Search Results (949)

Precisely recognizing diseases in tomato leaves plays a crucial role in enhancing the health, productivity, and quality of tomato crops. However, disease identification methods that rely on single-mode information often face the problems of insufficient accuracy and weak generalization ability. Therefore, this paper proposes a tomato leaf disease recognition framework FCMNet based on multimodal fusion, which combines tomato leaf disease image and text description to enhance the ability to capture disease characteristics. In this paper, the Fourier-guided Attention Mechanism (FGAM) is designed, which systematically embeds the Fourier frequency-domain information into the spatial-channel attention structure for the first time, enhances the stability and noise resistance of feature expression through spectral transform, and realizes more accurate lesion location by means of multi-scale fusion of local and global features. In order to realize the deep semantic interaction between image and text modality, a Cross Vision–Language Alignment module (CVLA) is further proposed. This module generates visual representations compatible with Bert embeddings by utilizing block segmentation and feature mapping techniques. Additionally, it incorporates a probability-based weighting mechanism to achieve enhanced multimodal fusion, significantly strengthening the model’s comprehension of semantic relationships across different modalities. Furthermore, to enhance both training efficiency and parameter optimization capabilities of the model, we introduce a Multi-strategy Improved Coati Optimization Algorithm (MSCOA). This algorithm integrates Good Point Set initialization with a Golden Sine search strategy, thereby boosting global exploration, accelerating convergence, and effectively preventing entrapment in local optima. Consequently, it exhibits robust adaptability and stable performance within high-dimensional search spaces. The experimental results show that the FCMNet model has increased the accuracy and precision by 2.61% and 2.85%, respectively, compared with the baseline model on the self-built dataset of tomato leaf diseases, and the recall and F1 score have increased by 3.03% and 3.06%, respectively, which is significantly superior to the existing methods. This research provides a new solution for the identification of tomato leaf diseases and has broad potential for agricultural applications. Full article

This study investigates farmers’ knowledge, perceptions, and adaptation strategies to climate change in Rwanda’s Eastern Province, integrating social and physical science approaches. Analyzing meteorological data (1981–2021) and surveys from 204 farmers across five districts, we assessed climate trends and adaptation behaviors using statistical methods (descriptive statistics, Chi-square, logistic regression, Regional Kendall test, dynamic linear state-space model). Results show that 85% of farmers acknowledge climate change, with 54% observing temperature increases and 37% noting rainfall declines. Climate data confirm significant rises in annual minimum (+0.76 °C/decade) and mean temperatures (+0.48 °C/decade), with the largest seasonal increase (+0.86 °C/decade) in June–August. Rainfall trends indicate a non-significant decrease in March–May and a slight increase in September–December. Farmers report crop failures, yield reductions, and food shortages as major climate impacts. Common adaptations include agroforestry, crop diversification, and fertilizer use, though financial limitations, information gaps, and input scarcity impede adoption. Despite limited formal education (53.9% primary, 22.3% no formal education), indigenous knowledge aids seasonal prediction. Farm location, group membership, and farming goal are key adaptation enablers. These findings emphasize the need for targeted policies and climate communication to enhance rural resilience by strengthening smallholder farmer support systems for effective climate adaptation. Full article

Precise monitoring of vegetative growth is essential for assessing crop responses to environmental changes. Conventional methods of geometric characterization of plants such as RGB imaging, multispectral sensing, and manual measurements often lack precision or scalability for growth monitoring of rice. LiDAR offers high-resolution, non-destructive 3D canopy characterization, yet applications in rice cultivation across different growth stages remain underexplored, while LiDAR has shown success in other crops such as vineyards. This study addresses that gap by using LiDAR for geometric characterization of rice plants at early, middle, and late growth stages. The objective of this study was to characterize rice plant geometry such as plant height, canopy volume, row distance, and plant spacing using the proximal LiDAR sensing technique at three different growth stages. A commercial LiDAR sensor (model: VPL−16, Velodyne Lidar, San Jose, CA, USA) mounted on a wheeled aluminum frame for data collection, preprocessing, visualization, and geometric feature characterization using a commercial software solution, Python (version 3.11.5), and a custom algorithm. Manual measurements compared with the LiDAR 3D point cloud data measurements, demonstrating high precision in estimating plant geometric characteristics. LiDAR-estimated plant height, canopy volume, row distance, and spacing were 0.5 ± 0.1 m, 0.7 ± 0.05 m³, 0.3 ± 0.00 m, and 0.2 ± 0.001 m at the early stage; 0.93 ± 0.13 m, 1.30 ± 0.12 m³, 0.32 ± 0.01 m, and 0.19 ± 0.01 m at the middle stage; and 0.99 ± 0.06 m, 1.25 ± 0.13 m³, 0.38 ± 0.03 m, and 0.10 ± 0.01 m at the late growth stage. These measurements closely matched manual observations across three stages. RMSE values ranged from 0.01 to 0.06 m and r² values ranged from 0.86 to 0.98 across parameters, confirming the high accuracy and reliability of proximal LiDAR sensing under field conditions. Although precision was achieved across growth stages, complex canopy structures under field conditions posed segmentation challenges. Further advances in point cloud filtering and classification are required to reliably capture such variability. Full article

