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GPS-Guided Precision Sowing on the Productivity of Legume Crops

W
Wenzhe Shao1
C
Chich-Jen Shieh2,*
1Faculty of Intelligence Technology, Shanghai Institute of Technology, No. 100 Haiquan Road, Shanghai, Fengxian District, 201418, China.
2Excellent Center of Disruptive Innovative Technology for Education, Chulalongkorn University, Bangkok, Thailand.

  • Submitted24-12-2025|

  • Accepted16-04-2026|

  • First Online 24-04-2026|

  • doi 10.18805/LRF-926

ABSTRACT

Background: Precision agriculture has transformed the face of farming with modern technologies, including GPS guided sowing. Legume crops-which are critical for the purpose of sustainable agriculture owing to their nitrogen-dropping capacity, could be improved substantially with fine placement of seeds. However, the traditional sowing method usually causes non-uniform seed distribution that will influence germination rate and the efficiency of a crop in general. The other study reviewed the yield and efficiency of legume crop production using GPS guided precision sowing.

Methods: A field experiment with GPS and precision seed GPS enabled for the planting of legume crops was done versus direct conventional sowing. Collected parameters, reproduced on seed placement, germination, plant population and final yield were recorded. The data was processed by statistical software for the evaluation of precision sowing impact on the performance of the crop.

Result: The study found that GPS precision sowing drastically changed the seed uniform distribution, providing the highest germination and best plant spacing. Thus, legume yields increased on average by 15-20% over general sowing practices. It also increased resource efficiency with less seed going to waste and improved soil health. Overall, these results imply that the adoption of GPS guided sowing for legumes leads to higher productivity and sustainability.

INTRODUCTION

Precision agriculture (PA) aims to optimise the use of mechanical, human and natural resources while minimising disturbance to agroecosystems. Rising costs of seeds, fertilisers and crop protection chemicals have increased the importance of technologies that enhance resource-use efficiency and improve land productivity. With rapid population growth and increasing pressure on global food systems, agriculture faces major challenges that require more efficient and sustainable production methods (Kumar et al., 2017; Albiero et al., 2022). Climate change, urbanisation and the growing demand for high-quality food are further reducing available arable land and threatening agricultural productivity and environmental sustainability (Kadagonda and Yalla, 2023). Consequently, modernising agricultural systems through precision technologies has become increasingly important.
       
Precision agriculture is supported by several technological innovations that enable site-specific crop management. These include aerial imaging, remote sensing, smart sensors, automated machinery and data-driven decision systems (Bauer et al., 2020). Such technologies facilitate monitoring and management across all stages of crop production-from soil preparation and sowing to crop growth monitoring and harvesting. By integrating diverse data sources, PA allows farmers to make more precise decisions regarding fertiliser application, irrigation management and pest control, thereby improving productivity while reducing environmental impacts (Ma et al., 2024; Koike et al., 2023; Ghuriani et al., 2023).
       
Technological advancements in agricultural monitoring systems have also expanded rapidly. For example, precision livestock farming (PLF) technologies have evolved from simple electronic milk meters to sophisticated wearable sensors and integrated monitoring systems that track physiological and behavioural parameters in real time (Shah et al., 2017). Similarly, in crop production systems, drones, satellite imagery and machinery-mounted sensors enable continuous monitoring of fields and support targeted management interventions. In Europe, approximately 70% of fertiliser and pesticide application equipment is compatible with smart or ISO bus-enabled devices, although only about 25% of farmers actively utilise PA technologies (Rajeswari et al., 2017). Adoption rates vary among countries, with around 36% of farmers in Germany, Finland and Denmark reporting some experience with precision agriculture technologies. According to the European Parliament, PA represents a modern agricultural management approach that employs digital technologies to monitor and optimise crop production (Zhang et al., 2023; Bong-Hyun et al., 2024; Min et al., 2024). Adoption of these technologies is influenced by factors such as farm size, production systems, socio-economic conditions and technology accessibility (Avola et al., 2024).
       
