Search Results (108)

Background/Objectives: Having longer mesocotyls is beneficial for the deep-sowing tolerance of rice, which is important for seedling establishment. Methods: Here, we performed transcriptome analysis of the elongating mesocotyl of Zhengdao 209 in response to three different sowing depths to identify the pivotal genes regulating rice mesocotyl elongation. Results: Three groups with different mesocotyl lengths were compared using transcriptome analysis, and 60 common differentially expressed genes were detected. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that these genes are primarily involved in phenylpropanoid biosynthesis, cutin suberine and wax biosynthesis, the plant mitogen-activated protein kinase signaling pathway, diterpenoid biosynthesis, cyanoamino acid metabolism, carbon fixation in photosynthetic organisms, flavonoid biosynthesis, and glutathione metabolism. Furthermore, weighted gene co-expression network and hierarchical clustering analyses showed that most of the differentially expressed genes are implicated in phenylpropanoid biosynthesis, carbon metabolism, photosynthesis antenna proteins, and plant–pathogen interactions. Among the genes involved in phenylpropanoid biosynthesis processes, the expression levels of OsPHT3 and LOC_Os04g59260 increased, while OsCCR1, OsPGIP4, and LOC_Os01g45110 expression decreased with increasing sowing depth. Among the genes involved in the mitogen-activated protein kinase signaling pathway, the expression levels of LOC_Os07g03319 and LOC_Os07g03580 increased, while LOC_Os07g03409 decreased with increasing sowing depth. Among the genes involved in diterpenoid biosynthesis processes, the expression levels of OsCYP76M5 and OsCYP71Z2 decreased, while OsCYP71Z21 increased with increasing sowing depth. Furthermore, the expression levels of these genes were analyzed using quantitative real-time polymerase chain reaction, which confirmed the transcriptome analysis results. Conclusions: This study identified candidate genes governing rice mesocotyl length and provides novel insights into the molecular regulatory mechanisms underlying mesocotyl elongation in rice. Full article

Soil organic carbon (SOC) and total nitrogen (TN) are key indicators of soil fertility; however, the dynamics of carbon (C) and nitrogen (N) fractions during winter wheat growth under different tillage systems remain poorly understood. This study examined the effects of three tillage practices: no tillage (NT), subsoiling tillage (SS), and deep tillage (DT) on four soil organic carbon fractions (SOC, soil organic carbon; EOC, easily oxidized organic carbon; DOC, dissolved organic carbon; POC, particulate organic carbon) and four nitrogen fractions (TN, total nitrogen; NO₃⁻-N, nitrate nitrogen; NH₄⁺-N, ammonium nitrogen; DON, dissolved organic nitrogen) across five winter wheat growth stages (sowing, overwintering, jointing, filling and harvest) in the 0–50 cm soil profile. The results showed that SOC, its labile fractions, and TN all decreased with increasing soil depth, with tillage effects mainly confined to the 0–20 cm layer. SS achieved the highest SOC and TN contents in the topsoil, while NT and SS significantly enhanced the surface enrichment of C and N. In contrast, DT promoted more uniform nutrient distribution into the 30–50 cm subsoil. DON continuously accumulated throughout the growing season with faster accumulation rates under SS and NT; DOC peaked at the jointing stage, while EOC and NH₄⁺-N followed a consistent “decline–recovery–decline” seasonal pattern. SS yielded the highest total SOC stock (166.20 t ha⁻¹) in the 0–50 cm profile, particularly in the 0–30 cm layer. Correlation analysis showed that the coupling relationships among C and N indicators varied with soil depth, with the strongest positive correlation between SOC and EOC in the topsoil. Both SS and DT maintained higher soil water content (SWC) than NT in the 20–50 cm layers throughout the experimental period. In conclusion, SS emerges as the optimal balanced tillage strategy for dryland wheat fields on the Loess Plateau, simultaneously improving topsoil fertility, water retention, and C sequestration; meanwhile, DT is more effective for enhancing subsoil water and nutrient conditions. These findings provide a scientific basis for targeted tillage management to sustain soil fertility and productivity in rainfed dryland farming systems. Full article

