Search Results (59)

Due to the limitations of the sowing machine performance and rice seed germination rates, missing seedlings inevitably occur after rice is sown in large fields. This phenomenon has a direct impact on the rice yield. In the field environment, the existing methods for detecting missing seedlings based on unmanned aerial vehicle (UAV) remote sensing images often have unsatisfactory effects. Therefore, to enable the fast and accurate detection of missing rice seedlings and facilitate subsequent reseeding, this study proposes a UAV remote-sensing-based method for detecting missing rice seedlings in large fields. The proposed method uses an improved PCERT-DETR model to detect rice seedlings and missing seedlings in UAV remote sensing images of large fields. The experimental results show that PCERT-DETR achieves an optimal performance on the self-constructed dataset, with an mean average precision (mAP) of 81.2%, precision (P) of 82.8%, recall (R) of 78.3%, and F₁-score (F₁) of 80.5%. The model’s parameter count is only 21.4 M and its FLOPs reach 66.6 G, meeting real-time detection requirements. Compared to the baseline network models, PCERT-DETR improves the P, R, F₁, and mAP by 15.0, 1.2, 8.5, and 6.8 percentage points, respectively. Furthermore, the performance evaluation experiments were carried out through ablation experiments, comparative detection model experiments and heat map visualization analysis, indicating that the model has a strong detection performance on the test set. The results confirm that the proposed model can accurately detect the number of missing rice seedlings. This study provides accurate information on the number of missing seedlings for subsequent reseeding operations, thus contributing to the improvement of precision farming practices. Full article

In the case of direct sowing of rice in Japan, cold stress is a critical constraint affecting seed germination and early seedling development, ultimately reducing crop productivity. We evaluated the effects of priming, with or without iron coating on the germination and vigor of rice seeds harvested in 2022, 2023, and 2024. The assessments were conducted at seven temperature conditions: 13 °C, 15 °C, 17 °C, 19 °C, 21 °C, 23 °C, and 25 °C. Seeds were primed with or without PEG6000; coated with or without a mixture of calcined gypsum and iron powder; and tested for germination percentage, germination speed, and seedling vigor index. Under optimal conditions, iron-coated seeds harvested in 2022 showed a significant increase in germination from 58% (non-coated without priming) to 76% (coated with priming), and the seedling vigor index improved from 615 to 890. Under cold stress (15 °C), the coated seeds of the same year achieved 68% germination with priming compared to 46% in non-coated seeds without priming, with a vigor index increase from 480 to 750. Similar improvements were observed in seeds from 2023 and 2024, although the effect was more prominent in older than younger seeds. These results indicate that iron seed coating in combination with PEG priming mitigates the negative impacts of seed aging and enhances tolerance to cold stress during germination. The technique offers a promising, low-cost approach to improving rice establishment in environments facing suboptimal seed storage and early-season cold temperatures, in particular, aiming for direct sowing methods. Full article

Accurate early-season crop classification is critical for food security, agricultural applications and policymaking. However, when classification is performed earlier, the available time-series data gradually become scarce. Existing methods mainly focus on enhancing the model’s ability to extract features from limited data to address this challenge, but the extracted critical phenological information remains insufficient. This study proposes a Cascade Learning Early Classification (CLEC) framework, which consists of two components: data preprocessing and a cascade learning model. Data preprocessing generates high-quality time-series data from the optical, radar and thermodynamic data in the early stages of crop growth. The cascade learning model integrates a prediction task and a classification task, which are interconnected through the cascade learning mechanism. First, the prediction task is performed to supplement more time-series data of the growing stage. Then, crop classification is carried out. Meanwhile, the cascade learning mechanism is used to iteratively optimize the prediction and classification results. To validate the effectiveness of CLEC, we conducted early-season classification experiments on soybean, corn and rice in Northeast China. The experimental results show that CLEC significantly improves crop classification accuracy compared to the five state-of-the-art models in the early stages of crop growth. Furthermore, under the premise of obtaining reliable results, CLEC advances the earliest identifiable timing, moving from the flowing to the third true leaf stage for soybean and from the flooding to the sowing stage for rice. Although the earliest identifiable timing for corn remains unchanged, its classification accuracy improved. Overall, CLEC offers new ideas for solving early-season classification challenges. Full article

