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The growth of the global population, coupled with concomitant economic development, has resulted in the generation of a substantial quantity of waste. The transition of the European Union’s economy towards a closed-loop model is prompting a comprehensive search for waste management concepts across a range of industrial sectors. The objective of this study is to valorise deinking paper sludge, which has a high potential for soil formation due to its high organic matter content. To produce organic–mineral fertiliser, the deinking sludge was subjected to acid hydrolysis, then neutralised with KOH solution and enriched with poultry litter ash. The final products were characterised in terms of their nutrient and heavy metal content. The bioavailability of phosphorus, along with the forms in which it occurs in fertilisers, was determined through the implementation of a five-step fractionation procedure. Furthermore, an eight-week incubation period was conducted to assess the fertilisers’ performance in soil. Soil samples were tested on a weekly basis for pH, water-soluble and bioavailable phosphorus content using the spectroscopic method after previous extraction in water and Bray’s solution, and catalase activity using the titrimetric method. The resulting fertilisers were found to meet the requirements for organo-mineral fertilisers and were categorised as PK-type fertilisers with a total nutrient content of 24.6–39.3%. Fractionation studies demonstrated that the fertilisers contained 20–30% of the total potentially bioavailable phosphorus. Furthermore, the long-term release of phosphorus from the fertilisers was confirmed through incubation studies. Additionally, the fertilisers were observed to contribute to an increase in catalase activity in the soil.

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.

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.

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.

The growth of the global population, coupled with concomitant economic development, has resulted in the generation of a substantial quantity of waste. The transition of the European Union's economy towards a closed-loop model is prompting a comprehensive search for waste management concepts across a range of industrial sectors. The objective of this study is to valorise deinking paper sludge, which has a high potential for soil formation due to its high organic matter content. To produce organic–mineral fertiliser, the deinking sludge was subjected to acid hydrolysis, then neutralised with KOH solution and enriched with poultry litter ash. The final products were characterised in terms of their nutrient and heavy metal content. The bioavailability of phosphorus, along with the forms in which it occurs in fertilisers, was determined through the implementation of a five-step fractionation procedure. Furthermore, an eight-week incubation period was conducted to assess the fertilisers' performance in soil. Soil samples were tested on a weekly basis for pH, water-soluble and bioavailable phosphorus content using the spectroscopic method after previous extraction in water and Bray's solution, and catalase activity using the titrimetric method. The resulting fertilisers were found to meet the requirements for organo-mineral fertilisers and were categorised as PK-type fertilisers with a total nutrient content of 24.6–39.3%. Fractionation studies demonstrated that the fertilisers contained 20–30% of the total potentially bioavailable phosphorus. Furthermore, the long-term release of phosphorus from the fertilisers was confirmed through incubation studies. Additionally, the fertilisers were observed to contribute to an increase in catalase activity in the soil. [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.

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]

With increasing concentrations of atmospheric greenhouse gases, the interaction between climate change and agriculture is receiving widespread attention as part of food security and sustainable human development. However, a comprehensive summary of knowledge in the field of climate change and agriculture from a scientometric perspective is still lacking. Here, we selected 25,872 papers related to climate change and agriculture from the Web of Science Core Collection database for the period 1985 to 2023 and used VOSviewer software to reveal the research status and trends. The main results were as follows: (1) the number of papers in this field showed a rapidly increasing trend after 2007, with a clear interdisciplinary characteristic; (2) The United States was the most influential country in this field with 6819 papers and 363,109 citations. China had the second highest number of papers (3722 papers), but the Chinese Academy of Sciences was the most influential institution with the most papers. On an author level, Pete Smith was the most influential; (3) All keywords were divided into four different research topics, such as the impact of climate change on agriculture, climate change mitigation and adaptation in agriculture, and crop growth in response to climate change. Among them, some keywords related to climate change adaptation were the most recent topics of interest in this field. These findings provide effective scientific references for relevant scientists and practitioners to better conduct future theoretical and practical research on climate change and agriculture. [ABSTRACT FROM AUTHOR]