Timely and accurate detection of sow oestrus is crucial for enhancing reproductive efficiency and reducing non-productive days (NPDs) in large-scale pig farms. However, traditional manual observation is labour-intensive and subjective, while cloud-based deep learning solutions face challenges such as high latency and privacy risks when applied in intensive housing environments. This study developed an edge-intelligent monitoring system that integrates deep temporal modelling with sound source localisation technology. A three-stage hierarchical screening strategy was utilised to select and deploy a lightweight Stacked-LSTM model on the resource-constrained ESP32-S3 hardware platform. This model was trained and calibrated using a high-quality acoustic dataset validated against serum reproductive hormones, specifically follicle-stimulating hormone (FSH), luteinising hormone (LH), and progesterone ($P_4$). Experimental results demonstrate that the optimised model achieved a classification accuracy of 96.17%, with an inference latency of only 41 ms, thereby fully satisfying the stringent real-time monitoring requirements while maintaining a minimal memory footprint. Furthermore, the system integrates a localisation algorithm based on Generalised Cross-Correlation with Phase Transform (GCC-PHAT). Through spatial geometric modelling, the system successfully implements the functional mapping of vocalisation events to individual gestation stalls (Stall IDs). Laboratory pressure tests validated the robustness and low-cost deployment advantages of the “edge recognition–cloud synchronization” architecture, providing a reliable technical framework for the precision management of smart livestock farming. Full article

Direct seeded rice, being less water- and labor-intensive, can be an alternative approach to conventional rice planting methods. However, uneven and poor stand establishment caused by deep sowing in the field is one of the major hurdles in the adoption of direct seeding technology. Varieties with the potential to emerge from deeper layers of soil may have a positive impact on crop establishment. To evaluate the behavior of ten rice cultivars against their potential to emerge from different soil depths (0, 2.5, and 5.0 cm), a pot experiment was conducted under semi-controlled conditions at the PARC Rice Programme, Kala Shah Kaku, Lahore. Data on different seedling parameters were collected. The results showed that the highest mean seedling emergence percentage (95%) was achieved by the tested genotypes at a 2.5 cm seeding depth, while surface sowing and placement of seeds at a 5 cm depth demonstrated a similar mean emergence percentage (89%). Seeding depth, genotypes, and their interactions significantly affected mean emergence time, mesocotyl and coleoptile lengths, and root and shoot lengths. Sowing seeds at a 5 cm depth increased mean emergence time by 28%. However, increasing sowing depth increased the coleoptile length, mesocotyl length, first leaf sheath length, and shoot length of rice seedlings. Mesocotyls and coleoptile lengths showed a linear relationship with mean emergence time. Mesocotyl and coleoptile are key structures of the apical–basal axis in grasses that elongate to facilitate the emergence of germinating seeds under deep sowing. The longest coleoptiles (1.47 cm) and mesocotyls (3.27 cm) were measured from seedlings sown at a depth of 5 cm. Among genotypes, PK-1121 exhibited maximum coleoptile elongation (2.10 cm) under deep sowing (5 cm), while the longest mesocotyls were recorded from deep-sown (5 cm) seedlings of Chenab Basmati. Root length was found to be inversely proportional to sowing depth. PK-1121 aromatic, Kisan Basmati, Punjab Basmati, and Chenab Basmati produced longer shoots (22.61, 23.37, 23.32, and 21.05 cm, respectively) and took a relatively short time for emergence when sown deep. These varieties may have better potential to emerge from deeper soil layers, which may have a positive impact on even germination and better crop stand establishment. Full article

