocomposites, textiles, biomass energy, seed oil, filtration aids, industrial absorbents, and even as a component of potting medium or as a potential source of medicine. Although traditionally a niche crop, the discovery of its diverse applications positions kenaf for rapid expansion in production in the upcoming decades. This article aims to explore the manifold applications of kenaf, highlighting those with the greatest future potential and discussing those that hold promise for commercial-level application with additional research. [ABSTRACT FROM AUTHOR]

With the rapid development of new-generation cyber–physical system (CPS) technologies, the smart operation and management of the broad bean harvesting system are enabled. This paper proposed a smart broad bean harvesting system (SBHS) and a self-adaptive control method based on CPS technologies. Firstly, the overall architecture of the SBHS is designed, which consists of four main components, namely optimal intelligent perception environment configuration, digital twin model construction, virtual simulation and real-time optimization, self-adaptive adjustment and control. Then, three key enabling technologies are discussed in detail, namely, intelligent perception environment configuration for the SBHS, digital twin model construction for the SBHS, colored Petri net (CPN)-based self-adaptive analysis and control of the harvesting system. Lastly, a proof-of-concept experiment based on a cooperative company is presented to illustrate the main work logic and advantage of the proposed SBHS. After the edge–cloud cooperative intelligent harvesting environment is configured, the CPN model for the workflow of the SBHS is created to analyze and optimize the harvesting processes. In addition, a management and control platform are developed to further illustrate the implementation of the proposed SBHS and the self-adaptive control method. [ABSTRACT FROM AUTHOR]

Target and off-target spray depositions determine the spray's effectiveness and impact on the environment. A decisive stage in the measurement of spray deposition and drift is selecting an appropriate sampling approach under field conditions. There are various approaches available for sampling spray deposition and drift, during the evaluation of ground sprayers used for the UAV sprayer assessment, under field conditions. In this study, two sampling approaches (water-sensitive paper, and glass strip collectors) were compared to analyze spray deposition in target and off-target zones. The results showed a variation in the estimation of the spray deposits among the two applied sampling methods. The results showed that the water-sensitive paper recorded the droplet deposition in the target zone with a range from 0.049 to 4.866 µLcm−2, whereas the glass strip recorded from 0.11 to 0.793 µLcm−2. The results also showed the water sensitive paper recorded an 80.3% higher deposition than that of the glass strip at zero position during the driving flight height 2 m and flight speed 2 ms−1 (T1 treatment). It can be concluded that variation in recorded depositing is due to the sampling material. It is recommended that the confident deposition results, measurement methods and sampling approaches must be standardized for UAV sprayers according to the field conditions and controlled within artificial assessments. [ABSTRACT FROM AUTHOR]

This article presents perennial grasses, without whose presence it is impossible to imagine the natural environment as well as agriculture, recreation, sport, and satisfactory aesthetics of the environment. Grasses have by far the widest distribution of all flowering families, grow on every continent, and are part of all the major biomes of the terrestrial world. They not only occur in almost all types of natural landscapes but also find a prominent place in the agricultural landscape. Grasses are not only a source of food for people (wheat, rice, maize, millet, etc.) and feed for livestock, but also a source of energy, building materials, a component of paper pulp, etc. Moreover, grasses have numerous uses to enhance the beauty of the surrounding landscape, bring relaxation, health, and comfort to people (i.e., gardens, parks, and sports facilities), and support land protection. This article describes just these, not often mentioned, and characterized grass uses, with an emphasis on the relationship between different species of perennial grasses and their functionality. The aim is to show the various aspects of the amenity use of grasses in the context of species diversity and their future under the conditions of a changing climate. [ABSTRACT FROM AUTHOR]

A large number of studies of Cistus ladanifer highlight this Mediterranean shrub as a source of the phenolic compounds responsible for the allelopathic potential of this species. There are few phenolic compounds present in C. ladanifer that have not yet been studied. The objective of this work was to evaluate the activity of estragole and 2-isopropylphenol on filter paper and soil on monocotyledons (Allium cepa) and dicotyledons (Lactuca sativa). The results showed that when the test was carried out on paper, the germination and the growth of the L. sativa was strongly inhibited by 2 isopropylphenol and estragole. 2 isopropylphenol showed an IC50 on the germination of 0.7 mM and 0.1 mM on the germination rate, 0.4 mM on the size of radicle and 0.3 mM on the size of hypocotyl. Estragole showed an IC50 on the germination rate of 1.5 mM and 1.1 mM on the size of hypocotyl. The effects of these pure compounds on A. cepa were lower, and when the assays were performed on the soil, they were dissipated. The mixture of these compounds on A. cepa had 0.6 mM IC50 for the length hypocotyl on paper and 1.1 mM for the length of the radicle on soil. The mixture on L. sativa also inhibited the length of the radicle with an IC50 of 0.6 mM. On the other hand, it was also observed that estragole stimulated the growth of the A. cepa radicle length on soil, showing a hormetic effect with an EC50 of 0.1 mM. In conclusion, it can be said that for a species to be allelopathic in nature, it is essential to verify the effect of its possible allelochemicals on the target species, on the soil in which they will exert their action and at the concentrations found in their usual environment, in addition to taking into account the interaction with other compounds present in the medium. [ABSTRACT FROM AUTHOR]

Plant biomass in the form of waste materials and by-products from various industries can be a valuable material for the production of composts and growing media for urban gardening. In this study, pulp and paper mill sludge, fruit-vegetable waste, mushroom spent substrate and rye straw were used to produce compost that was further used as a medium component in container cultivation of tomato. The plants were grown in containers with a capacity of 3 dm3 filled with three types of compost-based growing media supplemented with high peat, fen peat, pine bark and wood fiber. The tomato plants grown in 100% peat substrate served as controls. The plants grown in the compost-enriched media had a higher leaf greening index and percentage of ripe fruit, and exhibited an increased content of total polyphenols and flavonoids, potassium, calcium, magnesium and copper in fruit as compared with the control. The tomatoes grown in a medium consisting of 25% compost, 30% high peat, 15% low peat, 20% pine bark and 10% wood fiber reached the highest fresh fruit weight, total polyphenol content and L-ascorbic acid levels. This study demonstrated that the compost produced from natural materials from various sources was a valuable potting medium supplement with positive effects on tomato yield and nutritional value. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the influence of paper bagging of grape bunches on the morphological, mechanical, and chemical characteristics of berries of three table grapes varieties as an environmentally-friendly technique for protecting clusters from biotic and abiotic agents. Clusters of Italia, Autumn Royal, and Regal Seedless grape cultivars were bagged and compared to a not-bagged control. Air temperature inside and outside the bags was monitored. Bunch weight and length, number of berries per bunch, berry longitudinal and transversal diameter, berry mass, number of seeds per berry (normal in size and aborted), soluble solid content, titratable acidity, and skin color by CIEL*a*b* parameters were determined on four points of each berry. Berries were evaluated using texture analysis, and the main texture profile analysis parameters were compared. The air temperatures around not-bagged clusters were slightly higher than inside the bags. In all the cultivars under evaluation, bagged bunches were heavier compared with not-bagged ones. In Autumn Royal and Regal Seedless, these differences were mainly owing to the higher number of berries and higher berry weight of the bagged bunches. Regarding mechanical properties, in seedless varieties, the bagging treatment increased berry hardness (related to the berry firmness) and decreased berry cohesiveness and resilience, whereas an opposite behavior was found in cv. Italia. Berry skin break force was lower in the bagging treatment in all the analyzed varieties, indicating a softer and easier-to-chew berry skin. The findings demonstrate that the bagging technique affected the three variety parameters to different extents. The main differences were found in the seedless varieties in terms of berry size and bunch characteristics. For all varieties, bagged bunches achieved the quality level required by the market, confirming the suitability of this technique. However, the bag industry is proposing many different bag types (differing in material, shape, color, and closing system); therefore, further studies are needed to obtain more complete and exhaustive technical information. [ABSTRACT FROM AUTHOR]