Large weed infestation is a major problem in dry direct-seeded rice (DSR). Chemical weed control serves as a crucial component for integrated weed management in DSR. Over the last decade, herbicide use has increased from 42 to 55%, and the worldwide contamination of water resources and food by herbicides is a major health issue. In the present study, the use of herbicides in three different establishment methods of rice was examined with the objective to present and discuss the herbicide use pattern and cost of weed control. For this, a field-wide survey was conducted over an area of 165.4 ha in eight villages of the Tarn Taran District of Punjab, India. For two DSR methods, during the initial stage of crop growth, the weed infestation was reported to be less in moist fields sown with direct seeding (soil moisture in the field capacity stage) after pre-sowing irrigation (DSR-PSI). The herbicide use and cost of weed control under DSR-PSI conditions were similar to that of puddled transplanted rice, but were significantly lower than that of direct seeding in dry fields (rice seeds are sown in dry fields, and irrigation is applied immediately after sowing), i.e., DSR-IAS. Therefore, the DSR-PSI method of rice establishment can ensure minimum dependence on herbicides, as well as other benefits of direct seeding. Thus, there is a need to promote the DSR-PSI method over the DSR-IAS method among farmers in order to reduce herbicide use in DSR and ensure environmental safety. Full article

The integration of crop models and remote sensing data has become a useful method for monitoring crop growth status and crop yield based on data assimilation. The objective of this study was to use leaf area index (LAI) values and plant nitrogen accumulation (PNA) values generated from spectral indices to calibrate the Decision Support System for Agrotechnology Transfer (DSSAT) model using the Monte Carlo Markov Chain (MCMC) technique. The initial management parameters, including sowing date, sowing rate, and nitrogen rate, are recalibrated based on the relationship between the remote sensing state variables and the simulated state variables. This integrated technique was tested on independent datasets acquired from three rice field tests at the experimental site in Deqing, China. The results showed that the data assimilation method achieved the most accurate LAI (R² = 0.939 and RMSE = 0.74) and PNA (R² = 0.926 and RMSE = 7.3 kg/ha) estimations compared with the spectral index method. Average differences (RE, %) between the inverted initialized parameters and the original input parameters for sowing date, seeding rate, and nitrogen amount were 1.33%, 4.75%, and 8.16%, respectively. The estimated yield was in good agreement with the measured yield (R² = 0.79 and RMSE = 661 kg/ha). The average root mean square deviation (RMSD) for the simulated values of yield was 745 kg/ha. Yield uncertainty from data assimilation between crop models and remote sensing was quantified. This study found that data assimilation of crop models and remote sensing data using the MCMC technique could improve the estimation of rice leaf area index (LAI), plant nitrogen accumulation (PNA), and yield. Data assimilation using the MCMC technique improves the prediction of LAI, PNA, and yield by solving the saturation effect of the normalized difference vegetation index (NDVI). This method proposed in this study can provide precise decision-making support for field management and anticipate regional yield fluctuations in advance. Full article

The direct-seeded rice (DSR) system is poised to become the dominant rice cultivation method due to its advantages, including reduced water usage, less labor requirements, decreased greenhouse gas emissions, and improved adaptation to climate change. However, weeds, particularly jungle rice (Echinochloa colona), significantly hinder DSR and cause substantial yield losses. This study aimed to develop rice cultivars competitive against jungle rice through selective breeding, focusing on early seed germination (ESG) and seedling vigor (ESV). We utilized 181 early-backcross selective introgression breeding lines (EB-SILs) developed using Green Super Rice (GSR) technology by backcrossing Weed Tolerant Rice1 (WTR1) with three donor parents, Haoannong, Cheng Hui 448, and Y134. Using the tunable genotyping-by-sequencing (tGBS^®, Data2Bio Technologies, Ames, IA, USA) method, we identified 3971 common single nucleotide polymorphisms (SNPs) that facilitated the mapping of 19 novel quantitative trait loci (QTLs) associated with weed competitiveness—eight linked to ESG traits and eleven to ESV traits. Notably, all QTLs were novel except qRPH1, linked to relative plant height at 14 and 21 days after sowing. Key QTLs were located on chromosomes 2, 3, 5, 6, 8, 9, 10, and 12. Candidate genes identified within these QTLs are implicated in the plant’s response to various abiotic and biotic stresses. Our findings enhance the understanding of the genetic basis for ESG and ESV traits critical for weed competitiveness, supporting marker-assisted and genomic selection approaches for breeding improved rice varieties. Furthermore, this research lays the groundwork for employing gene expression, cloning, and CRISPR editing strategies to combat jungle rice, with potential applications for other weed species and contributing to effective integrated weed management in the DSR system. Full article