The DNDC-Rice model effectively simulates yield and greenhouse gas emissions within a paddy system, while its performance under upland conditions remains unclear. Using data from a long-term cover crop experiment (fallow [FA] vs. rye [RY]) in a soybean field, this study validated the DNDC-Rice model’s performance in simulating soil dynamics, crop growth, and C-N cycling processes in upland systems through various indicators, including soil temperature, water-filled pore space (WFPS), soybean biomass and yield, CO₂ and N₂O fluxes, and soil organic carbon (SOC). Based on simulated results, the underestimation of cumulative N₂O flux (25.6% in FA and 5.1% in RY) was attributed to both underestimated WFPS and the algorithm’s limitations in simulating N₂O emission pulses. Overestimated soybean growth increased respiration, leading to the overestimation of CO₂ flux. Although the model captured trends in SOC stock, the simulated annual values differed from observations (−9.9% to +10.1%), potentially due to sampling errors. These findings indicate that the DNDC-Rice model requires improvements in its N cycling algorithm and crop growth sub-models to improve predictions for upland systems. This study provides validation evidence for applying DNDC-Rice to upland systems and offers direction for improving model simulation in paddy-upland rotation systems, thereby enhancing its applicability in such contexts. Full article

Sugarcane is mostly planted in rows, and the accurate identification of crop rows is important for the autonomous navigation of agricultural machines. Especially in the elongation period of sugarcane, accurate row identification helps in weed control and the removal of ineffective tillers in the field. However, sugarcane leaves and stalks intertwine and overlap at this stage. They can form a complex occlusion structure, which poses a greater challenge to target detection. To address this challenge, this paper proposes an improved target detection method, SugarRow-YOLO, based on the YOLOv11n model. The method aims to achieve accurate sugarcane identification and provide basic support for subsequent sugarcane row detection. This model introduces the WTConv convolutional modules to expand the sensory field and improve computational efficiency, adopts the iRMB inverted residual block attention mechanism to enhance the modeling capability of crop spatial structure, and uses the UIOU loss function to effectively mitigate the misdetection and omission problem in the region of dense and overlapping targets. The experimental results show that SugarRow-YOLO performs well in the sugarcane target detection task, with a precision of 83%, recall of 87.8%, and mAP₅₀ and mAP_50-95 of 90.2% and 69.2%. In addition to addressing the problem of large variability in row spacing and plant spacing of sugarcane, this paper introduces the DBSCAN clustering algorithm and combines it with a smooth spline curve to fit the crop rows in order to realize the accurate extraction of crop rows. This method achieved 96.6% in the task, with high precision in sugarcane target detection and demonstrates excellent accuracy in sugarcane row fitting, offering robust technical support for the automation and intelligent advancement of agricultural operations. Full article

Subsurface drainage and biochar application are conventional measures for improving saline–alkali soils. However, their combined effects on the fate of nitrogen (N) fertilizers remain unclear. This study investigated the combined effects of subsurface drainage and biochar amendment on the fate of nitrogen (N) in coastal saline–alkali soils, where these conventional remediation measures’ combined impacts on fertilizer N dynamics remain seldom studied. Using ¹⁵N-labeled urea tracing in an alfalfa–soil system, we examined how different drainage spacings (0, 6, 12, and 18 m) and biochar application rates (5, 10, and 15 t/ha) influenced N distribution patterns. Results demonstrated decreasing in drainage spacing and increasing in biochar application; these treatments enhanced ¹⁵N use efficiency on three harvested crops. Drainage showed more sustained effects than biochar. Notably, the combination of 6 m drainage spacing with 15 t/ha biochar application achieved optimal performance of ¹⁵N use, showing N utilization efficiency of 46.0% that significantly compared with most other treatments (p < 0.05). ¹⁵N mass balance analysis revealed that the plant absorption, the soil residual and the loss of applied N accounted for 21.6–46.0%, 38.6–67.5% and 8.5–18.1%, respectively. These findings provide important insights for optimizing nitrogen management in coastal saline–alkali agriculture, demonstrating that strategic integration of subsurface drainage (6 m spacing) with biochar amendment (15 t/ha) can maximize N use efficiency, although potential N losses warrant consideration in field applications. Full article