In order to search for bio-rational and eco-friendly pest control agents to protect crops from insect infestation, while avoiding the toxic hazards of chemical pesticides, essential oils (EOs) were hydrodistilled from Juniperus procera and Thymus vulgaris and analyzed using gas chromatography–flame ionization detection (GC–FID), and gas chromatography–mass spectrometry (GC–MS). Eugenol (71.3%), β-caryophyllene (11.8%), and α-pinene (6.1%) were isolated as the major components of J. procera EO, whereas thymol (58.1%), p-cymen (10.3%), and carvacrol (8.3%) were the main terpenes in T. vulgaris EO. The EOs and terpenes exhibited considerable bioactivity against the khapra beetle using the contact and fumigation bioassays, where T. vulgaris EO was superior in bioactivity. Among the terpenes tested, carvacrol and eugenol were superior. Regarding contact toxicity using impregnated filter paper and after 24 h of exposure, the LC50 values ranged between 21.4 and 77.0 µL/cm2 against larvae and between 16.1 and 69.6 µL/cm2 against adults. After 48 h, these values decreased remarkably. Upon fumigation and after 48 h of exposure, the LC50 values ranged between 23.0 and 65.3 µL/L against larvae, and from 14.2 to 56.4 µL/L against adults. The botanicals effectively inhibited the acetylcholinesterase activity of the larvae; however, they were safe for the earthworm E. fetida and did not alter the viability of wheat grains. There is a potential for using these botanicals to control T. granarium. However, further investigations are needed to confirm the safety of these phytochemicals before use as grain protectants on a commercial scale. [ABSTRACT FROM AUTHOR]

The knowledge within wheat production chain data has multiple levels and complex semantic relationships, making it difficult to extract knowledge from them. Therefore, this paper proposes a fine-grained knowledge extraction method for the wheat production chain based on ontology. For the first time, the conceptual layers of ploughing, planting, managing, and harvesting were defined around the main agricultural activities of the wheat production chain. Based on this, the entities, relationships, and attributes in the conceptual layers were defined at a fine-grained level, and a spatial–temporal association pattern layer with four conceptual layers, twenty-eight entities, and forty-two relationships was constructed. Then, based on the characteristics of the self-constructed dataset, the Word2vec-BiLSTM-CRF model was designed for extracting the knowledge within it, i.e., the entity–relationship–attribute model and the Word2vec-BiLSTM-CRF model in this paper were compared with the four SOTA models. The results show that the accuracy and F1 value improved by 8.44% and 8.89%, respectively, compared with the BiLSTM-CRF model. Furthermore, the entities of the pest and disease dataset were divided into two different granularities for the comparison experiment; the results show that for entities with "disease names" and "pest names", the recognition accuracy at the fine-grained level is improved by 32.71% and 31.58%, respectively, compared to the coarse-grained level, and the recognition performance of various fine-grained entities has been improved. [ABSTRACT FROM AUTHOR]

Corn crop row recognition during different growth stages is a major difficulty faced by the current development of visual navigation technology for agricultural robots. In order to solve this problem, an algorithm for recognizing corn crop rows during different growth stages is presented based on the ST-YOLOv8s network. Firstly, a dataset of corn crop rows during different growth stages, including the seedling stage and mid-growth stage, is constructed in this paper; secondly, an improved YOLOv8s network, in which the backbone network is replaced by the swin transformer (ST), is proposed in this paper for detecting corn crop row segments; after that, an improved supergreen method is introduced in this paper, and the segmentation of crop rows and background within the detection frame is achieved utilizing the enhanced method; finally, the corn crop row lines are identified using the proposed local–global detection method, which detects the local crop rows first, and then detects the global crop rows. The corn crop row segment detection experiments show that the mean average precision (MAP) of the ST-YOLOv8s network during different growth stages increases by 7.34%, 11.92%, and 4.03% on average compared to the MAP of YOLOv5s, YOLOv7, and YOLOv8s networks, respectively, indicating that the ST-YOLOv8s network has a better crop row segment detection effect compared to the comparison networks. Corn crop row line detection experiments show that the accuracy of the local–global detection method proposed in this paper is improved by 17.38%, 10.47%, and 5.99%, respectively, compared with the accuracy of the comparison method; the average angle error is reduced by 3.78°, 1.61°, and 0.7°, respectively, compared with the average angle error of the comparison method; and the average fitting time is reduced by 5.30 ms, 18 ms, and 33.77 ms, respectively, compared with the average fitting time of the comparison method, indicating that the local–global detection method has a better crop row line detection effect compared to the comparison method. In summary, the corn crop row recognition algorithm proposed in this paper can well accomplish the task of corn crop row recognition during different growth stages and contribute to the development of crop row detection technology. [ABSTRACT FROM AUTHOR]

The accurate prediction of soil moisture content helps to evaluate the quality of farmland. Taking the black soil in the Nanguan District of Changchun City as the research object, this paper proposes a stacking ensemble learning model integrating hybrid neural networks to address the issue that it is difficult to improve the accuracy of inversion soil moisture content by a single model. First, raw hyperspectral data are processed by removing edge noise and standardization. Then, the gray wolf optimization (GWO) algorithm is adopted to optimize a convolutional neural network (CNN), and a gated recurrent unit (GRU) and an attention mechanism are added to construct a hybrid neural network model (GWO–CNN–GRU–Attention). To estimate soil water content, the hybrid neural network model is integrated into the stacking model along with Bagging and Boosting algorithms and the feedforward neural network. Experimental results demonstrate that the GWO–CNN–GRU–Attention model proposed in this paper can better predict soil water content; the stacking method of integrating hybrid neural networks overcomes the limitations of a single model's instability and inferior accuracy. The relative prediction deviation (RPD), root mean square error (RMSE), and coefficient of determination (R2) on the test set are 4.577, 0.227, and 0.952, respectively. The average R2 and RPD increased by 0.056 and 1.418 in comparison to the base learner algorithm. The study results lay a foundation for the fast detection of soil moisture content in black soil areas and provide a data source for intelligent irrigation in agriculture. [ABSTRACT FROM AUTHOR]

The cacao pod borer (CPB) (Conopomorpha cramerella) is an invasive insect that causes significant economic loss for cacao farmers. One of the most efficient ways to reduce CPB damage is to continuously monitor its presence. Currently, most automated technologies for continuous insect pest monitoring rely on an internet connection and a power source. However, most cacao plantations are remotely located and have limited access to internet and power sources; therefore, a simpler and readily available tool is necessary to enable continuous monitoring. This research proposes a mobile application developed for rapid and on-site counting of CPBs on sticky paper traps. A CPB counting algorithm was developed and optimized to enable on-device computations despite memory constraints and limited capacity of low-end mobile phones. The proposed algorithm has an F1-score of 0.88, with no significant difference from expert counts (R2 = 0.97, p-value = 0.55, α = 0.05). The mobile application can be used to provide the required information for pest control methods on-demand and is also accessible for low-income farms. This is one of the first few works on enabling on-device processing for insect pest monitoring. [ABSTRACT FROM AUTHOR]

Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted. [ABSTRACT FROM AUTHOR]