This research explores, using discrete element method (DEM) simulations, the behavior of rice seed infiltration into soil when it is deployed via unmanned aerial vehicle (UAV)-mounted systems. Five distinct sowing strategies were analyzed to evaluate their effectiveness in embedding seeds within paddy soil: gravitational drop, centrifugal spreading, airflow propulsion, pneumatic discharge, and pneumatic shooting. A two-step analysis was performed. Initially, the flight dynamics of rice seeds were modeled, and the influence of air and water drag forces were accounted for. Subsequently, soil penetration was simulated with DEM based on the material properties and contact parameters sourced from the existing literature. The results show that the pneumatic methods effectively penetrated the soil, with pneumatic shooting proving to be the most efficient due to its superior impact momentum. Conversely, the methods that failed to penetrate left seeds on the soil surface. These findings demonstrate the necessity to enhance UAV sowing technology to improve penetration depth while maintaining operational efficiency, and they also offer crucial insights for the progress of UAV applications in agriculture. Full article

Rice (Oryza sativa L.) is one of the most crucial cereal crops worldwide, serving as a staple food for a significant portion of the global population. Rice is the second most important staple food crop in Pakistan after wheat, and it is also a major export commodity. Concerning this, the current study aimed to evaluate the effects of different seed rates on the yield and yield-contributing parameters of rice varieties. The experiment was conducted over two consecutive kharif summer seasons, from 2020–21 and 2021–22, at the Pakistan Agricultural Research Council (PARC) Rice Program experimental area in Kala Shah Kaku, Lahore, Pakistan, by following a factorial randomized complete block design with three replications using coarse rice (KSK-133) and fine rice (Super Basmati) varieties. Different seed rates, including 27 kg/ha, 22 kg/ha, 17 kg/ha, and 12 kg/ha, were tested during the experiment. Different growth and yield-related attributes, such as plant height (cm), the number of productive tillers per plant, panicle length (cm), the number of grains per panicle, and grain yield (m⁻²), were recorded. The results showed that for KSK-133 and Super Basmati, the maximum grain yield was achieved at a sowing rate of 27 kg/ha in direct seed rice (DSR). The lowest yield was observed at a seeding rate of 12 kg/ha for KSK-133 and Super Basmati in DSR. Both basmati (Super Basmati) and coarse-grain (KSK-133) varieties exhibited similar responses to seed rate treatments, with the optimal performance observed at the highest seed rate of 27 kg/ha for both seasons. Grains per panicle and thousand grain weight emerged as critical determinants of yield, highlighting the need to balance vegetative growth with reproductive development. Breeding programs should focus on developing varieties that balance vegetative traits like tiller production and panicle length with reproductive traits to enhance overall yield. Based on these findings, it is concluded that using an optimal seeding rate of 27 kg/ha for direct-seeded fine and coarse rice varieties is beneficial in terms of tillers and higher yield. Full article

To address challenges in seed feeding stability and seeding uniformity in agricultural practices, this study aimed to introduce a cylindrical air-assisted drill sowing device (CADSD) designed for rapeseed, wheat, and rice (RWR). The device features a prototype hill-feeding mechanism that addresses problems related to seed feeding, airflow disruptions, and seed–wall collisions. Comprehensive bench tests, Discrete Element Method (DEM) simulations, and preliminary field experiments were conducted to evaluate the seed-feeding stability characteristics and optimize the structural parameters of the air-assisted drill sowing system, enhancing seeding uniformity and operational efficiency. The optimal operating speed range is between 4 and 5 km/h. When the seed feeding speed is 30 to 38 r/min, the coefficient of variation of the seed supply rate stability is less than 0.55%, and the relative error between the theoretical and the experimental actual values of the RWR supply rate regression model is less than 2%, further supporting the effectiveness of the device. A preliminary field test revealed a seeding uniformity coefficient of variation (CV) of 3.44% and an emergence rate of 88%, closely aligning with the desired metrics. The CADSD effectively sows multiple crop types with improved precision and uniformity, handling diverse seed types and sizes without requiring equipment modifications, highlighting its innovative impact on agricultural technology in the precise seeding of RWR. Full article

