Search Results (797)

Context: Optimizing nitrogen application and planting density is critical for achieving high yields and increasing lodging resistance in crops. However, the agronomic mechanisms underlying these benefits remain unclear. Objectives: This study aimed to elucidate the relationships among light distribution within the canopy, photosynthetic capacity, root architecture, yield, and lodging resistance in sweet corn. Methods: A two-year field experiment (2024–2025) was conducted using a split-plot design with two factors: nitrogen application levels as main plots (namely, N150 and N200; 150 kg/ha and 200 kg/ha, respectively) and three planting densities as sub-plots (D20, D25, and D30, representing plant spacing of 20 cm, 25 cm, and 30 cm, respectively, with a fixed row spacing of 80 cm). Results: At a given planting density, N150-treated plants exhibited significantly enhanced basal stem node strength and root architecture compared to those treated with N200. These improvements were closely associated with the increase in light interception rate (IR) into the lower canopy under N150. Consequently, root-lodging resistance increased, reducing the root lodging rate by 80.82% (7.32% vs. 13.21% under N200). Due to these advantages, the average yield of N150-treated plants was higher than that of N200-treated plants (+3.16%). Notably, increasing planting density emerged as the primary factor driving ear yield improvement, with the highest yield observed under the N150D20 group plants, which can reach ~29 t/ha. Conclusion: Coordinating nitrogen input with appropriate planting density improves vertical light distribution, particularly in the middle and lower canopy, thereby strengthening the basal stem and root systems and enhancing root lodging resistance and yield. Implication: These findings offer practical guidance for achieving high sweet corn yields by integrating canopy light management with optimized nitrogen application and planting density, and provide scientific guidance on “smart canopy” selection for sweet corn breeding. Full article

Morningglories (Ipomoea lacunosa, I. hederacea, and I. purpurea) are persistent, problematic weeds in summer row crops throughout warm-temperate regions. Their vining growth habit and enduring seedbanks lead to recurring infestations and harvest interferences. This review synthesizes current knowledge on the seed ecology of these species to clarify how dormancy, germination, and emergence processes contribute to their persistence. Published anatomical and ecological studies were examined to summarize dormancy mechanisms, environmental signals regulating dormancy release, germination requirements, and seasonal emergence patterns. Morningglories exhibit a dormancy system dominated by physical dormancy, occasionally combined with a transient physiological component. Dormancy release is promoted by warm and fluctuating temperatures, hydration–dehydration cycles, and long-term seed-coat weathering. Once permeable, seeds germinate across broad temperature ranges, vary in sensitivity to water potential, and show limited dependence on light. Field studies indicate extended emergence windows from late spring through midsummer, especially in no-till systems where surface seeds experience strong thermal and moisture fluctuations. Despite substantial progress, significant gaps remain concerning maternal environmental effects, population-level variation, seedbank persistence under modern management, and the absence of mechanistic emergence models. An improved understanding of these processes will support the development of more predictive and ecologically informed management strategies. Full article

Efficient nitrogen (N) management is critical for improving productivity and sustainability in intercropping systems, especially in semi-arid regions. Maize and peanut, the two dominant local crops, were selected to represent a typical cereal/legume intercropping system with contrasting nitrogen acquisition strategies. To investigate how spatial configuration regulates nitrogen uptake and nitrogen use efficiency in maize/peanut intercropping systems, a 3-year field (2022–2024) experiment was conducted on sandy soils in semi-arid northwest Liaoning, China. Six cropping systems were evaluated, including sole maize, sole peanut, and four intercropping configurations differing in strip width and crop proportion, including M2P2 (two rows of maize intercrop with two rows of peanut, M indicates maize and P indicates peanut), M2P4, M4P4, and M8P8. The total land equivalent ratio (LER) varied from 0.65 to 1.09, indicating that yield advantages were highly dependent on spatial configuration. Maize consistently exhibited stronger competitiveness than peanut, resulting in suppressed peanut growth in narrow-strip systems. Increasing strip width and peanut proportion alleviated interspecific competition and improved fertilizer nitrogen equivalent ratio (FNER) and nitrogen equivalent ratio (NER) in intercrops. Although intercropping did not consistently enhance total nitrogen uptake, nitrogen use efficiency was significantly improved. Narrow-strip systems (M2P2 and M2P4) increased nitrogen use efficiency, whereas wide-strip systems (M4P4 and M8P8) achieved yield benefits mainly through enhanced nitrogen uptake. Overall, the results highlight that spatial configuration plays a key role in regulating nitrogen uptake and interspecific competition in maize/peanut intercropping under semi-arid sandy conditions. Optimizing strip width and crop proportion is therefore critical for stabilizing yield and improving resource use efficiency in maize/peanut intercropping systems in dryland agriculture. Full article