Fast and accurate counting and positioning of flowers is the foundation of automated flower cultivation production. However, it remains a challenge to complete the counting and positioning of high-density flowers against a complex background. Therefore, this paper proposes a lightweight flower counting and positioning model, Light-FC-YOLO, based on YOLOv8s. By integrating lightweight convolution, the model is more portable and deployable. At the same time, a new efficient detection head, Efficient head, and the integration of the LSKA large kernel attention mechanism are proposed to enhance the model's feature detail extraction capability and change the weight ratio of the shallow edge and key point information in the network. Finally, the SIoU loss function with target angle deviation calculation is introduced to improve the model's detection accuracy and target positioning ability. Experimental results show that Light-FC-YOLO, with a model size reduction of 27.2% and a parameter reduction of 39.0%, has a Mean Average Precision (mAP) and recall that are 0.8% and 1.4% higher than YOLOv8s, respectively. In the counting comparison experiment, the coefficient of determination (R2) and Root Mean Squared Error (RMSE) of Light-FC-YOLO reached 0.9577 and 8.69, respectively, both superior to lightweight models such as YOLOv8s. The lightweight flower detection method proposed in this paper can efficiently complete flower positioning and counting tasks, providing technical support and reference solutions for automated flower production management. [ABSTRACT FROM AUTHOR]

Traditional DeepLabV3+ image semantic segmentation methods face challenges in pitaya orchard environments characterized by multiple interference factors, complex image backgrounds, high computational complexity, and extensive memory consumption. This paper introduces an improved visual navigation path recognition method for pitaya orchards. Initially, DeepLabV3+ utilizes a lightweight MobileNetV2 as its primary feature extraction backbone, which is augmented with a Pyramid Split Attention (PSA) module placed after the Atrous Spatial Pyramid Pooling (ASPP) module. This improvement enhances the spatial feature representation of feature maps, thereby sharpening the segmentation boundaries. Additionally, an Efficient Channel Attention Network (ECANet) mechanism is integrated with the lower-level features of MobileNetV2 to reduce computational complexity and refine the clarity of target boundaries. The paper also designs a navigation path extraction algorithm, which fits the road mask regions segmented by the model to achieve precise navigation path recognition. Experimental findings show that the enhanced DeepLabV3+ model achieved a Mean Intersection over Union (MIoU) and average pixel accuracy of 95.79% and 97.81%, respectively. These figures represent increases of 0.59 and 0.41 percentage points when contrasted with the original model. Furthermore, the model's memory consumption is reduced by 85.64%, 84.70%, and 85.06% when contrasted with the Pyramid Scene Parsing Network (PSPNet), U-Net, and Fully Convolutional Network (FCN) models, respectively. This reduction makes the proposed model more efficient while maintaining high segmentation accuracy, thus supporting enhanced operational efficiency in practical applications. The test results for navigation path recognition accuracy reveal that the angle error between the navigation centerline extracted using the least squares method and the manually fitted centerline is less than 5°. Additionally, the average deviation between the road centerlines extracted under three different lighting conditions and the actual road centerline is only 2.66 pixels, with an average image recognition time of 0.10 s. This performance suggests that the study can provide an effective reference for visual navigation in smart agriculture. [ABSTRACT FROM AUTHOR]

Cotton is an important economic crop; therefore, enhancing cotton yield and cultivating superior varieties are key research priorities. The seedling stage, a critical phase in cotton production, significantly influences the subsequent growth and yield of the crop. Therefore, breeding experts often choose to measure phenotypic parameters during this period to make breeding decisions. Traditional methods of phenotypic parameter measurement require manual processes, which are not only tedious and inefficient but can also damage the plants. To effectively, rapidly, and accurately extract three-dimensional phenotypic parameters of cotton seedlings, precise segmentation of phenotypic organs must first be achieved. This paper proposes a neural network-based segmentation algorithm for cotton seedling organs, which, compared to the average precision of 75.4% in traditional unsupervised learning, achieves an average precision of 96.67%, demonstrating excellent segmentation performance. The segmented leaf and stem point clouds are used for the calculation of phenotypic parameters such as stem length, leaf length, leaf width, and leaf area. Comparisons with actual measurements yield coefficients of determination R 2 of 91.97%, 90.97%, 92.72%, and 95.44%, respectively. The results indicate that the algorithm proposed in this paper can achieve precise segmentation of stem and leaf organs, and can efficiently and accurately extract three-dimensional phenotypic structural information of cotton seedlings. In summary, this study not only made significant progress in the precise segmentation of cotton seedling organs and the extraction of three-dimensional phenotypic structural information, but the algorithm also demonstrates strong applicability to different varieties of cotton seedlings. This provides new perspectives and methods for plant researchers and breeding experts, contributing to the advancement of the plant phenotypic computation field and bringing new breakthroughs and opportunities to the field of plant science research. [ABSTRACT FROM AUTHOR]

Root traits have an important impact on plant growth, which reflects the adaptation of plants to nutrients. This paper describes the focus and progress of root traits research and provides references for future research. Based on the Web of Science online database, articles related to root traits from 2000–2021 were evaluated using bibliometric methods. The results showed that the study of root traits has been growing steadily in the last two decades. Wang Yan is the author with the highest number of published papers, the most citations and the highest H-index. The top three published countries are the USA, China and India. The top three institutions are the University of Western Australia, China Agricultural University and Nanjing Agricultural University. Plant and Soil, Frontiers in Plant Science and PLoS One have the highest number of published articles. In terms of co-occurrence of keyword analysis, growth, soil and plant diversity are highly correlated with root traits. It was found that cooperation between authors, institutions and countries are not close enough and that z global network of cooperation has not yet been formed. Therefore, worldwide cooperation should be strengthened to promote resource sharing and the openness of relevant mechanisms. [ABSTRACT FROM AUTHOR]

The use of deinking paper sludge (DPS) as a fertilizer instead of sending it to landfill could play a role in reducing greenhouse gases and improving soil properties. The objectives of this study were (1) to evaluate the changes in the physical (permeability and structural stability), chemical (particularly soil pH), and biological (microbial metabolic quotient (qCO2), microbial biomass soil CO2 emissions) of a calcareous agricultural soil following two successive annual amendments with three treatments (0, 30, and 60 Mg DPS ha−1—control, DPS30, and DPS60, respectively); and (2) to determine whether the addition of N-fertilizer to these treatments (controlF, DPS30F, and DPS60F, respectively) causes changes to soil fertility. The DPS application increased soil organic matter (+0.80%: DPS60 vs. control; and +0.35%: controlF vs. DPS60F), available phosphorus (+23.14 mg kg−1: DPS60 vs. control; and +14.34 mg kg−1: DPS60F vs. controlF), potassium (+0.6 g kg−1: controlF vs. DPS30F), and calcium (+0.28 g kg−1: DPS60 vs. control). The 60 Mg DPS ha−1 rate improved permeability and structural stability, regardless of the presence or absence of N-fertilizer. On the other hand, the 60 Mg DPS ha−1 rate without N-fertilizer lead to a decrease in total mineralization rate and qCO2, thereby indicating a reduction in CO2 emissions. The rate of 60 Mg ha−1 DPS could be effectively used to enhance the permeability and stability (soil restoration) and mitigate CO2 emissions, whereas the 30 Mg ha−1 rate could be used as fertilizer to improve the fertility of calcareous soils. [ABSTRACT FROM AUTHOR]