Plant-parasitic nematodes represent a significant threat to agriculture, causing substantial economic losses worldwide. Among the biological alternatives for their control, the genus Trichoderma has emerged as a promising solution for suppressing various nematode species. This article reviews key studies on the interaction between Trichoderma spp. and plant-parasitic nematodes, highlighting the most studied species such as Trichoderma harzianum, Trichoderma longibrachiatum, Trichoderma virens, and Trichoderma viride, mainly against the genera Meloidogyne, Pratylenchus, Globodera, and Heterodera. Trichoderma spp. act through mechanisms such as mycoparasitism, antibiosis, competition for space in the rhizosphere, production of lytic enzymes, and modulation of plant defense responses. They also produce metabolites that affect nematode mobility, reproduction, and survival, such as gliotoxin, viridin and cyclosporine A. In addition, they secrete enzymes such as chitinases, proteases, lipases, and glucanases, which degrade the cuticle of nematodes and their eggs. Furthermore, Trichoderma spp. induce systemic resistance in plants through modulation of phytohormones such as jasmonic acid, ethylene, salicylic acid and auxins. The use of Trichoderma in integrated nematode management enables its application in combination with crop rotation, organic amendments, plant extracts, and resistant varieties, thereby reducing the reliance on synthetic nematicides and promoting more sustainable and climate-resilient agriculture. Full article

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article

The quest to establish permanent outposts in space, the Moon, and Mars requires growing plants for nutrition, water purification, and carbon/nutrient recycling, as well as the psychological well-being of crews and personnel on extra-terrestrial platforms/outposts. To achieve these essential goals, the safety, quality, and sustainability of plant material grown in space should be comparable to Earth-grown crops. In this study, radish plants were grown at 2500 ppm CO₂ in two successive grow-outs on the International Space Station and at similar CO₂ partial pressure at the Kennedy Space Center. An additional control experiment was performed at the University of Louisiana Lafayette laboratory, at ambient CO₂. Subsequent analyses of glucosinolate and sugar species and content showed that regardless of growth condition, glucoraphasatin, glucoraphenin, glucoerucin, glucobrassicin, 4-hydroxyglucobrassicin, 4-methoxyglucobrassicin, and three aliphatic GSLs tentatively assigned to 3-methylpentyl GSL, 4-methylpentyl GSL, and n-hexyl GSL were present in all examined plants. The most common sugars were fructose, glucose, and sucrose, but some plants also contained galactose, maltose, rhamnose, and trehalose. The variability of individual secondary metabolite abundances was not related to gravity conditions but appeared more sensitive to CO₂ concentration. No indication was found that radish cultivation in space resulted in stress(es) that increased glucosinolate secondary metabolism. Flavor and nutrient components in space-grown plants were comparable to cultivation on Earth. Full article

Dual polarimetric SAR is capable of reflecting the biophysical and geometrical information of terrain with open access data availability. When it is combined with time-series observations, it can effectively capture the dynamic evolution of scattering characteristics of crops in different growth cycles. However, the actual planting of crops often shows spatial dispersion, and the same crop may be dispersed in different plots, which fails to adequately consider the correlation information between dispersed plots of the same crop in spatial distribution. This study proposed a crop classification method based on multi-temporal dual polarimetric data, which considered the utilization of information between near and far spatial plots, by employing superpixel segmentation and a HyperGraph neural network, respectively. Firstly, the method utilized the dual polarimetric covariance matrix of multi-temporal data to perform superpixel segmentation on neighboring pixels, so that the segmented superpixel blocks were highly compatible with the actual plot shapes from a long-term period perspective. Then, a HyperGraph adjacency matrix was constructed, and a HyperGraph neural network (HGNN) was utilized to better learn the features of plots of the same crop that are distributed far from each other. The method fully utilizes the three dimensions of time, polarization and space information, which complement each other so as to effectively realize high-precision crop classification. The Sentinel-1 experimental results show that, under the optimal parameter settings, the classified accuracy of combined temporal superpixel scattering features using the HGNN was obviously improved, considering the near and far distance spatial correlations of crop types. Full article