Among the various technologies supporting precision agriculture, global positioning system (GPS)-based guidance systems play a central role in improving the accuracy of field operations. GPS enables precise positioning of agricultural machinery, facilitating accurate field mapping, automated steering and controlled application of agricultural inputs. In sowing operations, GPS-guided systems allow precise row spacing, uniform seed distribution and consistent planting depth, thereby reducing overlaps and gaps during planting (Singh et al., 2021). Precision farming technologies have demonstrated considerable potential in improving resource efficiency; for example, Anastasiou (2023) reported that precision technologies can reduce herbicide use by up to 97%, insecticide use by 70% and weed densities by 89%.
       
Legume crops represent an important component of sustainable agricultural systems due to their ecological and agronomic benefits. The Leguminosae family is the third-largest group of flowering plants, comprising approximately 800 genera and more than 20.000 species (Lu et al., 2024). Many legumes are cultivated as grain crops, while others contribute to soil fertility through biological nitrogen fixation. Despite their benefits, legumes occupy only a relatively small portion of global arable land, which is largely dominated by cereal production. Soybeans account for the majority of global legume cultivation, with approximately 117.72 million hectares cultivated in 2014, largely supported by specialised production systems. In contrast, the cultivation of many other legumes remains limited due to weak supply chains, market constraints and limited technological adoption (Kanwal et al., 2022; Dadrasi et al., 2024).
       
Uniform plant establishment is particularly important for legume crops because their productivity depends strongly on plant density, spatial distribution and early root development. Uneven seed placement can result in irregular crop stands, increased plant competition and reduced nodulation efficiency, ultimately affecting yield stability. Optimal spacing between plants allows better access to light, nutrients and soil moisture while supporting effective symbiotic nitrogen fixation. However, traditional sowing methods often result in inconsistent seed placement and variable planting depth, which can negatively affect germination and early crop growth.
       
Recent developments in precision agriculture technologies offer opportunities to improve planting accuracy and resource efficiency. For example, precision cultivation techniques integrating remote sensing, soil moisture sensors, GPS-guided machinery and data analytics have been used to optimise resource utilisation and enhance crop productivity (Chaudhari, 2024). Similarly, AI-based precision irrigation systems have demonstrated improved water-use efficiency and increased pea yields through real-time data-driven irrigation scheduling (Kim and Alzubi, 2024). Nevertheless, empirical research specifically examining the performance of GPS-guided precision sowing in legume production systems remains limited.
       
Despite the potential advantages of GPS-guided sowing, several uncertainties remain regarding its agronomic performance and practical adoption. In particular, limited evidence exists on its influence on seed placement accuracy, plant establishment, nodulation and yield performance under different agro-climatic conditions. Furthermore, economic feasibility and accessibility remain important considerations for smallholder farmers and integration of GPS-guided sowing with other precision agriculture technologies requires further investigation.
       
Therefore, the present study evaluates the performance of GPS-guided precision sowing in legumes compared with traditional sowing methods. The study analyses seed placement accuracy, germination rate, plant establishment, crop density and yield performance under field conditions. Additionally, it examines resource-use efficiency in terms of seed utilisation and input optimisation while considering potential economic and adoption barriers. The study addresses the following research questions
•   How does GPS-guided sowing improve seed placement accuracy compared with traditional sowing methods?
•   What are its effects on germination, early plant growth and crop yield?
•   How does GPS-guided sowing influence resource efficiency, particularly seed utilisation?
•   What economic and adoption barriers may limit the implementation of this technology?

MATERIALS AND METHODS

Study area
 
The research was conducted in Madhya Pradesh, India, a major agricultural hub known for legume cultivation, particularly soybean, chickpea and pigeon pea. The selected experimental site had loamy soil with moderate fertility and received an average annual rainfall of 1.000-1.200 mm, typical of the semi-arid tropical climate.
 