Potato diseases and pests have a serious impact on the quality and yield of potatoes, and timely prevention and control of potato diseases and pests is essential. A rich knowledge reserve of potato diseases and pests is one of the most important prevention and control measures; however, valuable knowledge is buried in the massive data of potato diseases and pests, making it difficult for potato growers and managers to obtain and use it in a timely manner and to develop the potential of knowledge. Therefore, this paper explores the construction method of a knowledge graph for automatic knowledge extraction, which extracts the knowledge of potato diseases and pests scattered in heterogeneous data from multiple sources, organises it into a semantically related knowledge base, and provides potato growers with professional knowledge and timely guidance to effectively prevent and control potato diseases and pests. In this paper, a data corpus on potato diseases and pests, called PotatoRE, is first constructed. Then, a model of ALBert-BiLSTM-Self_Att-CRF is designed to extract knowledge from the corpus to form a triplet structure, which is imported into the Neo4j graph database for storage and visualisation. Furthermore, the performance of the model constructed in this paper is compared and verified using the datasets PotatoRE and People's Daily. The results show that compared to the SOTA models of ALBert BiLSTM-CRF and ALBert BiGRU-CRF, the accuracy of our model has been improved by 2.92% and 3.12%, respectively, using PotatoRE. Compared to the Bert BiLSTM-CRF model on two datasets, our model not only improves the accuracy, recall, and F1 values, but also has a higher efficiency. The model in this paper solves the problem of the difficult recognition of nested entities. On this basis, through comparative experiments, the TransH model is used to effectively represent the constructed knowledge graph, which lays the foundation for achieving inference, extension, and automatic updating of the knowledge base. The achievements of the thesis have made certain contributions to the automatic construction of large-scale knowledge bases. [ABSTRACT FROM AUTHOR]

Blueberries have high nutritional and economic value and are easy to cultivate, so they are common fruit crops in China. There is a high demand for blueberry in domestic and foreign markets, and various technologies have been used to extend the supply cycle of blueberry to about 7 months. However, blueberry grows in clusters, and a cluster of fruits generally contains fruits of different degrees of maturity, which leads to low efficiency in manually picking mature fruits, and at the same time wastes a lot of manpower and material resources. Therefore, in order to improve picking efficiency, it is necessary to adopt an automated harvesting mode. However, an accurate maturity detection model can provide a prerequisite for automated harvesting technology. Therefore, this paper proposes a blueberry ripeness detection model based on enhanced detail feature and content-aware reassembly. First of all, this paper designs an EDFM (Enhanced Detail Feature Module) that improves the ability of detail feature extraction so that the model focuses on important features such as blueberry color and texture, which improves the model's ability to extract blueberry features. Second, by adding the RFB (Receptive Field Block) module to the model, the lack of the model in terms of receptive field can be improved, and the calculation amount of the model can be reduced at the same time. Then, by using the Space-to-depth operation to redesign the MP (MaxPool) module, a new MP-S (MaxPool–Space to depth) module is obtained, which can effectively learn more feature information. Finally, an efficient upsampling method, the CARAFE (Content-Aware Reassembly of Features) module, is used, which can aggregate contextual information within a larger receptive field to improve the detection performance of the model. In order to verify the effectiveness of the method proposed in this paper, experiments were carried out on the self-made dataset "Blueberry—Five Datasets" which consists of data on five different maturity levels of blueberry with a total of 10,000 images. Experimental results show that the mAP (mean average precision) of the proposed network reaches 80.7%, which is 3.2% higher than that of the original network, and has better performance than other existing target detection network models. The proposed model can meet the needs of automatic blueberry picking. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of the influence of climatic characteristics on the rating of land suitability for agricultural use. Soil fertility is one of the most important factors in land productivity and crop capacity; it is a complex value that depends not only on agrophysical and agrochemical soil properties but also on other natural factors, such as climate. There are different methodical approaches for a quantitative assessment of fertility level. The objectives of the research were to understand whether the distributions of active temperature sums and annual precipitation sums have a significant effect on the spatial and temporal heterogeneity of the rating assessment of land suitability for agricultural use in the example of the Leningrad region. The estimation and comparison between Semenov–Blagovidov's method of quality land estimation and Karmanov's method of appraisal of soils are given in this article. Karmanov's method is highlighted in this paper for its ability to assess soil's ecological indices more effectively than traditional methods. The research suggested that climate change may lead to increased variability in soil quality, with potential benefits for agriculture under certain climate scenarios, but at the same time, excessive temperatures in summer and precipitations might become a limiting factor, pushing down yields. The results of such assessment show that the performed calculation models can be used to forecast crop yields for future periods. [ABSTRACT FROM AUTHOR]

The global agriculture industry is encountering challenges due to labor shortages and the demand for increased efficiency. Currently, fruit yield estimation in guava orchards primarily depends on manual counting. Machine vision is an essential technology for enabling automatic yield estimation in guava production. To address the detection of guava in complex natural environments, this paper proposes an improved lightweight and efficient detection model, V-YOLO (VanillaNet-YOLO). By utilizing the more lightweight and efficient VanillaNet as the backbone network and modifying the head part of the model, we enhance detection accuracy, reduce the number of model parameters, and improve detection speed. Experimental results demonstrate that V-YOLO and YOLOv10n achieve the same mean average precision (mAP) of 95.0%, but V-YOLO uses only 43.2% of the parameters required by YOLOv10n, performs calculations at 41.4% of the computational cost, and exhibits a detection speed that is 2.67 times that of YOLOv10n. These findings indicate that V-YOLO can be employed for rapid detection and counting of guava, providing an effective method for visually estimating fruit yield in guava orchards. [ABSTRACT FROM AUTHOR]

The rapid and accurate detection of Chinese flowering cabbage ripeness and the counting of Chinese flowering cabbage are fundamental for timely harvesting, yield prediction, and field management. The complexity of the existing model structures somewhat hinders the application of recognition models in harvesting machines. Therefore, this paper proposes the lightweight Cabbage-YOLO model. First, the YOLOv8-n feature pyramid structure is adjusted to effectively utilize the target's spatial structure information as well as compress the model in size. Second, the RVB-EMA module is introduced as a necking optimization mechanism to mitigate the interference of shallow noise in the high-resolution sounding layer and at the same time to reduce the number of parameters in this model. In addition, the head uses an independently designed lightweight PCDetect detection head, which enhances the computational efficiency of the model. Subsequently, the neck utilizes a lightweight DySample upsampling operator to capture and preserve underlying semantic information. Finally, the attention mechanism SimAm is inserted before SPPF for an enhanced ability to capture foreground features. The improved Cabbage-YOLO is integrated with the Byte Tracker to track and count Chinese flowering cabbage in video sequences. The average detection accuracy of Cabbage-YOLO can reach 86.4%. Compared with the original model YOLOv8-n, its FLOPs, the its number of parameters, and the size of its weights are decreased by about 35.9%, 47.2%, and 45.2%, respectively, and its average detection precision is improved by 1.9% with an FPS of 107.8. In addition, the integrated Cabbage-YOLO with the Byte Tracker can also effectively track and count the detected objects. The Cabbage-YOLO model boasts higher accuracy, smaller size, and a clear advantage in lightweight deployment. Overall, the improved lightweight model can provide effective technical support for promoting intelligent management and harvesting decisions of Chinese flowering cabbage. [ABSTRACT FROM AUTHOR]

Sensors are very import parts of the IoT (Internet of Things). To detect the cleaning loss of combine harvesters during operation, a new detection method based on acoustic signal was proposed. The simulation models of the impact among grain, stem and plate were established by using the ALE algorithm, and the vibration excitation characteristics of the grain and stem were investigated; the results showed that both grain and stem signal were generated by surface vibration's excitation of the air, and the highest amplitudes of grain and stem signal were 15 kHz and 7 kHz respectively, which made it possible to distinguish grain and stem impact acoustic signals. Therefore, this paper proposed a novel method to separate the grain and stem signals by identifying the characteristic frequency area and subsequently an improved EMD denoising method based on an autocorrelation function was proposed. The critical stratum of EMD was identified based on the autocorrelation value of the eigenmode function, and the improved de-noising method was able to effectively remove the noise and the stem signal. To verify the feasibility of the improved denoising method, a field wheat harvesting experiment was conducted to acquire the actual acoustic signal of the cleaning operation. Data processing results showed that the average error of the comparison between the detected loss amount and the actual loss amount was 6.1%. This paper presents a novel approach for researching combine harvester cleaning loss detection and denoising methods. [ABSTRACT FROM AUTHOR]