This study introduces an efficient real-time lane detection and navigation system for greenhouse environments, leveraging the LaneATT architecture. Designed for deployment on the Jetson Xavier NX edge computing platform, the system utilizes an RGB camera to enable autonomous navigation in greenhouse rows. From real-world agricultural environments, data were collected and annotated to train the model, achieving 90% accuracy, 91% F1 Score, and an inference speed of 48 ms per frame. The LaneATT-based vision system was trained and validated in greenhouse environments under heterogeneous illumination conditions and across multiple phenological stages of crop development. The navigation system was validated using a commercial skid-steering mobile robot operating within an experimental greenhouse environment under actual operating conditions. The proposed solution minimizes computational overhead, making it highly suitable for deployment on edge devices within resource-constrained environments. Furthermore, experimental results demonstrate robust performance, with precise lane detection and rapid response times on embedded systems. Full article

Cotton is a globally important economic crop and the foundational raw material for the textile industry, and the planting pattern plays a crucial role in determining both the yield and quality of cotton. The results demonstrated that compared with the use of the traditional wide–narrow row (66 + 10 cm) planting pattern, the use of uniform row spacing significantly increased cotton yield (pooled effect size = 0.09, p < 0.05; average yield increase of 9.41%) when interrow distances were homogenized to optimize the population canopy structure. Moreover, this approach comprehensively improved fiber quality, yielding an average increase of 2.02% in cotton fiber length (pooled effect size = 0.02, p < 0.001), an average increase of 8.32% in cotton breaking tenacity (pooled effect size = 0.08, p < 0.001), and an average decrease of 6.76% in the cotton micronaire value (pooled effect size = −0.07, p < 0.001). This study confirms that the use of a uniform row spacing planting pattern is a key agronomic measure for simultaneously achieving high yield and superior fiber quality in cotton, providing both theoretical and practical insights into the optimization of cotton cultivation patterns. Full article

To address the localization and mapping challenges for quadruped robots autonomously scouting under corn canopies, this paper proposes CK-SLAM, a SLAM algorithm integrating robot motion constraints and crop row features. The algorithm is implemented on the Jueying Mini quadruped robot, fusing data from 3D LiDAR, IMU, and joint sensors. First, an Invariant Extended Kalman Filter (InEKF) fuses multi-source motion information, dynamically adjusting observation noise via a foot contact probability model (derived from joint torque data) to achieve initial motion state estimation and reliable pose references for point cloud deskewing. Second, three feature extraction schemes are designed, inheriting line/plane features from LeGO-LOAM and adding an innovative crop plane feature extraction module, which uses grid filtering, differential evolution for crop row detection, and RANSAC plane fitting to capture corn plant structural features. Finally, a two-step Levenberg–Marquardt iteration realizes feature matching and pose optimization, with factor graph optimization fusing motion constraints and laser odometry for global trajectory and map refinement. CK-SLAM effectively adapts to gait-induced measurement noise and enhances feature matching stability under canopies. Experimental validation across four corn growth stages shows it achieves an average Absolute Pose Error (APE) RMSE of 2.0939 m (15.7%/56.4%/72.2% lower than A-LOAM/LeGO-LOAM/Point-LIO) and an average Relative Pose Error (RPE) RMSE of 0.0946 m, providing high-precision navigation support for automated field monitoring. Full article