The sustainable management of Northern China’s vulnerable agro-pastoral ecotone requires a clearer understanding of how tillage systems affect crop productivity through local soil-climate interactions. Therefore, this study was conducted to quantify and compare the long term effects of different tillage practices on soil hydrothermal regimes, resource use efficiency, and maize yield stability in a semi-arid agroecosystem. A long term five-year field experiment with maize was conducted in this ecotone to assess three tillage methods: no tillage (NT), deep ploughing (DP), and conventional rotary tillage (RT). Seasonal monitoring included soil moisture, temperature, bulk density, and straw cover. Analyses focused on soil water use efficiency (WUE), the production efficiency per soil thermal unit (PEsoil), and pathways affecting theoretical calculated yield. Results show that relative to RT and DP, NT consistently elevated soil water content within the 0–30 cm profile during the growing season, with the most marked increases from pre-sowing to the V12 stage. This water-conserving effect was stronger in wet years, highlighting the role of precipitation in NT’s performance. DP also retained more soil water than RT, particularly in deeper layers, though its effect was less pronounced than NT’s. Regarding temperature, NT lowered the daily mean soil temperature and accumulated growing degree days (GDD) in early growth phases, a result of residue cover buffering thermal changes. Despite reduced heat accumulation, NT achieved the greatest efficiencies for both heat and water use (PEsoil and WUE), showing increases of 62.03% and 16.64% over RT, respectively, without yield penalty. Key mechanisms include permanent straw mulch under NT, which curtails evaporation, promotes water infiltration, and stabilizes soil structure, thereby modulating hydrothermal dynamics. Structural equation modeling indicated that soil water content, ear number per hectare, and hundred-kernel weight directly and positively determined final yield. Tillage methods exerted indirect effects on yield by modifying soil physical traits and microclimatic conditions. In this semi-arid setting, both NT and DP outperformed RT in conserving soil water, moderating soil temperature, and boosting resource use efficiency. These practices present viable strategies for strengthening crop resilience and sustaining productivity amid climatic variability. Full article

The integration of artificial intelligence (AI) with ultrasonic biosensing presents a transformative opportunity for enhancing diagnostic accuracy in agricultural and biomedical applications. This study develops a data-driven deep learning model to address the challenge of acoustic artifacts in B-mode ultrasound imaging, specifically for sow pregnancy diagnosis. We designed a biosensing system centered on a mechanical sector-scanning ultrasound probe (5.0 MHz) as the core biosensor for data acquisition. To overcome the limitations of traditional filtering methods, we introduced a lightweight Deep Neural Network (DNN) based on the YOLOv8 architecture, which was data-driven and trained on a purpose-built dataset of sow pregnancy ultrasound images featuring typical artifacts like reverberation and acoustic shadowing. The AI model functions as an intelligent detection layer that identifies and masks artifact regions while simultaneously detecting and annotating key anatomical features. This combined detection–masking approach enables artifact-aware visualization enhancement, where artifact regions are suppressed and diagnostic structures are highlighted for improved clinical interpretation. Experimental results demonstrate the superiority of our AI-enhanced approach, achieving a mean Intersection over Union (IOU) of 0.89, a Peak Signal-to-Noise Ratio (PSNR) of 34.2 dB, a Structural Similarity Index (SSIM) of 0.92, and clinically tested early gestation accuracy of 98.1%, significantly outperforming traditional methods (IoU: 0.65, PSNR: 28.5 dB, SSIM: 0.72, accuracy: 76.4). Crucially, the system maintains a single-image processing time of 22 ms, fulfilling the requirement for real-time clinical diagnosis. This research not only validates a robust AI-powered ultrasonic biosensing system for improving reproductive management in livestock but also establishes a reproducible, scalable framework for intelligent signal enhancement in broader biosensor applications. Full article