The continuous and reliable operation of autonomous systems is important for farm management decision making, whether such systems perform crop monitoring using imaging systems or crop handling in pruning and harvesting applications using robotic manipulators. Autonomous systems, including robotic ground vehicles, drones, and tractors, are major research efforts of precision crop management. However, these systems may be less effective or require specific customizations for planting systems in low tunnels, high tunnels, or other environmentally controlled enclosures. In this work, a compact and lightweight crop management platform is developed that uses overhead power cables for continuous operation over row crops, requiring less human intervention and independent of the ground terrain conditions. The platform does not carry batteries onboard for its operation, but rather pulls power from overhead cables, which it also uses to navigate over crop rows. It is developed to be modular, with the top section consisting of mobility and power delivery and the bottom section addressing a custom task, such as incorporating additional sensors for crop monitoring or manipulators for crop handling. This prototype illustrates the infrastructure, locomotive mechanism, and sample usage of the system (crop imaging) in the application of low-tunnel-covered strawberries; however, there is potential for other row crop systems with regularly spaced support structures to adopt this platform as well. Full article

Greenhouses offer optimal environments for crop cultivation during the winter months. The rationale for this study was identified as the synergistic exchange of air between the soil, the wall, and the indoor environment within the greenhouse (referring to the coupling law of the temperature fields of the three elements in space and time, including the direction of heat transfer and the consistency of the temperature zoning), thereby maintaining a more optimal temperature. However, there is a paucity of research on the impact of different spans on the thermal environment in solar greenhouses and even fewer studies on the synergistic law of changes in soil-wall indoor air in solar greenhouses with different spans. In this study, two solar greenhouses with different spans were analyzed through a combination of experiments as follows: K-means classification optimized using the grey wolf optimizer (GWO), computational fluid dynamics (CFD) simulations, and long short-term memory (LSTM) prediction models. The two solar greenhouses, designated as S1 and S2, had spans of 11 m and 10 m, respectively. The results are as follows: In two greenhouses when the span and temperature were the same, the indoor air temperature and soil temperature of the S1 greenhouse were lower than those of the S2 greenhouse; there was an isothermal layer in the north wall of greenhouses S1 and S2 (a stable area where the temperature change over time is less than 0.5 °C), the horizontal distance between the isothermal layer on the inside of the greenhouse wall and the inside of the wall was more than 400 mm, and that of the outside of the greenhouse wall was more than 200 mm; within the solar greenhouse, this study identified that heat was emitted from the inner surface of the wall (at 0 mm from the inner surface) toward the outer surface of the wall (at 0 mm from the outer surface), as well as at a horizontal distance of 200 mm from the inner surface of the wall. The temperature data from 0:00 to 8:00 at night were selected for the purpose of analyzing the temperature synergistic change in soil-wall indoor air in the S1 greenhouse. The temperature change can be classified into four categories according to K-means classification, which was optimized based on the grey wolf algorithm. The categories were as follows: high-temperature region, medium-high temperature region, medium-low temperature region, and low-temperature region. The low-temperature region spanned the range of X = (800, 3000) mm, and its height range was Y = (−150, 1200) mm. The CFD model and LSTM prediction model have been shown to be superior, and the findings of this study offer a theoretical basis for the optimization of thermal environment control in solar greenhouses. Full article

The amino acid membrane transporters of grape species take part in metabolic pathways that play crucial roles in nitrogen trafficking and in the synthesis of secondary metabolites. Therefore, identifying these amino acid transporters and defining their functional properties might have further applications in crop improvement and, hence, relevance to human nutrition. The VvCAT2 (Cation Amino acid Transporter) transporter cDNA has been isolated and cloned into a specific plasmid for over-expression in Escherichia coli. The expressed protein, after purification by Ni²⁺-chelating chromatography, has been functionally characterized in an experimental model of proteoliposomes by measuring the uptake of radiolabeled compounds. Arginine was revealed to be the best substrate, confirming the role of CAT2 in nitrogen trafficking in plant cells and within sub-cellular spaces, given its plausible localization in vacuoles. The transporter activity is modulated by pH, osmotic imbalance and ATP. The transport kinetics have been measured. Overall, the obtained data indicate the capacity of VvCAT2 in transporting arginine, making it a possible target for crop improvement with a relevance to human health. Full article