Crop row line detection is essential for precision agriculture, supporting autonomous navigation, field management, and growth monitoring. To address the low detection accuracy of rapeseed seedling rows under complex field conditions, this study proposes a detection framework that integrates an improved BiSeNetV2 with a dynamic sliding-window fitting strategy. The improved BiSeNetV2 incorporates the Efficient Channel Attention (ECA) mechanism to strengthen crop-specific feature representation, an Atrous Spatial Pyramid Pooling (ASPP) decoder to improve multi-scale perception, and Depthwise Separable Convolutions (DS Conv) in the Detail Branch to reduce model complexity while preserving accuracy. After semantic segmentation, a Gaussian-filtered vertical projection method is applied to identify crop-row regions by locating density peaks. A dynamic sliding-window algorithm is then used to extract row trajectories, with the window size adaptively determined by the row width and the sliding process incorporating both a lateral inertial-drift strategy and a dynamically adjusted longitudinal step size. Finally, variable-order polynomial fitting is performed within each crop-row region to achieve precise extraction of the crop-row lines. Experimental results indicate that the improved BiSeNetV2 model achieved a Mean Pixel Accuracy (mPA) of 87.73% and a Mean Intersection over Union (MIoU) of 79.40% on the rapeseed seedling dataset, marking improvements of 9.98% and 8.56%, respectively, compared to the original BiSeNetV2. The crop row detection performance for rapeseed seedlings under different environmental conditions demonstrated that the Curve Fitting Coefficient (CFC), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) were 0.85, 1.57, and 1.27 pixels on sunny days; 0.86, 2.05 and 1.63 pixels on cloudy days; 0.74, 2.89, and 2.22 pixels on foggy days; and 0.76, 1.38, and 1.11 pixels during the evening, respectively. The results reveal that the improved BiSeNetV2 can effectively identify rapeseed seedlings, and the detection algorithm can identify crop row lines in various complex environments. This research provides methodological support for crop row line detection in precision agriculture. Full article

Contemporary nurseries of fruit trees and ornamental plants constitute a key component in the production of high-quality planting material. At present, conventional technology dominates in nurseries in Poland and throughout the European Union. It is based on universal agricultural tractors working with numerous specialized machines—typically underutilized—including sprayers, inter-row cultivation equipment, fertilizer spreaders, and tree lifters. This concept entails several limitations and high investment costs. Because of the considerable size and turning radius of such machinery, a dense network of service roads (every 15–18 m) and wide headlands must be maintained. These areas, which constitute approximately 20% of the total surface, are effectively wasted yet require continuous agronomic maintenance. An alternative concept employs a set of implements mounted on a high-clearance portal tractor (1.6–1.8 m), forming a specialized unit capable of moving above the rows of nursery crops. The study objective of the research was to evaluate the air distribution generated by an air-jet system installed on a crop-spray boom mounted on a portal sprayer, and to assess spray deposition during treatments in nursery trees. Such a configuration enables the mechanization of a broader range of nursery operations than currently possible, while reducing investment costs compared with conventional technology. One still underutilized technology consists of sprayers with an auxiliary airflow (AA) generated by air sleeves. Mean air velocity was measured in three vertical planes, and they showed lower air velocity between 1.0 m and 5.5 m. Spray deposition on apple nursery trees was assessed using a fluorescent tracer. The experimental design consists of a comparative field experiment with and without air flow support, spraying at two standard working rates (200 and 400 L·ha⁻¹) and determining the application of the liquid to plants in the nursery. The results demonstrated a positive effect of the AA system on deposition. At a travel speed of 6.0 km·h⁻¹ and an application rate of 200 L·ha⁻¹, deposition on the upper leaf surface was 68% higher with the fan engaged. For a 400 L·ha⁻¹ rate, deposition increased by 47%, with both differences statistically significant. The study showed that the nursery sprayer mounted on a high-clearance portal tractor and equipped with an AA system achieved an increase of 58% in spray deposition on the upper leaf surface when the fan was operating at 200 L·ha⁻¹ and 28% at 400 L·ha⁻¹. Substantial differences were found between deposition on the upper and lower leaf surfaces, with the former being 20–30 times greater. Given the complexity of nursery production technology, sprayers that ensure the highest possible biological efficacy and high quality of nursery material will play a pivotal role in its development. At the current stage, AA technology fulfils these requirements. Full article

Canopy configuration affects crop yields by optimizing radiation interception and/or use efficiency in greenhouses. Although tomato metrics have been reported, the effects of row spacing on growth, yield and radiation for different cultivars are not well documented. Here, we examined tomato growth, yield, radiation interception and use efficiency in a greenhouse with four row spacing patterns (T1: 50 cm, T2: 60 cm, T3: 70 cm and T4: 80 cm) and two tomato cultivars (Aomeila1618 and Zhefen202) over a two-year period. A constructed intermediate model was used to simulate tomato radiation interception. Although there were great differences in the genotypes between the two selected cultivars, 50 cm (T1) was the optimal row spacing to produce a larger leaf area per unit of land area, intercept more radiation and ultimately achieve higher yield than the other three row spacing patterns (T2, T3 and T4). The mean total radiation interception across years and cultivars was 559.43 MJ m⁻² in T1, 2.8–3.8% higher than in the other three row spacing patterns. Despite similar dry matter and RUE to Aomeila1618, Zhefen202 in the narrow strip used light more efficiently. These results will help to optimize canopy structures by taking cultivar-specific responses in RUE and growth traits into consideration for high-efficiency tomato production in greenhouses. Full article