Human activities, such as mining, industrialization, industrial waste emissions, and agricultural practices, have caused heavy metals to become widespread and excessively accumulated in soil. The high concentrations of heavy metals in soil can be toxic to plants, severely affecting crop yield and quality. Moreover, these heavy metals can also enter the food chain, affecting animals and humans and leading to various serious illnesses. Selenium (Se) is not only an essential element for animals and humans but is also beneficial for plants, as it promotes their ability to respond actively to biotic and abiotic stresses. The global issue of Se deficiency in diets has made plants the primary source for human Se supplementation. This paper comprehensively reviews the effects of heavy metal stress on plant growth and development, physiological responses of plants to such stress, and the intracellular transport processes of heavy metals within plants. It particularly focuses on the mechanisms by which Se alleviates heavy metal stress in plants. Additionally, the study delves into how Se significantly enhances plant tolerance mechanisms against typical heavy metals, such as cadmium (Cd), lead (Pb), and mercury (Hg). This integrative research not only expands the boundaries of research in the field of plant heavy metal stress and Se application but also provides new perspectives and solutions for understanding and addressing complex environmental heavy metal pollution issues. By integrating these aspects, this paper not only fills existing gaps in the literature but also offers comprehensive scientific basis and strategic recommendations for environmental protection and sustainable agriculture development. [ABSTRACT FROM AUTHOR]

A precision agriculture approach that uses drones for crop protection and variable rate application has become the main method of rice weed control, but it suffers from excessive spraying issues, which can pollute soil and water environments and harm ecosystems. This study proposes a method to generate variable spray prescription maps based on the actual distribution of weeds in rice fields and utilize DJI plant protection UAVs to perform automatic variable spraying operations according to the prescription maps, achieving precise pesticide application. We first construct the YOLOv8n DT model by transferring the "knowledge features" learned by the larger YOLOv8l model with strong feature extraction capabilities to the smaller YOLOv8n model through knowledge distillation. We use this model to identify weeds in the field and generate an actual distribution map of rice field weeds based on the recognition results. The number of weeds in each experimental plot is counted, and the specific amount of pesticide for each plot is determined based on the amount of weeds and the spraying strategy proposed in this study. Variable spray prescription maps are then generated accordingly. DJI plant protection UAVs are used to perform automatic variable spraying operations based on prescription maps. Water-sensitive papers are used to collect droplets during the automatic variable operation process of UAVs, and the variable spraying effect is evaluated through droplet analysis. YOLOv8n-DT improved the accuracy of the model by 3.1% while keeping the model parameters constant, and the accuracy of identifying weeds in rice fields reached 0.82, which is close to the accuracy of the teacher network. Compared to the traditional extensive spraying method, the approach in this study saves approximately 15.28% of herbicides. This study demonstrates a complete workflow from UAV image acquisition to the evaluation of the variable spraying effect of plant protection UAVs. The method proposed in this research may provide an effective solution to balance the use of chemical herbicides and protect ecological safety. [ABSTRACT FROM AUTHOR]

Crop diseases and pests are important factors restricting agricultural production. Traditional pest detection methods are mainly targeted at a single pest species, which is difficult to meet the needs of multi-target identification and rapid response in real scenes. Therefore, this paper improves the YOLOv8n model for efficient multi-target pest detection. Two feature enhancement modules, EMSFEM and AFEM_SIE, are proposed in this paper. The EMSFEM module enriches the model's receptive field through the combination of multi-scale asymmetric convolution kernel and different expansion rates and can better extract the width, height, texture, and edge information of the target. The AFEM_SIE module captures the similarities and differences between upper and lower features through spatial information exchange and enhances feature representation through inter-feature information exchange. In addition, an improved feature fusion operation, Concat_Weighting, is proposed on the basis of Concat. The module uses the learned weights to carry out channel weighting and feature graph weighting for input features, which realizes more flexible and effective feature fusion. The results of experiments conducted on the publicly available large-scale crop pest and disease dataset IP102 show that the performance of the AEC-YOLOv8n model is significantly improved compared with the original YOLOv8n model, with mAP50 increased by 8.9%, accuracy increased by 6.8%, and recall rate increased by 6.3%. The AEC-YOLOv8n model proposed in this study can effectively identify and deal with a variety of crop pests and has achieved the best detection accuracy on the IP102 dataset, which has high application value. [ABSTRACT FROM AUTHOR]

The high cost of manual weed control and the overuse of herbicides restrict the yield and quality of soybean. Intelligent mechanical weeding and precise application of pesticides can be used as effective alternatives for weed control in the field, and these require accurate distinction between crops and weeds. In this paper, images of soybean seedlings and weeds in different growth areas are used as datasets. In the aspect of soybean recognition, this paper designs a YOLOv8nGP algorithm with a backbone network optimisation based on GhostNet and an unconstrained pruning method with a 60% pruning rate. Compared with the original YOLOv8n, the YOLOv8nGP improves the Precision (P), Recall (R), and F1 metrics by 1.1% each, reduces the model size by 3.6 mb, and the inference time was 2.2 ms, which could meet the real-time requirements of field operations. In terms of weed recognition, this study utilises an image segmentation method based on the Normalized Excess Green Index (NExG). After filtering the soybean seedlings, the green parts of the image are extracted for weed recognition, which reduces the dependence on the diversity of the weed datasets. This study combines deep learning with traditional algorithms, which provides a new solution for weed recognition of soybean seedlings. [ABSTRACT FROM AUTHOR]

Droplet collectors are commonly utilized to gauge the effect of pesticide deposition on crops. However, the varying surface characteristics of these collectors can lead to disparate data outcomes. Notably, water-sensitive paper is limited in humid environments, hindering rapid droplet deposition evaluation. Consequently, the selection of appropriate droplet collectors based on the environmental conditions is imperative. This study involved the use of five typical droplet collectors to establish a method for the swift and accurate evaluation of spray effectiveness, employing various spray liquids. It was observed that the surface free energy of five widely used droplet collectors was measured as follows: 35.11 mN m−1 for semigloss paper, 33.81 mN m−1 for coated paper laminated with polyvinyl chloride, 48.38 mN m−1 for kromekote paper (KP), 33.90 mN m−1 for polyvinyl chloride cards, and 39.95 mN m−1 for water-sensitive paper. When comparing the outcomes of deposition tests across these five collectors, it was noted that the results pertaining to droplet density were minimally influenced by the surface properties of the collectors with droplet coverage following. The volume of deposition was found to be the most susceptible to the surface characteristics of the collectors. Therefore, in the context of collecting and processing droplets, prioritizing droplet density as the metric for evaluation proved to be more reliable than using the other indicators. [ABSTRACT FROM AUTHOR]