Climate change and anthropogenic activities, which are central to landscape-related concerns, affect both the well-being of populations and the structure of semi-urban and urban landscapes worldwide. This article aims to assess the environmental impact of landscape modifications across Togo as perceived through the lens of urban ecology. In conjunction with Landsat 8 satellite imagery, data were gathered via questionnaires distributed to stakeholders in urban space development. Four land use classifications are discernible from analyzing the Agoè-Nyivé northern municipalities’ cartography: vegetation, development areas/artificial surfaces, crops and fallows, meadows, and wetlands. Between 2014 and 2022, meadows and wetlands decreased by 57.14%, vegetation cover decreased by 27.77%, and fields and fallows decreased by 15.38%. Development areas/artificial surfaces increased by 40.47% due to perpetual expansion, displacing natural habitats, including wetlands and meadows, where rapid growth results in the construction of flood-prone areas. In wetland ecosystems, 91 plant species were identified and classified into 84 genera and 37 families using a floristic inventory. Typical species included Mitragyna inermis (Willd.) Kuntze; Nymphaea lotus L.; Typha australis Schumach; Ludwigia erecta (L.); Ipomoea aquatica Forssk; Hygrophila auriculata (Schumach.) Heine. This concerning observation could serve as an incentive for policymakers to advocate for incorporating urban ecology into municipal development strategies, with the aim of mitigating the environmental risks associated with rapid urbanization. Full article

The provision of food in pre-Inca/Inca cultures (1000 BC–≈1532 AD) in environments near Lake Titikaka (approximately 4000 m above sea level) was possible through an agricultural technique called “Waru-Waru”, which consists of filling the space (volume) between rows of land containing plants that are cultivated (a series of earth platforms surrounded by water canals) with water, using water as thermal energy storage to store energy during the day and to regulate the temperature of the soil and crop atmosphere at night. The problem is that these cultures left no evidence in written documents that have been preserved to this day indicating the mathematical models, the physics involved, and the experimental part they performed for the research, development, and innovation of the “Waru-Waru” technique. From a review of the existing literature, there is (1) bibliography that is devoted to descriptive research (about the geometry, dimensions, and shapes of the crop fields (and more based on archaeological remains that have survived to the present day) and (2) studies presenting complex mathematical models with many physical parameters measured only with recently developed instrumentation. The research objectives of this paper are as follows: (1) develop a mathematical model that uses finite differences in fluid mechanics, thermodynamics, and heat transfer to explain the experimental and theory principles of this pre-Inca/Inca technique; (2) the proposed mathematical model must be in accordance with the mathematical calculation tools available in pre-Inca/Inca cultures (yupana and quipu), which are mainly based on arithmetic operations such as addition, subtraction, and multiplication; (3) develop a mathematical model in a sequence of steps aimed at determining the best geometric form for thermal energy storage and plant cultivation and that has a simple design (easy to transmit between farmers); (4) consider the assumptions necessary for the development of the mathematical model from the point of view of research on the geometry of earth platforms and water channels and their implantation in each cultivation area; (5) transmit knowledge of the construction and maintenance of “Waru-Waru” agricultural technology to farmers who have cultivated these fields since pre-Hispanic times. The main conclusion is that, in the mathematical model developed, algebraic mathematical expressions based on addition and multiplication are obtained to predict and explain the evolution of soil and water temperatures in a specific crop field using crop field characterization parameters for which their values are experimentally determined in the crop area where a “Waru-Waru” is to be built. Therefore, the storage of thermal energy in water allows crops to survive nights with low temperatures, and indirectly, it allows the interpretation that the Inca culture possessed knowledge of mathematics (addition, subtraction, multiplication, finite differences, approximation methods, and the like), physics (fluids, thermodynamics, and heat transfer), and experimentation, with priority given to agricultural techniques (and in general, as observed in all archaeological evidence) that are in-depth, exact, practical, lasting, and easy to transmit. Understanding this sustainable energy storage technique can be useful in the current circumstances of global warming and climate change within the same growing areas and/or in similar climatic and environmental scenarios. This technique can help in reducing the use of fossil or traditional fuels and infrastructure (greenhouses) that generate heat, expanding the agricultural frontier. Full article