The improper treatment of crop straw not only leads to resource wastage but also adversely impacts the ecological environment. However, the application of microorganisms can accelerate the decomposition of crop straw and improve its utilization. In this study, cellulose-degrading microbial strains were isolated from naturally decayed corn straw and screened using Congo red staining, along with assessing variations in carboxymethyl cellulase (CMCase) activity, filter paper enzyme (FPase) activity and β-glucosidase (β-Gase) activity, as well as the degradation rate. The eight strains, namely Neurospora intermedia isolate 29 (A1), Streptomyces isolate FFJC33 (A2), Gibberella moniliformis isolate FKCB-009 (A3), Fusarium fujikuroi isolate EFS3(2) (A4), Fusarium Fujikuroi isolate FZ04 (A5), Lysine bacillus macroides strain LNHL43 (B1), Bacillus subtilis strain MPF30 (B2) and Paenibacilli lautus strain ALEB-P1 (C), were identified and selected for microbial strain consortium design based on their high activities of CMCase, FPase and β-Gase. The fungi, bacteria and actinomycete strains were combined without antagonistic effects on corn straw decomposition. The results showed the A2B2 combination had a significantly higher FPase at 55.44 U/mL and β-Gase at 25.73 U/mL than the other two strain combinations (p < 0.05). Additionally, the degradation rate of this combination was 40.33%, which was considerably higher than that of the other strains/consortia. The strain combination A4B2C also had superior enzyme activity, including CMCase with a value of 35.03 U/mL, FPase with a value of 63.59 U/mL and β-Gase with a value of 26.15 U/mL, which were significantly different to those of the other three strain combinations (p < 0.05). Furthermore, seven single microbial strains with high cellulase activities were selected to construct various microbial consortiums for in situ composting in order to evaluate their potential. Taken as a whole, the results of composting, including temperature, moisture content, pH, E4/E6 value and seed germination index, indicated that the microbial strain consortium consisting of Neurospora intermediate isolate 29, Fusarium fujikuroi isolate EFS3(2), Fusarium fujikuroi isolate FZ04, Lysinibacillus macrolides, Lysinibacillus sphaericus, Bacillus subtilis and Paenibacillus lautus was advantageous for corn straw decomposition and yielded high-quality compost. The screened flora was able to effectively degrade corn straw. This study provides a novel solution for the construction of a microbial consortium for the composting of corn straw. [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.

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.

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]

Considering the demand for the optimization of the technical factors of spraying for a greater area coverage and minimal drift, field tests were carried out to determine the interaction between the area coverage, number of droplets per cm2, droplet diameter, and drift. The studies were conducted with two different types of sprayers (axial and radial fan) in an apple orchard and a vineyard. The technical factors of the spraying interactions were nozzle type (ISO code 015, code 02, and code 03), working speed (6 and 8 km h−1), and spraying norm (250–400 L h−1). The airflow of both sprayers was adjusted to the plantation leaf mass and the working pressure was set for each repetition separately. A method using water-sensitive paper and a digital image analysis was used to collect data on coverage factors. The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). Nozzle type had the highest predictive value for the properties of number of droplets per cm2 (axial = 69.1%; radial = 66.0%), droplet diameter (axial = 30.6%; radial = 38.2%), and area coverage (axial = 24.6%; radial = 34.8%). Spraying norm had the greatest predictive value for area coverage (axial = 43.3%; radial = 26.9%) and drift (axial = 72.4%; radial = 62.3%). Greater coverage of the treated area and a greater number of droplets were achieved with the radial sprayer, as well as less drift. The accuracy of the machine learning model for the prediction of the treated surface showed a satisfactory accuracy for most properties (R2 = 0.694–0.984), except for the estimation of the droplet diameter for an axial sprayer (R2 = 0.437–0.503). [ABSTRACT FROM AUTHOR]