To address the time-consuming, labor-intensive, and inefficient nature of existing contact-based measurements of sow backfat thickness (BFT), in this study, a method to estimate BFT from depth images using deep learning and image processing is proposed. Measurements of BFT and hip depth images were collected from 254 Jinfen White sows. Following preprocessing, including depth-value filtering and colorization, a modified YOLOv8n-ShuffleNetV2 detector was trained and deployed to predict regions of interest in the buttock images. Depth values were then extracted from these regions and converted into distance estimates. Then, 11 external morphological pixel-based parameters were extracted, including hip area, hip-circumference length, and the area of the fitted ellipse. A random sample of 203 sows was selected for training and testing, and the relationship between BFT and the external morphological parameters was analyzed in 152, with the rest being used for testing. The results show significant positive correlations between BFT and several hip morphological parameters, with Pearson correlation coefficients exceeding 0.90 for both hip and fitted ellipse area. Principal component analysis was applied to the selected hip features to extract area and length related factors as inputs to a machine learning model. An elastic net regression model was employed to estimate BFT. The model’s generalization capability was evaluated using 51 sows not involved in training and testing. The model achieved an R² = 0.8617, MSE = 4.3626 mm², and MAE = 1.6456 mm. Finally, a BFT estimation system for Jinfen White pigs was developed using PyQt5 and Python, which enables automatic preprocessing of sow hip images and real-time estimation of BFT. Together, these results address the cumbersome and inefficient traditional manual collection of sow BFT data and support precision management in sow breeding farms. Full article

Maternal stress during gestation can alter offspring physiology, behaviour, and immune function. In pigs, such ‘prenatal stress’ is known to increase stress sensitivity, but the potential to automatically detect such sensitivity has remained unexplored. Automatic detection of facial expression has successfully identified differences in pigs dependent on their stress status. This study progresses this work by demonstrating that, for the first time, using a deep learning framework applied to facial analysis, stress-linked phenotypes can be learned from one generation and detected in the next. Using a dataset of over 7000 facial images from 18 gestating sows and 53 of their daughters, we trained and evaluated five state-of-the-art deep learning architectures across six independent daughter cohorts. Attention-based models significantly outperformed CNN-based models, with the Vision Transformer (ViT) model achieving a mean accuracy of 0.78 and an average F1-score of 0.76. Grad-CAM visualisations showed that the ViT consistently attended to biologically relevant facial regions, such as the eyes and snout, whereas CNNs often focused on diffuse or non-informative areas, resulting in reduced low-stress recall and greater batch sensitivity. Models trained on maternal facial images successfully predicted stress responsiveness in daughters from unrelated lineages, indicating that the model captured generalisable facial cues of stress rather than familial resemblance. This approach supports previous work showing that machine vision can detect putatively stress-related alterations to facial expression in pigs. Future application of this approach could offer a scalable, non-invasive tool for early detection of stress in livestock production systems, opening new avenues for welfare-oriented precision livestock management and informed breeding strategies aimed at improving stress resilience. Full article

Salinization of irrigation areas is a key environmental challenge faced by arid and semi-arid regions worldwide, and the complexity of natural environment and human activities increases the uncertainty of salinization distribution. This study takes the Xiaohaizi Irrigation Area in Kashgar, Xinjiang as the research location. Soil samples were collected before sowing and after harvesting in 2023 and analyzed. Using geostatistics and digital soil mapping techniques, the spatial distribution and temporal evolution of soil salinization in the region were finely characterized. The results showed that the soil salinization in Xiaohaizi Irrigation District was moderate to high, with mean salt contents of 8.29 g/kg in the 0–30 cm layer, 6.16 g/kg at 30–60 cm, and 4.80 g/kg at 60–100 cm before sowing, all indicating moderate to high salinity levels. The salt content showed a surface aggregation distribution with significant differences between different depths. The main ions that affect salinization are SO₄²⁻, Ca²⁺, Mg²⁺, Cl⁻, K⁺, and Na⁺. The 0–30 cm soil layer is mainly composed of mildly saline soil, and the degree of soil salinization decreases with the depth of the soil layer. After harvesting, the overall degree of salinization in the irrigation area intensified, and the spatial distribution of salinization was uneven. The degree of salinization was higher in the northwest and lower in the south. The impact of human activities on surface soil salinization is greater than that on deep soil. The areas where the degree of salinization in the 0–30, 30–60, and 60–100 cm soil layers undergoes transformation account for 57.18%, 33.15%, and 26.9%, respectively. This study reveals the complex dynamics of soil salinization in the Xiaohaizi irrigation area, providing scientific support for soil management and irrigation strategies in the region, and is of great significance for achieving sustainable development of oasis agriculture. Full article