Mechanical transplanting has become an important part of modern Chinese rice production, and an inadequate sowing rate severely inhibits rice seedling growth and development. Precision drill sowing is an effective method for obtaining higher quality seedlings during machine transplanting. There is a lack of systematic research on the precision drilling of rice. Therefore, we carried out research on the quality of machine-transplanted seedlings and precision drill sowing transplantation. A greenhouse experiment (Liaoning Rice Research Institute) and field experiment (Sujiatun District, Shenyang City, Liaoning Province, China) were conducted between 2020 and 2021 to analyze the influence of precision drill sowing on rice growth and yield. Precision drill sowing was conducted at four sowing rates (3400, 3600, 3800, and 4000 seeds/tray), and traditional broadcasting was also conducted at a sowing rate of 4000 seeds/tray. We evaluated the seedling rice quality, physiological and biochemical characteristics and transplanting quantity. The results indicated that precision drill sowing at a sowing rate of 3400 seeds/tray resulted in the highest plumpness value (0.18) and seedling strength index (0.42) of individual plants. However, the empty hill rate was as high as 3.05%, which did not satisfy the field seedling number requirement. Precision drill sowing at a sowing rate of 4000 seeds/tray resulted in the lowest physiological (the average levels of SOD, POD and soluble protein were 311.78 µg/g, 8.25 µg/g and 1.28 µg/g) and biochemical indices of individual plants. The damaged seedling rate increased by 2.07%, and the dead seedling rate increased by 0.25%, resulting in poor seedling and transplanting quality. In this study, 3800 seeds/tray was the best option and had the highest yields of 10,776.60 kg/ha and 10,730.85 kg/ha over the two years. This sowing approach performs well in terms of field transplanting, provides a balance point between seedling number and quality and is conducive to rice yield production. The results of this study are important for improving rice seedling quality, enhancing field transplanting quantity and increasing rice yield and food security. Full article

The handling of the remnants of rice crops in the field is not an easy operation, and farmers prefer burning, which causes air pollution, smog, and disease. This research reports the development of a novel precision crop seeder by handling the remnants of previous crops through mechanization. The precision seeder performed multiple operations in a single path, viz, chop residues, incorporate into soil, make mini trenches, and sow wheat with fertilizer application. The precision seeder has a 2040 mm working width, and specially designed C-type blades are used to shred the crop residue. A multiple-speed gearbox with a gear ratio of 1:0.52 is installed, with a further set of spur gears with 16, 18, and 20 teeth that provide 225, 250, 310, and 350 RPMs to the main rotor. In the middle of the seeder, after the main rotor shaft, 11 V-shaped trencher plates are fixed on the trencher roller for the making of trenches. The trencher roller is powered by star wheels, which showed good results. A zero-tillage-type sharp tip edge novel seeder unit was developed for the precise placement of seed and fertilizer. Seed and fertilizer were placed into the mini trenches through 11 seeder units through a ground wheel calibration system. The field capacity of the precision seeder was 0.408 ha/h and the operational cost was calculated 40.68 USD/ha. The seeder showed good results, with the production of 5028 kg/ha compared to conventional methods. The precision seeder provides a mechanized solution for wheat sowing with minimal operational costs by enhancing organic matter in soil with 13% more yield. Full article

Paddy (Oryza sativa) is one of the most consumed food grains in the world. The process from its sowing to consumption via harvesting, processing, storage and management require much effort and expertise. The grain quality of the product is heavily affected by the weather conditions, irrigation frequency, and many other factors. However, quality control is of immense importance, and thus, the evaluation of grain quality is necessary. Since it is necessary and arduous, we try to overcome the limitations and shortcomings of grain quality evaluation using image processing and machine learning (ML) techniques. Most existing methods are designed for rice grain quality assessment, noting that the key characteristics of paddy and rice are different. In addition, they have complex and expensive setups and utilize black-box ML models. To handle these issues, in this paper, we propose a reliable ML-based IoT paddy grain quality assessment system utilizing affordable sensors. It involves a specific data collection procedure followed by image processing with an ML-based model to predict the quality. Different explainable features are used for classifying the grain quality of paddy grain, like the shape, size, moisture, and maturity of the grain. The precision of the system was tested in real-world scenarios. To our knowledge, it is the first automated system to precisely provide an overall quality metric. The main feature of our system is its explainability in terms of utilized features and fuzzy rules, which increases the confidence and trustworthiness of the public toward its use. The grain variety used for experiments majorly belonged to the Indian Subcontinent, but it covered a significant variation in the shape and size of the grain. Full article