Appropriate organic amendment management is essential in potato production to increase soil productivity and potato (Solanum tuberosum L.) quality. The objectives of this two-year field study (2004–2005) were to evaluate the effects of organic amendment applications with or without mineral N fertilizer addition on potato yield, N uptake, N use efficiency (NUE), and on disease incidence. The experimental design was a split-plot, which included nine treatments with four different organic amendments applied in fall 2003 or in spring 2004 at a rate of 40 Mg ha−1 (wet basis) and an unamended control in main plots, and N fertilizer rates (0 and 90 kg N ha−1) in sub-plots. Organic amendments consisted of fresh cattle manure (FCM), composted cattle manure (CCM), paper mill sludge with C/N ratio 15 (PMS2) applied alone (0 kg N ha−1) or supplemented with mineral fertilizer at a rate 90 kg N ha−1. The N fertilizer rate in the unamended control consisted of 0 and 150 kg N ha−1. No organic amendments were applied in 2005 to evaluate residual effects. Fall and spring applications of FCM, CCM and PMS alone significantly increased N uptake and potato marketable yields by 2.5 to 16.4 Mg ha−1, compared to the unfertilized control. Combining organic amendments with N fertilizer at 90 kg N ha−1 increased potato yields, N uptake, and specific gravity, which were comparable to those obtained in mineral N fertilizer treatments (150 kg N ha−1). Residual effects of organic amendments alone had no significant effects on potato yields or on N uptake compared to the unfertilized control. Potato NUE for FCM, CCM and PMS ranged from 6% to 25% in the first year and from 2% to 8% in the residual year. The NUE values were higher for PMS with lower C/N ratio compared to FCM and CCM. This study did not show any difference between organic amendment applied in spring or fall on potato yield and quality. It was also demonstrated that mineral N fertilizer needed to be combined with organic amendments to sustain potato yield and N nutrition. This study showed that appropriate organic amendment management could increase potato yield and quality and reduce N fertilizer requirement.

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.

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]

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.

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]

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]

Background: Corn is the main grain crop grown in China, and the ear shape index of corn is an important parameter for breeding new varieties, including ear length, diameter, row number of ears, row number of grains per ear, and so on. Objective: In order to solve the problem of limited field of view associated with computer detection of the corn ear shape index against a complex background, this paper proposes a panoramic splicing method for corn ears against a complex background, which can splice 10 corn ear panoramic images at the same time, to improve information collection efficiency, display comprehensive information, and support data analysis, so as to realize automatic corn seed examination. Methods: A summary of corn ear panoramic stitching methods under complex backgrounds is presented as follows: 1. a perceptual hash algorithm and histogram equalization were used to extract video frames; 2. the U-Net image segmentation model based on transfer learning was used to predict corn labels; 3. a mask preprocessing algorithm was designed; 4. a corn ear splicing positioning algorithm was designed; 5. an algorithm for irregular surface expansion was designed; 6. an image stitching method based on template matching was adopted to assemble the video frames. Results: The experimental results showed that the proposed corn ear panoramic stitching method could effectively solve the problems of virtual stitching, obvious stitching seams, and too-high similarity between multiple images. The success rate of stitching was as high as 100%, and the speed of single-corn-ear panoramic stitching was about 9.4 s, indicating important reference value for corn breeding and disease and insect detection. Discussions: Although the experimental results demonstrated the significant advantages of the panoramic splicing method for corn ear images proposed in this paper in terms of improving information collection efficiency and automating corn assessment, the method still faces certain challenges. Future research will focus on the following points: 1. addressing the issue of environmental interference caused by diseases, pests, and plant nutritional status on the measurement of corn ear parameters in order to enhance the stability and accuracy of the algorithm; 2. expanding the dataset for the U-Net model to include a wider range of corn ears with complex backgrounds, different growth stages, and various environmental conditions to improve the model's segmentation recognition rate and precision. Recently, our panoramic splicing algorithm has been deployed in practical applications with satisfactory results. We plan to continue optimizing the algorithm and more broadly promote its use in fields such as corn breeding and pest and disease detection in an effort to advance the development of agricultural automation technology. [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]