Using nanoparticles (NPs) to effectively mitigate the negative effects of abiotic stressors on plant growth and development will help to achieve sustainable agriculture. Although there have been some prior reviews in this area, bibliometrics is still lacking. In this study, the most productive nations or regions, journals, publishers, and scholars in this field were identified using an objective bibliometric examination of the pertinent material published in the Web of Science core database. To dig deeper for information, the keywords co-occurrence, collaboration network of countries/regions and authors, and network map of highly cited papers citation are used to characterize present advances and forecast potential future trends. The results show a growing interest in using nanoparticles to alleviate abiotic stressors. There has been an exponential increase in the number of relevant papers and citations. Moreover, Asian countries are the most active in this subject, even if the USA generates papers with the best overall quality. The most common publishers and journals in this area are Elsevier and Environmental Science-Nano, while Wu HH is currently the most active author. Even though many researchers have formed close partnerships, there is not yet a large collaborative group of researchers in this field. Gaps in the current scientific literature are highlighted, such as the lack of use of omics, especially multi-omics, to provide a more in-depth and comprehensive explanation of the molecular mechanisms by which NPs enhance plant resistance to abiotic stresses. This bibliometric study will provide a valuable reference for studying the evolution of the field and identifying research frontiers. [ABSTRACT FROM AUTHOR]

Black polyethylene (PE) is the most common mulching material used in horticultural crops in the world but its use represents a very serious environmental problem. Biodegradable films and paper mulches are available alternatives but farmers are reluctant to adopt them because of their high market prices. The aim of this paper is to evaluate the economic profitability of eight biodegradable mulching materials available for open-air pepper production. The economic evaluation is based on a four-year trial located in a semi-arid region of Spain. Three scenarios of PE waste management are examined: (i) absence of residues management, (ii) landfill accumulation, and (iii) total recycling. The inclusion of the costs of waste management and recycling under the current Spanish legislation only reduced the final net margin by 0.2%. The results show that an increase in subsidy rates of up to 50.1% on the market price would allow all biodegradable films to be economic alternatives to PE. The study supports the mandatory measures for the farmers to assume the costs of waste management and recycling. Despite savings in field conditioning costs, high market prices of biodegradable materials and papers are not compensated by the current level of subsidies, hampering their adoption in the fields. [ABSTRACT FROM AUTHOR]

Cellulolytic enzymes produced by spore-forming bacteria seem to be a potential solution to the degradation of lignocellulosic waste. In this study, several dozen bacterial spore-forming strains were isolated from soil and one of them was selected for further studies. The studied bacterial strain was identified to genus Bacillus (strain 8E1A) by 16S rRNA gene sequencing. Bacillus sp. 8E1A showed an activity of carboxymethyl cellulase (CMCase) with visualization with Congo Red-25 mm (size of clear zone). To study CMCase, filter paper hydrolase (FPase), and microcrystalline cellulose Avicel hydrolase (Avicelase) production, three different cellulose sources were used for bacterial strain cultivation: carboxymethyl cellulose (CMC), filter paper (FP), and microcrystalline cellulose Avicel. The highest CMCase (0.617 U mL−1), FPase (0.903 U mL−1), and Avicelase (0.645 U mL−1) production of Bacillus sp. 8E1A was noted for using CMC (after 216 h of incubation), Avicel cellulose (after 144 h of incubation), and CMC (after 144 h of incubation), respectively. Subsequently, the cellulases' activity was measured at various temperatures and pH values. The optimal temperature for CMCase (0.535 U mL−1) and Avicelase (0.666 U mL−1) activity was 70 °C. However, the highest FPase (0.868 U mL−1) activity was recorded at 60 °C. The highest CMCase and Avicelase activity was recorded at pH 7.0 (0.520 and 0.507 U mL−1, respectively), and the optimum activity of FPase was noted at pH 6.0 (0.895 U mL−1). These results indicate that the cellulases produced by the Bacillus sp. 8E1A may conceivably be used for lignocellulosic waste degradation in industrial conditions. [ABSTRACT FROM AUTHOR]

In the context of sustainability, the integration of renewable energy into industrial processes not only minimizes dependence on fossil fuels but also contributes to the efficient management of industrial waste. By transforming organic waste, including agri-food and urban waste, into biogas, green energy can be generated, thus reducing the impact on the environment and closing the loop of material used in the economic circuit. Thus, a sustainable system can be promoted, where resources are continuously reused and exploited. Statistical methods and a decision tree with the Classification and Regression Trees (CRT) algorithm were employed to analyze data. The paper focuses on the importance of industrial waste and biogas for the generation, transformation, and consumption of energy in the EU (European Union)-27 countries. To provide a thorough analysis, we have divided these countries based on real gross domestic product (GDP) per capita, grouping them above/below the annual average for the period 2012–2021/2022. Descriptive statistics revealed observable differences between the two groups, but the paper aimed to provide evidence regarding the existence of these differences as statistically significant. Using the Kolmogorov–Smirnov test, the non-normal distribution of the data was confirmed, requiring non-parametric inferential methods. The Mann–Whitney U test revealed statistically significant differences between the two groups for all the studied variables. This comprehensive approach highlights the distinct energy-related characteristics influenced by economic development in the EU-27. [ABSTRACT FROM AUTHOR]

This paper proposes PestNet, a lightweight method for classifying crop pests, which improves upon MobileNet-V2 to address the high model complexity and low classification accuracy commonly found in pest classification research. Firstly, the training phase employs the AdamW optimizer and mixup data augmentation techniques to enhance the model's convergence and generalization capabilities. Secondly, the Adaptive Spatial Group-Wise Enhanced (ASGE) attention mechanism is introduced and integrated into the inverted residual blocks of the MobileNet-V2 model, boosting the model's ability to extract both local and global pest information. Additionally, a dual-branch feature fusion module is developed using convolutional kernels of varying sizes to enhance classification performance for pests of different scales under real-world conditions. Lastly, the model's activation function and overall architecture are optimized to reduce complexity. Experimental results on a proprietary pest dataset show that PestNet achieves classification accuracy and an F1 score of 87.62% and 86.90%, respectively, marking improvements of 4.20 percentage points and 5.86 percentage points over the baseline model. Moreover, PestNet's parameter count and floating-point operations are reduced by 14.10% and 37.50%, respectively, compared to the baseline model. When compared with ResNet-50, MobileNet V3-Large, and EfficientNet-B1, PestNet offers superior parameter efficiency and floating-point operation requirements, as well as improved pest classification accuracy. [ABSTRACT FROM AUTHOR]

Pests have caused significant losses to agriculture, greatly increasing the detection of pests in the planting process and the cost of pest management in the early stages. At this time, advances in computer vision and deep learning for the detection of pests appearing in the crop open the door to the application of target detection algorithms that can greatly improve the efficiency of tomato pest detection and play an important technical role in the realization of the intelligent planting of tomatoes. However, in the natural environment, tomato leaf pests are small in size, large in similarity, and large in environmental variability, and this type of situation can lead to greater detection difficulty. Aiming at the above problems, a network target detection model based on deep learning, YOLONDD, is proposed in this paper. Designing a new loss function, NMIoU (Normalized Wasserstein Distance with Mean Pairwise Distance Intersection over Union), which improves the ability of anomaly processing, improves the model's ability to detect and identify objects of different scales, and improves the robustness to scale changes; Adding a Dynamic head (DyHead) with an attention mechanism will improve the detection ability of targets at different scales, reduce the number of computations and parameters, improve the accuracy of target detection, enhance the overall performance of the model, and accelerate the training process. Adding decoupled head to Head can effectively reduce the number of parameters and computational complexity and enhance the model's generalization ability and robustness. The experimental results show that the average accuracy of YOLONDD can reach 90.1%, which is 3.33% higher than the original YOLOv5 algorithm and is better than SSD, Faster R-CNN, YOLOv7, YOLOv8, RetinaNet, and other target detection networks, and it can be more efficiently and accurately utilized in tomato leaf pest detection. [ABSTRACT FROM AUTHOR]