Integrated agronomic optimization can synergistically enhance crop yields and resource use efficiency. This strategy incorporates suitable sowing date, planting density, and fertilization and irrigation management adapted to the local environment. However, there is a dearth of research on how integrated agronomic optimization practices enhance wheat productivity and water use efficiency (WUE) by improving population root distribution and canopy production capacity. Therefore, a two-year field experiment was conducted in the North China Plain. The experiment involved three integrated agronomic practice treatments with four replications: local farmer’s agronomic practice (FP); high-input agronomic practice (HP), which aimed to explore wheat yield potential regardless of resource input costs; and optimized high-input agronomic practice (OP), which was adapted to local conditions to revamp the wheat production system. Compared to FP and HP, OP involved a later sowing date, higher planting density, and lower N fertilizer or irrigation inputs. Results showed that OP significantly improved grain yield, WUE, N fertilizer productivity (NFP), and net profit compared to FP (p < 0.05). Although OP’s yield was 4.25% lower than that of HP, it achieved a 22.99% increase over FP. Compared to HP, OP increased average WUE, NFP, and net profit by 3.08%, 25.68%, and 9.12%, respectively. Over the 2 years, OP promoted deeper roots and higher root length density, which enhanced the uptake of soil water and N. Furthermore, the high transpiration under OP, required for canopy productivity, was sustained by efficient water extraction from deep soil. Additionally, the reduction in unproductive evaporation loss was attributed to increased population density and reduced irrigation. Moreover, OP sustained a higher canopy photosynthetic rate for a longer duration, facilitated by greater post-anthesis N uptake. These improvements in resource acquisition, combined with sustained photosynthetic capacity, ultimately led to more efficient water and N utilization and high grain yield. These indicate that integrated optimization of agronomic practices used under OP can synergistically enhance wheat yield, WUE, and NFP. This was achieved by enlarging and deepening population root distribution while supporting high canopy photosynthesis. Our findings may provide actionable insights into establishing high-yielding, efficient, and profitable wheat production systems in the region. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

Aiming at the problems of excessive soil disturbance caused by deep plowing and stripped straw backfilling in strip tillage machinery, which are induced by the large amount of residual straw before maize sowing in the Huang-Huai-Hai Region, an integrated tillage machine suitable for pre-sowing strip tillage of summer maize—integrating subsoiling, stubble-crushing, and soil-guiding functions—was designed. First, the physical properties of straw were analyzed to determine the tooth profile parameters of the stubble-crushing wheel. The unique convex structure of the tooth disc enables it to simultaneously perform depth-limiting and soil-pressing functions. By calculating the flow characteristics of soil during tillage, the angle and distance between the subsoiling shovel and the stubble-crushing wheel were designed. This not only enhances soil crushing and flow but also reduces the occurrence of blockages. A discrete element simulation test with quadratic orthogonal rotation combination was conducted. The machine’s forward speed, wheel position distance, and wheel deflection angle were selected as test factors to analyze their effects on the soil loss rate of the seedbed strip and straw backflow effect under different combinations. The optimal combination of parameters was determined as follows: forward speed of 7.383 km/h, front–rear position distance parameter of −10.131 cm, and deflection angle of 8.608°, with the soil loss rate of the seedling belt reaching 5.486% under this condition. Field experiments were conducted in combination with the strip tiller to verify the simulation-optimized parameters, and comparative experiments at different speeds were also carried out. The field experimental results showed that the deviation of the actual soil disturbance rate caused by the machine from the simulated value was −1.166%, and the soil disturbance rate within the seedling belt was even lower. The results indicated that after the operation of the improved machine, there were no obvious ruts on the soil surface, and the straw was evenly distributed at the edge of the seedling belt, which meets the agronomic requirements for maize planting. Full article