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]

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]

Biostimulants include a wide array of microorganisms and substances that can exert beneficial effects on plant development and growth, often enhancing nutrient uptake and improving tolerance against abiotic and biotic stress. Depending on their composition and time of application, these products can influence plant physiology directly as growth regulators or indirectly through environmental condition changes in the rhizosphere, such as nutrient and water availability. This review evaluated 48 case studies from 39 papers to summarize the effects of biostimulant application on fruit and tuber yields and on the quality of processing tomato and potato in open field conditions. For potato, PGPR bacteria were the main studied biostimulant, whereas the low number of studies on processing tomato did not permit us to delineate a trend. The yield and quality were greatly influenced by cultivars and biostimulant composition, application method, period, and dose. For processing tomato, a positive effect of the biostimulant application on the marketable yield was reported in 79% of the case studies, whereas for potato, the effect was reported in only 47%. Few studies, on processing tomato and potato, also reported data for quality parameters with contrasting results. The variability of crop response to biostimulant application in open field conditions highlights the need for more comprehensive studies. Such studies should focus on diverse cultivars, deeply understand the interaction of biostimulant application with agronomic management (e.g., irrigation and fertilization), and evaluate yield and quality parameters. This approach is crucial to fully understand the potential and limitations of biostimulant applications in agriculture, particularly regarding their role in sustainable crop production. [ABSTRACT FROM AUTHOR]

As climate change intensifies and countries actively pursue carbon peaking and carbon neutrality targets, agriculture has emerged as a significant source of carbon emissions. A comprehensive analysis of global agricultural carbon emission research can enhance the agricultural environment and achieve a mutually beneficial outcome for environmental protection and economic development. Despite the evolution of research domains and methodologies, the global context remains closely connected to the current state of the discipline. Drawing on the Web of Science core collection, this paper develops a knowledge network framework, examines the current status and hotspots of agricultural carbon emissions, forecasts future development trends, and analyzes the findings using CiteSpace visualization software. The findings indicate that the number of papers on agricultural carbon emissions has been increasing annually, with minor fluctuations; time series analysis and sustainable development have emerged as the current focal points, and relevant institutions are collaborating increasingly closely. However, cooperation among scholars requires further enhancement. Countries such as China, the United States, and Germany are the primary nations for paper publication. The hotspot analysis reveals a high frequency of keywords such as greenhouse gas emissions and climate change, indicating that research on agricultural carbon emissions has matured and the emphasis has shifted from accounting to management. This paper develops a domain knowledge framework to assist readers in understanding agricultural carbon emission patterns and provide resources for further research. Follow-up studies should enhance both comprehensiveness and breadth, promote interdisciplinary cooperation, provide a scientific foundation for policymakers, and outline future research directions. [ABSTRACT FROM AUTHOR]

Aiming at the problems of small stems and irregular contours of bitter gourd, which lead to difficult and inaccurate location of picking points in the picking process of mechanical arms, this paper proposes an improved YOLOv5-seg instance segmentation algorithm with a coordinate attention (CA) mechanism module, and combines it with a refinement algorithm to identify and locate the picking points of bitter gourd. Firstly, the improved algorithm model was used to identify and segment bitter gourd and melon stems. Secondly, the melon stem mask was extracted, and the thinning algorithm was used to refine the skeleton of the extracted melon stem mask image. Finally, a skeleton refinement graph of bitter gourd stem was traversed, and the midpoint of the largest connected region was selected as the picking point of bitter gourd. The experimental results show that the prediction precision (P), precision (R) and mean average precision (mAP) of the improved YOLOv5-seg model in object recognition were 98.04%, 97.79% and 98.15%, respectively. Compared with YOLOv5-seg, the P, R and mA values were increased by 2.91%, 4.30% and 1.39%, respectively. In terms of object segmentation, mask precision (P(M)) was 99.91%, mask recall (R(M)) 99.89%, and mask mean average precision (mAP(M)) 99.29%. Compared with YOLOv5-seg, the P(M), R(M), and mAP(M) values were increased by 6.22%, 7.81%, and 5.12%, respectively. After testing, the positioning error of the three-dimensional coordinate recognition of bitter gourd picking points was X-axis = 7.025 mm, Y-axis =5.6135 mm, and Z-axis = 11.535 mm, and the maximum allowable error of the cutting mechanism at the end of the picking manipulator was X-axis = 30 mm, Y-axis = 24.3 mm, and Z-axis = 50 mm. Therefore, this results of study meet the positioning accuracy requirements of the cutting mechanism at the end of the manipulator. The experimental data show that the research method in this paper has certain reference significance for the accurate identification and location of bitter gourd picking points. [ABSTRACT FROM AUTHOR]