Canavalia ensiformis (L.) DC. (Fabaceae) is used in Cuba in soils dedicated to coffee cultivation, contributing to soil nutrition and crop productivity. However, no rhizobial isolates are currently available for inoculating this legume in Chromic Eutric Cambisol soils. The aim of this study was to select rhizobial strains that promote the growth of C. ensiformis in Chromic Eutric Cambisol soils. Nodules were collected from C. ensiformis plants, surface-sterilized, and macerated to isolate potential rhizobia. The isolates were characterized based on cultural, morphological, and biochemical traits, and their symbiotic effectiveness was evaluated through in vitro inoculation assays in Macroptilium atropurpureum (siratro) plants. Inoculation trials were conducted under semi-controlled conditions and in the field between coffee rows. The number and dry weight of effective nodules, number of trifoliate leaves, and shoot dry biomass were measured. Nine bacterial isolates were obtained, grouped into four morphotypes, and assigned as possible members of the families Phyllobacteriaceae, Methylobacteriaceae, or Nitrobacteraceae. Under semi-controlled conditions, inoculation with three isolates increased the number of nodules (by 56–80%), the number of trifoliate leaves (by 20–45%), and shoot biomass (by 10–40%) compared to the non-inoculated treatment. Additionally, one of the isolates increased nodule dry weight by 27%. In the field between coffee row, increases were also observed in the number of trifoliate leaves (by 18–26%) and shoot biomass (by 15–24%). This study supports the selection of efficient rhizobia adapted to the edaphoclimatic conditions of Cuban coffee agroecosystems. Full article

Maize–alfalfa intercropping is practiced in Northeast China to improve land productivity and forage production. However, competition between the two crops can reduce system performance, which calls for an emphasis on optimal row ratio. Hence, the current study evaluated the effects of diverse maize–alfalfa row ratio configurations (1:1, 2:1, 2:2, 3:1, 3:2, and 3:3) on resource-use efficiency, physiological traits, and yield performance. It was noted that the mono-cropping system had higher physiological and agronomic values for both crops. With regard to the intercropping configuration, the 2:2 steadily outperformed all other intercropping row ratios. Whereas alfalfa grew tallest in 2:2, maize plant height peaked under the 3:1. Photosynthetic rate and chlorophyll content were highest under 2:2, for both crops. The yield results indicated that alfalfa achieved maximum forage and biomass, whereas maize performed best under a 3:1 configuration. Outstandingly, under the 2:2 ratio, the cumulative system yield exceeded alfalfa mono-cropping by 55% and maize mono-cropping by 56–57%. There was superior complementarity and land-use advantage under 2:2, as indicated by the highest resource-use indicators of LER (land equivalent ratio), LEC (land equivalent coefficient), SPI (system productivity index), and K (crowding index). Competitive Indices showed that competition was more balanced under 2:2, with maize dominating in systems with higher maize proportions. Overall, the 2:2 row ratio provided the best balance of reduced competition and enhanced complementarity, offering a more efficient and sustainable maize-alfalfa intercropping strategy. Full article

Drip fertigation (DF) is a sustainable agricultural management technique that optimizes water and nutrient usage, enhances crop productivity, and reduces environmental impact. Herein, we compared the effects of DF and conventional fertilization (CF) with a basal fertilizer on yield, soil inorganic nitrogen dynamics, N₂O emissions, and nitrogen leaching during facility-grown eggplant cultivation. The experiment was conducted in a greenhouse from September 2023 to May 2024, with treatments arranged in three rows and three replicates. Soil, gas, and water samples were collected and analyzed throughout the growing season. The results revealed that the DF treatment produced yields comparable to those obtained with the CF treatment while significantly reducing nitrogen and phosphorus inputs. DF effectively prevented excessive nitrogen accumulation in the soil and reduced nitrogen loss through leaching and gas emissions. N₂O emissions were significantly lower by more than 60% under DF than under CF. Precise nutrient management in DF suppressed nitrification and denitrification processes, mitigating N₂O emissions. DF also significantly reduced nitrogen leaching by more than 70% compared with that in CF. These findings demonstrate that DF effectively enhances agricultural sustainability by improving nutrient use efficiency, reducing greenhouse gas emissions, and minimizing nitrogen leaching during the cultivation of facility-grown eggplant. Full article

