Research Frontiers: Agricultural Engineering & Innovation

The Energy Requirements, Productivity and Profitability Effects of Removing Subsoil Compaction in Maize Cropping in the Eastern Pampas of Argentina

Mon, 24 Aug 2026 00:00:00 +0000

Removing subsoil compaction caused by agricultural traffic is energy-demanding and therefore expensive. Experimental work was undertaken on a Typic Argiudoll to quantify the energy required to remove subsoil compaction and determine the associated effects on yield and profitability. The following treatments were compared: (T1) soil under no-tillage for 20 years, which was used as a control; (T2) deep tillage performed with a paratill on soil that had had no-tillage in the 20 years prior to this study; and (T3) deep tillage performed with a chisel plow on soil that had had no-tillage in the 20 years prior to this study. The paratill and chisel plow were operated at depths of 400 and 250 mm, respectively, and the energy required to perform both (deep tillage) operations was determined. Soil cone index and maize yield were measured over three growing seasons and compared with T1. Results showed that the effect of deep tillage lasted for two years, after which the soil reconsolidated reaching soil strength values comparable to their pre-treatment condition. The reconsolidation of tilled soil over this period was due to both natural settlement and post-treatment (random) machinery traffic. The paratill treatment significantly increased maize yield compared with no-tillage, which therefore improved crop gross margins across all three seasons. The chisel plow treatment increased crop yields compared with no-tillage, but yield differences were small and therefore the average crop gross margins were not significantly different. Deep tillage with paratill costed US$76 per ha and generated an average gross income of US$1134 per ha, whereas deep tillage with chisel plow costed US$29 per ha and generated an average gross income of US$1027 per ha. These results compared with an average gross income of US$1001 per ha obtained under no-tillage. If (strategic) deep tillage needs to be performed on long-term no-tillage soil to remediate compaction, paratill may be preferred to chisel plow, but care should be exercised not to re-compact the soil after the operation has been performed. One effective way to do this is by implementing controlled traffic.

Economic Impact of Semi-Mechanized Transplanting In Coffee Farming: Comparison of Operational Costs Between Conventional Systems and Systems with Autopilot and Time Updates

Tue, 18 Aug 2026 00:00:00 +0000

Studies related to the mechanization of coffee transplanting combined with precision agriculture techniques demonstrate a need to verify the quality of the operation to optimize future production processes in the field and reduce costs. The objective of this study was to analyze the operational costs arising from a semi-mechanized coffee transplanting process using an autopilot. The study was conducted on a rural property in the municipality of Santo Antônio do Amparo—MG—where 7458 coffee seedlings were transplanted using a planting platform pulled by a tractor equipped with an autopilot and a GNSS antenna, over a period of 4 days. The date and time data of the operation, recorded every second by the autopilot, were collected and recorded in spreadsheets to assist in calculating operational costs. Two semi-mechanized transplanting scenarios were compared: one using autopilot and the other using conventional semi-mechanized transplanting. The results indicated that the hourly cost of operation with autopilot was US$2130.42 h⁻¹, while the conventional system presented US$326.03 h⁻¹. The effective operational cost was US$3975.61 ha⁻¹ for the system with autopilot and US$442.31 ha⁻¹ for the conventional system in 2020. After monetary updating to 2025, the operational costs increased to US$1845.19 ha⁻¹ and US$116.28 ha⁻¹, respectively. The investment analysis indicated an Internal Rate of Return of 89.7%, highlighting the potential return on investment in the study. Therefore, it was emphasized that even with a high investment cost, the application of autopilot is viable for improving tractor steering during operation and ensuring uniformity in the positioning of coffee seedlings.