This document provides a summary of research papers that explore sustainable strategies for controlling crop diseases and pests while reducing the use of synthetic pesticides. The papers highlight the negative effects of pesticides on the environment, human health, and biodiversity, and emphasize the need for alternatives. The European Green Deal is mentioned as a framework for promoting resource efficiency and addressing climate change, biodiversity loss, and pollution. The research papers propose various alternative strategies such as biological control, plant extracts, and chemical agents, which have shown promising results in reducing pesticide usage and effectively managing crop pests and diseases. The papers also discuss the importance of genetic resistance and understanding plant resistance mechanisms for breeding programs, as well as the potential of natural predators and sustainable control strategies for pest management. Overall, this collection of research provides valuable insights for farmers and researchers seeking environmentally friendly approaches to crop protection. [Extracted from the article]

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]

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]

Farmers are increasingly faced with challenges such as climate change, population growth, and the need for sustainable food production, while simultaneously having to address the environmental impacts of conventional agriculture. Agroecology has emerged as a holistic and sustainable approach to agriculture, integrating environmental, social, and economic principles. This study investigates the role of digital tools, including decision support systems (DSSs), in supporting agroecological transitions. Through a literature review and analysis of case studies, this paper examines the benefits and challenges associated with the adoption of digital tools in agroecology, highlighting their potential to promote sustainable practices such as soil and water management, pest control, and efficient resource use. The findings indicate that while digital solutions offer significant potential to enhance productivity and improve environmental outcomes, their adoption remains limited due to barriers such as low digital literacy, lack of infrastructure, and concerns about effectiveness in real-world farming conditions. Despite these challenges, digital solutions offer significant potential to enhance productivity, improve environmental outcomes, and support farmers' decision-making. To comprehensively understand their benefits, a holistic approach is necessary, combining digital tools with hands-on training, policy support, and ongoing research. This paper highlights the role of digital tools in agroecology, explores their benefits and challenges, and discusses the need for continued research to assess their long-term potential in terms of the agroecological transition. [ABSTRACT FROM AUTHOR]

In the detection of the pests and diseases of flax, early wilt disease is elusive, yellow leaf disease symptoms are easily confusing, and pest detection is hampered by issues such as diversity in species, difficulty in detection, and technological bottlenecks, posing significant challenges to detection efforts. To address these issues, this paper proposes a flax pest and disease detection method based on an improved YOLOv8n model. To enhance the detection accuracy and generalization capability of the model, this paper first employs the Albumentations library for data augmentation, which strengthens the model's adaptability to complex environments by enriching the diversity of training samples. Secondly, in terms of model architecture, a Bidirectional Feature Pyramid Network (BiFPN) module is introduced to replace the original feature extraction network. Through bidirectional multi-scale feature fusion, the model's ability to distinguish pests and diseases with similar features and large scale differences is effectively improved. Meanwhile, the integration of the SimAM attention mechanism enables the model to learn information from three-dimensional channels, enhancing its perception of pest and disease features. Additionally, this paper adopts the EIOU loss function to further optimize the model's bounding box regression, reducing the distortion of bounding boxes caused by high sample variability. The experimental results demonstrate that the improved model achieves a significant detection performance on the flax pest and disease dataset, with notable improvements in the detection accuracy and mean average precision compared to the original YOLOv8n model. Finally, this paper proposes a YOLOv8n model with a four-headed detection design, which significantly enhances the detection capability for small targets such as pests and diseases with a size of 4 × 4 pixels or larger by introducing new detection heads and optimizing feature extraction. This method not only improves the detection accuracy for flax pests and diseases but also maintains a high computational efficiency, providing effective technical support for the rapid and precise detection of flax pests and diseases and possessing an important practical application value. [ABSTRACT FROM AUTHOR]