The accurate identification of crop aphids is an important aspect of improving agricultural productivity. Aphids are characterised by small targets and a body colour similar to their surroundings. Even the most advanced detectors can experience problems such as low detection accuracy and a high number of missed detections. In this paper, a multi-stream target detection model is proposed for fast and accurate detection of crop aphids in complex backgrounds. First, inspired by the human visual system, we propose a bionic attention (BA) approach. Unlike previous strategies, we do not improve the model but input additional category labels as bionic information streams into the network at the network input stage to support mainstream recognition, which improves the detection effect. In addition, through web crawling and manual screening, we construct an aphid dataset containing 2059 images, named IP_Aphids, based on IP102. In this paper, we combine the proposed BA with a number of classical target detection models, including YOLOv5s, YOLOv7-tiny, YOLOv8n, SSD, and faster R-CNN, and we also compare the BA with the classical attention mechanisms CBAM and SE. This approach allows the effectiveness of the method to be evaluated from multiple perspectives. The results show that combining the bionic information flow of BA with the original mainstream information flow as inputs to the five target detection models improves the mAP by 2.2%, 7%, 2.7%, 1.78%, and 1.12%, respectively, compared to the models using only the original inputs. In addition, the mAP of the YOLOv8n_BA model is higher than that of the YOLOv8n, YOLOv8n_CBAM, and YOLOv8n_SE models by 4.6%, 3.3%, and 2.7%, respectively. This indicates that the BA proposed in this study is significantly better than the classical attention to improve crop aphid detection, which provides a reference for crop aphid-related research. [ABSTRACT FROM AUTHOR]

The intelligent control of precision planting can detect and regulate the operation quality of the planter in real time, which plays an important role in improving the operation quality of the planter and the yield of the corn. In this paper, the control system of a corn precision planter is designed to realize the operating quality monitoring and electric driving of the seed-metering device. The planting quality is calculated by the time interval between the neighboring falling seeds, instead of the plant spacing, to improve the operational efficiency of the system. At the same time, the forward speed of the planter is obtained by radar, which is used to accurately match the speed of the seed-metering device with the forward speed of the planter. The velocity error of the radar is analyzed, and the relevant relationship of the radar output frequency and forward speed is established. Comparative test results of this system and the JPS-12 test bench show that the detection performance of the system is reliable, and the maximum detection error of the quality parameters is less than 2.88%. Field experiments were carried out to verify the operational performance of the control system. Two speed sensors, radar and GPS, were chosen to study the effect of speed measuring on the performance of the control system. We found that speed measuring has a significant effect on planting performance. The qualified parameters of radar were significantly higher than those of GPS, at a forward speed of 6–12 km/h. The qualification feeding index (QFI) of radar was 0.51%, 0.67%, and 2.05% higher than that of GPS at speeds of 6, 8, 10, and 12 km/h. The precision index (PREC) of radar was 17.60%, 5.44%, 16.81%, and 17.30% lower than that of GPS. Therefore, the control system based on the radar speed measurement developed in this paper can significantly improve the operating quality of the planter. [ABSTRACT FROM AUTHOR]

The kale crop is an important bulk vegetable, and automatic segmentation to recognize kale is fundamental for effective field management. However, complex backgrounds and texture-rich edge details make fine segmentation of kale difficult. To this end, we constructed a kale dataset in a real field scenario and proposed an UperNet semantic segmentation model with a Swin transformer as the backbone network and improved the model according to the growth characteristics of kale. Firstly, a channel attention module (CAM) is introduced into the Swin transformer module to improve the representation ability of the network and enhance the extraction of kale outer leaf and leaf bulb information; secondly, the extraction accuracy of kale target edges is improved in the decoding part by designing an attention refinement module (ARM); lastly, the uneven distribution of classes is solved by modifying the optimizer and loss function to solve the class distribution problem. The experimental results show that the improved model in this paper has excellent performance in feature extraction, and the average intersection and merger ratio (mIOU) of the improved kale segmentation can be up to 91.2%, and the average pixel accuracy (mPA) can be up to 95.2%, which is 2.1 percentage points and 4.7 percentage points higher than the original UperNet model, respectively, and it effectively improves the segmentation recognition of kale. [ABSTRACT FROM AUTHOR]

The article discusses the importance of crop tolerance to both abiotic and biotic stresses in agricultural research. Abiotic stresses include environmental factors like drought and extreme temperatures, while biotic stresses involve pests, diseases, and pathogens. Understanding and enhancing crop tolerance to these stresses is crucial for global food security and sustainable crop production. The article highlights various research papers from different countries that focus on topics such as abiotic stress (salinity, drought) and biotic stress (pests, diseases). The research papers explore different strategies, including breeding and genetic engineering, to enhance crop tolerance. [Extracted from the article]

Shiitake mushrooms are an important edible fungus, and their nutrient content is related to their quality. With the acceleration of urbanization, there has been a serious loss of population and shortage of labor in rural areas. The problem of harvesting agricultural products after maturity is becoming more and more prominent. In recent years, deep learning techniques have performed well in classification tasks using image data. These techniques can replace the manual labor needed to classify the quality of shiitake mushrooms quickly and accurately. Therefore, in this paper, a MobileNetV3_large deep convolutional network is improved, and a mushroom quality classification model using images with complex backgrounds is proposed. First, captured image data of shiitake mushrooms are divided into three categories based on the appearance characteristics related to shiitake quality. By constructing a hybrid data set, the model's focus on shiitake mushrooms in images with complex backgrounds is improved. And the constructed data set is expanded using data enhancement methods to improve the generalization ability of the model. The total number of images after expansion is 10,991. Among them, the number of primary mushroom images is 3758, the number of secondary mushroom images is 3678, and the number of tertiary mushroom images is 3555. Subsequently, the SE module in MobileNetV3_large network is improved and processed to enhance the model recognition accuracy while reducing the network size. Finally, PolyFocalLoss and migration learning strategies are introduced to train the model and accelerate model convergence. In this paper, the recognition performance of the improved MobileNetV3_large model is evaluated by using the confusion matrix evaluation tool. It is also compared with other deep convolutional network models such as VGG16, GoogLeNet, ResNet50, MobileNet, ShuffleNet, and EfficientNet using the same experimental conditions. The results show that the improved MobileNetV3_large network has a recognition accuracy of 99.91%, a model size of 11.9 M, and a recognition error rate of 0.09% by the above methods. Compared to the original model, the recognition accuracy of the improved model is increased by 18.81% and the size is reduced by 26.54%. The improved MobileNetV3_large network model in this paper has better comprehensive performance, and it can provide a reference for the development of quality recognition and classification technologies for shiitake mushrooms cultivated in greenhouse environments. [ABSTRACT FROM AUTHOR]

The yield and quality of rice are closely related to field management. The automatic identification of field abnormalities, such as diseases and pests, based on computer vision currently mainly relies on high spatial resolution (HSR) images obtained through manual field inspection. In order to achieve automatic and efficient acquisition of HSR images, based on the capability of high-throughput field inspection of UAV remote sensing and combining the advantages of high-flying efficiency and low-flying resolution, this paper proposes a method of "far-view and close-look" autonomous field inspection by unmanned aerial vehicle (UAV) to acquire HSR images of abnormal areas in the rice canopy. First, the UAV equipped with a multispectral camera flies high to scan the whole field efficiently and obtain multispectral images. Secondly, abnormal areas (namely areas with poor growth) are identified from the multispectral images, and then the geographical locations of identified areas are positioned with a single-image method instead of the most used method of reconstruction, sacrificing part of positioning accuracy for efficiency. Finally, the optimal path for traversing abnormal areas is planned through the nearest-neighbor algorithm, and then the UAV equipped with a visible light camera flies low to capture HSR images of abnormal areas along the planned path, thereby acquiring the "close-look" features of the rice canopy. The experimental results demonstrate that the proposed method can identify abnormal areas, including diseases and pests, lack of seedlings, lodging, etc. The average absolute error (AAE) of single-image positioning is 13.2 cm, which can meet the accuracy requirements of the application in this paper. Additionally, the efficiency is greatly improved compared to reconstruction positioning. The ground sampling distance (GSD) of the acquired HSR image can reach 0.027 cm/pixel, or even smaller, which can meet the resolution requirements of even leaf-scale deep-learning classification. The HSR image can provide high-quality data for subsequent automatic identification of field abnormalities such as diseases and pests, thereby offering technical support for the realization of the UAV-based automatic rice field inspection system. The proposed method can also provide references for the automatic field management of other crops, such as wheat. [ABSTRACT FROM AUTHOR]