Monitoring groundcover diversity in vineyards is a complex task, often limited by the time and expertise required for accurate botanical identification. Remote sensing technologies and AI-based tools are still underutilized in this context, particularly for classifying herbaceous vegetation in inter-row areas. In this study, we introduce a novel approach to classify the groundcover into one of nine categories, in order to simplify this task. Using UAV images to train a convolutional neural network through a deep learning methodology, this study evaluates the effectiveness of different backbone structures applied to a UNet network for the classification of pixels into nine classes of groundcover: vine canopy, bare soil, and seven distinct cover crop community types. Our results demonstrate that the UNet model, especially when using an EfficientNetB0 backbone, significantly improves classification performance, achieving 85.4% accuracy, 59.8% mean Intersection over Union (IoU), and a Jaccard index of 73.0%. Although this study demonstrates the potential of integrating remote sensing and deep learning for vineyard biodiversity monitoring, its applicability is limited by the small image coverage, as data were collected from a single vineyard and only one drone flight. Future work will focus on expanding the model’s applicability to a broader range of vineyard systems, soil types, and geographic regions, as well as testing its performance on lower-resolution multispectral imagery to reduce data acquisition costs and time, enabling large-scale and cost-effective monitoring. Full article

Modern weeding technologies include chemical weeding, non-contact methods such as laser weeding, and conventional mechanical inter-row cultivation characterized by soil loosening and weed uprooting. For maize, mechanical inter-row cultivation is key to cutting herbicide use and enhancing the soil–crop environment. This study developed a vision-guided intelligent inter-row cultivator with electric lateral shifting—its frame fabricated from Q235 low-carbon structural steel and assembled mainly via bolted and pinned joints—that computes real-time lateral deviation between the implement and crop rows through maize plant recognition and crop row fitting and uses delay compensation to command a servo-electric cylinder for precise ±15 cm inter-row adjustments corresponding to 30% of the 50 cm row spacing. To test the system’s dynamic response, 1–15 cm-commanded lateral displacements were evaluated at 0.31, 0.42, and 0.51 m/s to characterize the time-displacement response of the servo-electric shift mechanism; field tests were conducted at 0.51 m/s with three 30 m passes per maize growth stage to collect row-guidance error and root-injury data. Field results show that at an initial offset of 5 cm, the mean absolute error is 0.76–1.03 cm, and at 15 cm, the 95th percentile error is 7.5 cm. A root damage quantification method based on geometric overlap arc length was established, with rates rising with crop growth: 0.12% at the V2 to V3 stage, 1.46% at the V4 to V5 stage, and 9.61% at the V6 to V8 stage, making the V4 to V5 stage the optimal operating window. Compared with chemical weeding, the system requires no herbicide application, avoiding issues related to residues, drift, and resistance management. Compared with laser weeding, which requires high tool power density and has limited effective width, the tractor–implement system enables full-width weeding and shallow inter-row tillage in one pass, facilitating integration with existing mechanized operations. These results, obtained at a single forward speed of 0.51 m/s in one field and implement configuration, still require validation under higher speeds and broader field conditions; within this scope they support improving the precision of maize mechanical inter-row cultivation. Full article

Biochar and well-rotted manure are commonly employed materials for sustainable agricultural development, possessing the potential to consistently enhance the yield of monoculture crops. However, their impact on the stability of crop yields in intercropping systems, as well as the microenvironment of the border-row rhizosphere, remains inadequately understood. Consequently, this study utilized corn stover biochar and well-rotted pig manure while minimizing the application of chemical fertilizers to investigate the synergistic effects of biochar and composted manure in augmenting maize yield within a soybean–maize intercropping system and regulating the nitrogen cycle in the border-row rhizosphere under reduced fertilization conditions. In comparison to traditional fertilization, the combination of biochar and manure under reduced fertilization conditions significantly increased the contents of ammonium nitrogen (55%), dissolved organic nitrogen (523%), and particulate organic nitrogen (833%) while simultaneously decreasing the content of mineral-associated organic nitrogen (60%). Additionally, this combination synergistically reduced urease activity (22%) while enhancing the activities of nitrogenase (11%), nitrate reductase (297%), and hydroxylamine reductase (20%). This study establishes a theoretical foundation for elucidating how organically amended materials consistently enhance productivity in intercropping systems and alter nitrogen ecology in border-row rhizospheres, offering new perspectives on sustainable fertilization strategies and crop patterns. Full article