Seed quality testing is crucial for ensuring food security and stability. To accurately detect the germination status of corn seeds during the paper medium germination test, this study proposes a corn seed germination status detection model based on YOLO v8n (CSGD-YOLO). Initially, to alleviate the complexity encountered in conventional models, a lightweight spatial pyramid pooling fast (L-SPPF) structure is engineered to enhance the representation of features. Simultaneously, a detection module dubbed Ghost_Detection, leveraging the GhostConv architecture, is devised to boost detection efficiency while simultaneously reducing parameter counts and computational overhead. Additionally, during the downsampling process of the backbone network, a downsampling module based on receptive field attention convolution (RFAConv) is designed to boost the model's focus on areas of interest. This study further proposes a new module named C2f-UIB-iAFF based on the faster implementation of cross-stage partial bottleneck with two convolutions (C2f), universal inverted bottleneck (UIB), and iterative attention feature fusion (iAFF) to replace the original C2f in YOLOv8, streamlining model complexity and augmenting the feature fusion prowess of the residual structure. Experiments conducted on the collected corn seed germination dataset show that CSGD-YOLO requires only 1.91 M parameters and 5.21 G floating-point operations (FLOPs). The detection precision(P), recall(R), mAP0.5, and mAP0.50:0.95 achieved are 89.44%, 88.82%, 92.99%, and 80.38%. Compared with the YOLO v8n, CSGD-YOLO improves performance in terms of accuracy, model size, parameter number, and floating-point operation counts by 1.39, 1.43, 1.77, and 2.95 percentage points, respectively. Therefore, CSGD-YOLO outperforms existing mainstream target detection models in detection performance and model complexity, making it suitable for detecting corn seed germination status and providing a reference for rapid germination rate detection. [ABSTRACT FROM AUTHOR]

Soybean bacterial blight, caused by Pseudomonas savastanoi pv. glycine, which is one of the common diseases of soybeans, has a strong harm and a great impact on the yield of soybeans. If the disease is not diagnosed in time and no solution comes up, it will lead to the serious loss of yield after the disease becomes serious. Therefore, this paper proposes the detection of the soybean bacterial blight with the polarization spectroscopic imaging method, derived from the detection principle and mathematical model of polarization bidirectional reflection distribution function on the basis of the Stokes vector analysis method. By synthesizing the spectral lines of the four polarization states and the non-polarization states, it was found that the physical parameters of I (135°, 90°) polarization state were the most suitable for identifying soybean bacterial blight disease, and other polarization states could also supplement the characteristic information. The results show that the polarization spectral image can effectively identify the polarization characteristics of healthy soybean leaves and early bacterial blight in the field, and can distinguish the healthy leaves and the diseased leaves by obtaining the relative polarization reflectance of different areas in soybean leaves. Finally, the soybean disease species can be accurately diagnosed. This paper provides an optical method for the detection of crop diseases and insect pests, which makes up for the deficiency of the traditional detection technology and can provide a scientific basis for the safe non-destructive detection of crop diseases and pests. [ABSTRACT FROM AUTHOR]