Climate is the most important environmental factor influencing yield during rice growth and development. To investigate the relationships between climate and yield under different crop rotation patterns and planting methods, three typical rotation patterns (vegetable–rice (V), rape–rice (R), and wheat–rice (W)) and two mechanical planting methods (mechanical transplanting (T1) and mechanical direct seeding (T2)) were established. The results showed that compared to the V rotation pattern, the average daily temperature (ADT) during the sowing to heading stage increased under both R and W rotation patterns, which significantly shortened the growth period. Thus, the effective accumulated temperature (EAT), photosynthetic capacity, effective panicle (EP), and spikelet per panicle (SP) under R and W rotation patterns significantly decreased, leading to reductions in grain yield (GY). VT2 had a higher ratio of productive tillers (RPT), relative chlorophyll content (SPAD), leaf area index (LAI), and net photosynthetic rate (Pn) than those of VT1, which significantly increased panicle dry matter accumulation (DMA), resulting in an increase in GY. Although RT2 and WT2 had a higher RPT than those of RT1 and WT1, the GY of RT1 and WT1 decreased due to the significant reductions in EAT and photosynthetic capacity. Principal component analysis (PCA) showed that the comprehensive score for different rotation patterns followed the order of V > R > T with VT2 ranking first. The structural equation model (SEM) showed that EAT and ADT were the most important climate factors affecting yield, with total effects of 0.520 and −0.446, respectively. In conclusion, mechanical direct seeding under vegetable–rice rotation pattern and mechanical transplanting under rape–rice or wheat–rice rotation pattern were the rice-planting methods that optimized the climate resources in southwest China. Full article

The tillage method determines several soil physical parameters that affect the emergence of post-rice chickpea (Cicer arietinum L.) in the Indo-Gangetic Plain of South Asia. Mechanised row-sowing with minimum soil disturbance and crop residue retention in medium-to-heavy-textured soils will alter the seedbed when compared to that prepared after traditional full tillage and broadcast sowing. Whilst minimum soil disturbance and timely sowing may alleviate the soil water constraint to crop establishment, other soil physical properties such as soil strength, bulk density, and aggregate size may still inhibit seedling emergence and root elongation. This study’s objective was to determine the limitations to chickpea crop establishment with increasing bulk density and soil strength, and different aggregate sizes below and above the seed. In two growth cabinet studies, chickpea seed was sown in clay soil with (i) a bulk density range of 1.3–1.9 Mg m⁻³ (Experiment 1) and (ii) the combination of bulk densities (1.3 and 1.8 Mg m⁻³) and aggregate sizes (<2 mm and >4 mm) above and below the seed (Experiment 2). Root length was significantly reduced with increasing bulk density (>1.4 Mg m⁻³), and soil strength impeded early root growth at >1 MPa. Where main root growth was impeded due to high bulk density and soil strength, a greater proportion of total root growth was associated with the elongation of lateral roots. The present study suggests that the soil above the seed needs to be loosely compacted (<1.3 Mg m⁻³) for seedling emergence to occur. Further research is needed to determine the size of the soil aggregates, which optimise germination and emergence. We conclude that soil strength values typical of field conditions in the Indo-Gangetic Plain at sowing will impede the root growth of chickpea seedlings. This effect can be minimised by changing tillage operations to produce seedbed conditions that are within the limiting thresholds of bulk density and soil strength. Full article

This study explored the effects of the sowing stage and nitrogen application rate on the grain yield and its allocation of light-temperature resources over a 9-year experiment from 2011 to 2019. Measurement indicators include the effective accumulative temperature on different growth durations, leaf area index (LAI), above-ground biomass production, and harvest index (HI). Methods: A split-plot design was arranged in the treatment, with N supply as the main plot and the sowing stage as the subplot. The main plots consisted of two nitrogen treatments: low nitrogen (LN: 120 kg ha⁻¹) and high nitrogen (HN: 180 kg ha⁻¹). The subplots contained two sowing stages: the early sowing stage (ES) and the late sowing stage (LS). Results: Compared with LNLS, LNES, and HNLS from 2011 to 2019, HNES of HHZ increased the grain yield by 9.5%, 2.5%, and 5.3%, while the difference in grain yield in YY8 was higher than HHZ, especially under HNES. Compared with LNLS, LNES, and HNLS from 2011 to 2019, HNES of HHZ increased the panicle number by 6.0%, 5.9%, and 1.0%, and HNES of YY8 increased by 12.7%, 11.4%, and 3.8%. Compared with HNLS of HHZ, LNES, LNLS, and HNES decreased the spikelets per panicle by 2.3%, 2.9%, and 1.1%, and decreased by 3.5%, 1.9%, and 2.2% in YY8. The early sowing or increasing N supply significantly increased the dry matter accumulated, grain weight, LAI, and HI. The higher grain yield in LNES was more closely related to the average temperature and the number of spikelets per panicle. The grain yield in HNES was more dependent on the effective accumulative temperature. Conclusions: Sowing in mid-May and increasing the N application (180 kg ha⁻¹) are beneficial to the allocation of light temperature and the increase in yield. Therefore, this research provides a theoretical basis for improving rice yield and optimizing the utilization of light-temperature resources in the future. Full article