Determining sow (Sus scrofa domesticus Erxleben) estrus status requires considerable labor investment, and continuous real-time monitoring is impractical. Workers typically identify estrus at scheduled intervals and determine artificial insemination timing based on experience. However, experience-based methods are subjective, vary with operator expertise, and impede standardized management in large-scale farms. This study employs cameras and deep learning to detect sows and analyze postural changes, enabling estrus detection and optimal insemination timing prediction. Experimental results indicate that the proposed method achieved an accuracy of 70% (42/60), where the recommended insemination timing differed by less than 24 h from human decisions. This approach facilitates data-driven estrus detection and insemination scheduling, potentially reducing labor intensity and improving reproductive outcomes, particularly beneficial for labor-intensive and large-scale swine production systems. Full article

Mesocotyl elongation is the key determinant of deep-sowing tolerance in maize. Sowing at an appropriate depth allows the seedling to exploit water and nutrients stored in deeper soil layers, thereby enhancing its ability to withstand drought and other abiotic stresses. Mesocotyl elongation is regulated by the phytohormones brassinosteroid (BR), auxin (IAA), gibberellin (GA), and reactive oxygen species (ROS). However, whether and how BR coordinates IAA, GA, and ROS to control mesocotyl elongation in maize remains unclear. Here, we demonstrated that BRs orchestrate ROS, IAA, and GA signaling to remodel cell-wall metabolism in mesocotyl cells, promote cell elongation, and, consequently, strengthen deep-sowing tolerance. BR promoted mesocotyl elongation through multiple routes: (1) decreasing the contents of cell-wall components (hemicellulose, cellulose, and pectin); (2) activating cell-wall-loosening enzymes (cellulase, pectinase, and acidic xylanase); and (3) disturbing ROS homeostasis by elevating superoxide dismutase (SOD) activity. Combined treatments of BR with either IAA or GA further enhanced mesocotyl elongation in a concentration-dependent manner. In deep-sowing trials (15 cm), application of BR alone or in combination with IAA or GA markedly increased mesocotyl length and emergence rate, thereby improving deep-sowing tolerance. Our work indicated that BR integrated ROS, IAA, and GA signals to restructure the cell wall and derived mesocotyl cell elongation, providing both theoretical insights and practical strategies for breeding maize varieties with enhanced deep-sowing tolerance. Full article

China ranks as the world’s leading potato (Solanum tuberosum L.) producer, while the poor seeding machinery performance limited a higher input–output ratio in potato cultivation and impeded sustainable development. We developed an advanced air-suction mulai-arm potato planter (ASPP) that incorporated integrated side-deep fertilization, automated seed feeding, negative-pressure seed filling, seed transportation, positive-pressure seed delivery, soil covering, and compaction. The study proposes a Negative-pressure seed extraction mechanism that minimizes seed damage by precisely controlling suction pressure, and the near-zero-speed seed delivery mechanism synchronizes seed release with ground speed, reducing bounce-induced spacing errors. Furthermore, the structural configuration and operation principle of ASPP were systematically elucidated, and key performance parameters and optimal values were identified. We conducted a randomized complete block design plot trial comparing the spoon-belt potato planter (SBPP) and spoon-chain potato planter (SCPP), evaluating sowing quality, seedling emergence rate (ER), potato yield (PY), and comprehensive economic benefits. The results revealed that plant spacing index (PSI), missed-seeding index (MI), re-seeding index (RI), and coefficient of variation (CV) of ASPP were 90.05%, 3.78%, 2.32%, and 7.93%, respectively. The mean ER values for ASPP, SBPP, and SCPP were 94.76%, 85.42%, and 83.46%, respectively, with the ASPP showing improvements of 10.93% and 13.54% over SBPP and SCPP. However, the SBPP and SCPP exhibited greater emergence uniformity than ASPP. The mean PY value was 37,205.25, 32,973.75, and 34,620 kg·ha⁻¹ for ASPP, SBPP, and SCPP. The ASPP outperformed the SBPP and SCPP by 12.83% and 7.47%. Overall, ASPP demonstrated balanced and superior performance across the above-mentioned indicators, demonstrating its potential to enable precision agriculture in tuber crop cultivation. Full article