Background: Given the severe economic burden that citrus greening disease imposes on fruit farmers and related industries, rapid and accurate disease detection is particularly crucial. This not only effectively curbs the spread of the disease, but also significantly reduces reliance on manual detection within extensive citrus planting areas. Objective: In response to this challenge, and to address the issues posed by resource-constrained platforms and complex backgrounds, this paper designs and proposes a novel method for the recognition and localization of citrus greening disease, named the HHS-RT-DETR model. The goal of this model is to achieve precise detection and localization of the disease while maintaining efficiency. Methods: Based on the RT-DETR-r18 model, the following improvements are made: the HS-FPN (high-level screening-feature pyramid network) is used to improve the feature fusion and feature selection part of the RT-DETR model, and the filtered feature information is merged with the high-level features by filtering out the low-level features, so as to enhance the feature selection ability and multi-level feature fusion ability of the model. In the feature fusion and feature selection sections, the HWD (hybrid wavelet-directional filter banks) downsampling operator is introduced to prevent the loss of effective information in the channel and reduce the computational complexity of the model. Through using the ShapeIoU loss function to enable the model to focus on the shape and scale of the bounding box itself, the prediction of the bounding box of the model will be more accurate. Conclusions and Results: This study has successfully developed an improved HHS-RT-DETR model which exhibits efficiency and accuracy on resource-constrained platforms and offers significant advantages for the automatic detection of citrus greening disease. Experimental results show that the improved model, when compared to the RT-DETR-r18 baseline model, has achieved significant improvements in several key performance metrics: the precision increased by 7.9%, the frame rate increased by 4 frames per second (f/s), the recall rose by 9.9%, and the average accuracy also increased by 7.5%, while the number of model parameters reduced by 0.137 × 10 7 . Moreover, the improved model has demonstrated outstanding robustness in detecting occluded leaves within complex backgrounds. This provides strong technical support for the early detection and timely control of citrus greening disease. Additionally, the improved model has showcased advanced detection capabilities on the PASCAL VOC dataset. Discussions: Future research plans include expanding the dataset to encompass a broader range of citrus species and different stages of citrus greening disease. In addition, the plans involve incorporating leaf images under various lighting conditions and different weather scenarios to enhance the model's generalization capabilities, ensuring the accurate localization and identification of citrus greening disease in diverse complex environments. Lastly, the integration of the improved model into an unmanned aerial vehicle (UAV) system is envisioned to enable the real-time, regional-level precise localization of citrus greening disease. [ABSTRACT FROM AUTHOR]

In the production process of maize, the uniformity of maize sowing is one of the main factors affecting maize yield. The effect of soil coverage and the compaction process on sowing uniformity, as the final link in determining the seed bed position, needs to be further investigated. In this paper, the parameters between soil particles and boundaries are calibrated using the Plackett–Burman test and the central composite design. Furthermore, based on the DEM–MBD coupling, the influence of soil coverage and the compaction process on the seed position of the seeding monomer at different forward speeds are analysed. It was found that the adhesion between the soil and the soil-touching component can have a significant effect on the contact process between the component and the soil. Therefore, the EEPA model was used to analyse the soil–component interaction process and the contact parameters between the soil and components were obtained for the calibration. Further, based on the above work, it was found that before and after mulching, the displacement of seed particles of all shapes in the longitudinal direction increased significantly with the increase in the advancement speed of the sowing unit, while the displacement of seed particles in the transverse and sowing depth directions decreased with the increase in the advancement speed of the unit. In addition, before and after suppression, as the forward speed of the sowing unit increased, the displacement of seed particles of all shapes in the longitudinal and transverse directions gradually increased, and the displacement of seed particles of all shapes in the direction of the sowing depth decreased; the disturbance of seed displacement by the mulch suppression process was not related to seed shape. As the operating speed of the seeding unit increased, the mulching compaction process significantly reduced the sowing uniformity of maize seeds. This paper provides a theoretical basis for the next step in optimising the structure and working process of the soil coverage and the compaction. [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]

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]

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]