Spray Application Rates, Adjuvants, and Boron Behavior in Soybean: Insights from Physiological Responses and Remote Sensing in Cerrado

Mon, 24 Aug 2026 00:00:00 +0000

The application of boron in soybeans in Oxisols of the Brazilian Cerrado is frequently integrated into complex tank fertilizer mixtures with multiple components via foliar application. This study investigated the interactive effects of varying spray application rates (40, 70, 100, and 130 L ha⁻¹) and adjuvant types (organosilicone surfactant; methylated seed oil; and a water control) on boron deposition and the resulting physiological status. The organosilicone surfactant provided superior technical stability and deposition efficiency, allowing for a reduction in application rates to volumes between 40 and 70 L ha⁻¹ maintaining a stable foliar B status across the evaluated range. In contrast, the performance of the methylated oil was strictly dependent on physical deposition, being effective only at intermediate rates, while the use of water alone represented a high risk of technical failure at reduced volumes. Furthermore, the NDRE index proved to be more responsive and robust than NDVI for monitoring delivery efficiency in high-density canopies, as it avoided signal saturation. Finally, Multivariate Analysis helped to observe that soybean yield in the Cerrado is primarily governed by the mitigation of water and thermal stress (TVDI), with optimized boron application acting as a key facilitator of reproductive success and yield stability under these environmental constraints.

Analysis of Human Vibrations Generated During Reduced Tillage That Affect the Operator of an Agricultural Tractor

Mon, 24 Aug 2026 00:00:00 +0000

This study analyzes whole-body vibration (WBV) exposure of an agricultural tractor operator during three different primary tillage systems: Standard Tillage (ST), Conservation Tillage Deep (CTD), and Conservation Tillage Shallow (CTS). Measurements were conducted in accordance with ISO 2631-1 and ISO 2631-4 along three orthogonal axes (x, y and z) at the operator’s seat. Descriptive and inferential statistical analyses indicate that while none of the mean vibration values exceeded the regulatory limit value of 1.15 m/s² defined in Directive 2002/44/EC, several measurements—particularly in the y-axis during ST (0.715 m/s²)—surpassed the exposure action value of 0.5 m/s². These findings suggest that prolonged daily exposure under similar operational conditions may pose long-term health risks for tractor operators. The highest mean WBV values were recorded in the x- and y-axes during CTS (0.354 m/s² and 0.446 m/s², respectively), whereas the z-axis exhibited the highest values during ST (0.426 m/s²). Conservation Tillage Deep (CTD) demonstrated the most favorable vibration profile in the vertical axis (0.344 m/s²), indicating its potential dual benefit for soil structure preservation and operator ergonomics. Although all measured values remained below the regulatory limit, the frequent exceedance of the action value underscores the importance of exposure time management, regular maintenance of suspension systems, and implement selection as practical mitigation strategies. This comparative assessment provides baseline WBV data for reduced-tillage systems on hydromorphic soils and offers axis-specific guidance for optimizing operator comfort in sustainable mechanization practices.

Plant-Leaf Disease Detection Based on Texture Enhancement Using ATD-Net

Yuheng Li, Xiafen Zhang — Fri, 15 May 2026 00:00:00 +0000

Early plant leaf disease detection and timely control is important for agricultural yield and stability. Yet, it is difficult for manual labor to monitor the health of the plant leaf 24 h a day. Existing detection approach cannot meet the demands of texture enhancement features. Therefore, this paper proposes a new detection approach which undergoes three-layer transformations: convolutional layer, attention mechanism layer and loss function layer. Firstly, ADown is used to extract fine-grained texture features from suspected leaves to reduce computational load. Secondly, Gabor texture enhancement is proposed to extract and enhance the contour and the directional texture of suspected areas using multi-directional filtering, followed by a combination Transformer to enhance the global context modeling capability. Thirdly, a dynamic boundary loss function (DBL) is employed to dynamically adjust the probability distribution of bounding box regression through adaptive temperature coefficient and information entropy, thereby improving the positioning accuracy of the detection box. The experiments show that ATD-Net achieved an average accuracy of 87.42% (mAP50) and an accuracy of 85.96%, with a computational complexity of 6.5 GFLOPs. The visualization results and ablation experiments show that the collaborative work of the proposed modules significantly improves the detection robustness in complex backgrounds, early diseases, and small target scenes. Compared to the original model, ATD-Net achieves a performance improvement of 1.1% at mAP50 and a speed increase of 17.7%. The model size remains almost unchanged, at 5.2 MB. It is an efficient and promising solution for future real-time disease recognition in complex agricultural environments.