The increasing industrial pulp production has led to a negative growth of the associated solid wastes, thus making necessary alternative ways of handling them in suitable sanitary landfills to minimize adverse effects on the environment and well-being of people. Solid waste treatment prior to its disposal is a target to minimize pollution of the natural resources (air, soil, water) due to accidental leaching. This paper aims to determine better experimental conditions in the container to develop an optimal composting design for pulp solid wastes. For this, an experimental methodology is introduced. This paper presents the results about the influence of independent control variables (grits addition and composting process time) on dependent variables (chemical and biological), for which a composting design was used, and a face-centered central composite factor was applied. The results showed mature compost over 60-day treatment, with the following experimental observations (i) the grits addition did not decrease the pH in the first stage of the composting process; and (ii) the microbial activities were high during the active stage of the composting progress and evolved to stable, lower values together with a proper trend of N–NH4+ and N–NO3− at the end. Grits addition of around 6% is the optimal experimental amount to use for the composting process of the secondary sludge from the Kraft mill industry. In conclusion, treating secondary sludges and grit residues from the Kraft mill industry to produce compost is feasible and sustainable. This action reduces the environmental pollution risk (evidenced by soil pH change and possible water pollution) and improves the soil assimilation capability of inorganic micronutrients and organic compounds after application. Thus, the controlled waste reuse will pass from a negative input to the environment to a positive, sustainable solution, which can be used as a soil-nutrient improver in agriculture. [ABSTRACT FROM AUTHOR]

Citrus are the most produced fruits worldwide. It is expected that these crops will be severely affected by climate change. However, the literature lacks works that attempt to understand the influence of climate change on citrus. For this reason, a bibliometric analysis was conducted on climate change and citrus to investigate its development and current trends in this research domain. The main trends, keywords, and their relations were identified. The period from 1992 to 2022 was analyzed, resulting in 178 documents in the Scopus database. The most significant publishers' countries were also the largest citrus producers in the world besides being G7 members. Three main research areas were identified: modeling, socio-political issues, and plant physiology. A tendency to change interest from modeling and risk analysis to physiology and stress studies was observed. Additionally, some of the most cited papers observed the positive impacts of climate change on certain citrus crops. Despite the multidisciplinary publications, two main gaps were identified: (i) the lack of investigations with combined stresses (abiotic and biotic) instead of isolated studies, and (ii) the lack of studies of predictive models for citrus production in different conditions and climate change scenarios. Finally, there was a tendency toward studying water use and irrigation alternatives due to water scarcity and management solutions to improve the production system's resilience, considering the potential impacts of climate change. [ABSTRACT FROM AUTHOR]

Maize diseases are reported to occur often, and are complicated and difficult to control, which seriously affects the yield and quality of maize. This paper proposes an improved YOLOv5n model incorporating a CA (Coordinate Attention) mechanism and STR (Swin Transformer) detection head, CTR_YOLOv5n, to identify common maize leaf spot, gray spot, and rust diseases in mobile applications. Based on the lightweight model YOLOv5n, the accuracy of the model is improved by adding a CA attention module, and the global information acquisition capability is enhanced by using TR2 as the detection head. The average recognition accuracy of the algorithm model can reach 95.2%, which is 2.8 percent higher than the original model, and the memory size is reduced to 5.1MB compared to 92.9MB of YOLOv5l, which is 94.5% smaller and meets the requirement of being light weight. Compared with SE, CBAM, and ECA, which are the mainstream attention mechanisms, the recognition effect we used is better and the accuracy is higher, achieving fast and accurate recognition of maize leaf diseases with fewer computational resources, providing new ideas and methods for real-time recognition of maize and other crop spots in mobile applications. [ABSTRACT FROM AUTHOR]

Irrigation, as one of the most impactful human interventions in the terrestrial water cycle, has been arousing great attention due to research on the impacts of its interaction with climate. In this paper, we used a scientometric analysis method to explore the overall publication output of the climatic effects of irrigation (CEI) field from the Web of Science Core Collection (WSCC) database, covering the time period from 1993 to 2022. And, through a visual scientific citation analysis tool, CiteSpace, we studied the knowledge structure, disciplinary trajectory, frontier hotspots, and academic impacts in the field of CEI. Using topic screening, 2919 publications related to irrigation climate were searched. CEI research has gone through the knowledge germination stage (1993–2005), knowledge accretion stage (2006–2012), and the knowledge prosperity stage (2013–2022), respectively. Ecology, earth, and marine are the most influential disciplines of research in this field, and they are influenced by earth, geology, geophysics and plant, ecology, zoology. AWM and SOTTE are the most popular journals currently. The academic impacts of scientific stakeholders are uneven. European and American countries have profound influence in the research field. The keyword of "Climate change" is the turning point in the co-word analysis network, and research hotspots focus on "carbon dioxide", "model", "climate", "growth", "temperature", "biomass", "global warming", "CO2", "global change", "dynamics", "adjustments", and "atmospheric CO2". The knowledge base of the CEI field can be divided into 14 clusters, such as cotton production, semi-arid condition, and irrigation water supply, and these three clusters are the three largest among them. This paper offers a comprehensive scientometric review of CEI, and, to some degree, provides some reference for the relevant research on the climate effects of irrigation, which will be beneficial to understand the current research situation and development trend in this field, as well as provide state-of-the-art and future perspectives. [ABSTRACT FROM AUTHOR]

The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP3Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP3Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters. [ABSTRACT FROM AUTHOR]

The integration of Global Navigation Satellite System (GNSS) Real-Time Kinematics (RTK) can provide high-precision, real-time, and global coverage of location information in open areas. But in arboretum environment, the ability to achieve continuous high-precision positioning using global positioning technology is limited due to various sources of interference, such as multi-path effects, signal obstruction, and environmental noise. In order to achieve precise navigation in challenging GNSS signal environments, visual SLAM systems are widely used due to their ability to adapt to different environmental features. Therefore, this paper proposes an optimized solution that integrates the measurements from GNSS-RTK and stereo cameras. The presented approach aligns the coordinates between the two sensors, and then employs an adaptive sliding window approach, which dynamically adjusts the window size and optimizes the pose within the sliding window. At the same time, to address the variations and uncertainties of GNSS signals in non-ideal environments, this paper proposes a solution that utilizes a Gaussian Mixture Model (GMM) to model the potential noise in GNSS signals. Furthermore, it employs a Variational Bayesian Inference-based (VBI) method to estimate the parameters of the GMM model online. The integration of this model with an optimization-based approach enhances the positioning accuracy and robustness even further. The evaluation results of real vehicle tests show that in challenging GNSS arboretum environments, GMM applied to GNSS/VO integration has higher accuracy and better robustness. [ABSTRACT FROM AUTHOR]