Experimental design
 
The experimental setup followed a comparative field trial approach rather than a randomized block design (RBD), as only two plots were used and treatments were not replicated across multiple blocks. Therefore, the study represents a field-scale comparative case study rather than a statistically replicated experiment. The experiment was conducted on a 10-hectare farm divided into two equal plots (5 ha each) representing two sowing approaches:
•  Precision sowing plot (5 ha) - Seeds were sown using a GPS-enabled precision seeder (John Deere 1780 Planter equipped with SF3 guidance providing ±3 cm pass-to-pass accuracy, variable seed rate control and hydraulic downforce system), ensuring uniform seed placement depth and spacing.
•  Conventional sowing plot (5 ha) - Seeds were sown using traditional manual or tractor-drawn sowing methods, which typically result in greater variability in seed placement.
       
Because only one plot per treatment was available, treatment level replication was not possible. To characterize within-plot variability, ten randomly selected quadrats (1 m² each) were used within each plot to measure germination rate, plant population density and seed spacing. These quadrats represent subsamples rather than independent experimental replications. Table 1 summarizes the experi-mental design.

Table 1: Experimental design.


 
Crop selection and sowing parameters
 
Table 2 summarizes the crop selection and sowing parameters used in this study.
•  Legume crops studied: Chickpea (Cicer arietinum), Pigeon Pea (Cajanus cajan) and Soybean (Glycine max).
•  Sowing time: Kharif season (June-July) for soybean and pigeon pea; Rabi season (October-November) for chickpea.
•  Seed rate and spacing:
o  Chickpea - 40 kg/ha, spacing 30 cm ×  10 cm
o  Pigeon Pea - 15 kg/ha, spacing 60 cm × 20 cm
o  Soybean - 80 kg/ha, spacing 45 cm × 5 cm

Table 2: Crop selection and sowing parameters.


 
Data collection parameters
 
Table 3 presents the data collection parameters used in this study.

Table 3: Data collection parameters.



Seed placement uniformity - Seed placement was evaluated by physically measuring inter-plant spacing along crop rows after seedling emergence. Measurements were taken within ten randomly selected quadrats (1 m2 each) per plot. The distance between consecutive plants was measured using a measuring tape and spacing uniformity was calculated as the percentage of plants falling within ±20% of the recommended plant spacing.
 
Germination rate (%) - Number of emerged seedlings per unit area after 15 days, counted in ten randomly selected quadrats (1 m² each) per plot.
 
Plant population density - Number of plants per square meter measured 30 days after sowing within the sampling quadrats.
 
Yield assessment (kg/ha) - Total grain yield measured at harvest and converted to kilograms per hectare based on the harvested plot area.
 
Resource efficiency (Seed wastage) - Seed wastage was estimated by comparing the total quantity of seed sown with the number of successfully established plants. Seed wastage percentage (SW%) was calculated as:

 
Data analysis
 
Within each plot, ten randomly selected quadrats (1 m2) were used as subsampling units to characterize within-plot variability for germination rate, plant population density and seed spacing. These quadrats represent subsamples rather than independent experimental replications. Accordingly, the collected data were analysed using descriptive statistics and mean values and observed ranges were calculated for the measured parameters, including germination rate, plant density, yield, seed placement uniformity and seed wastage. The results therefore provide a comparative field-scale assessment of GPS-guided precision sowing and conventional sowing under practical farm conditions.

RESULTS AND DISCUSSION

Seed placement accuracy
 
Table 4 shows that GPS-guided precision sowing substantially improved seed placement accuracy compared to conventional sowing methods. GPS-guided precision sowing produced noticeably more uniform seed placement compared with conventional sowing across the studied legume crops. Field measurements of plant spacing taken within randomly selected quadrats showed that seeds placed using the GPS-enabled precision seeder were distributed more consistently along crop rows.

Table 4: Seed placement uniformity under precision and conventional sowing.


       
Seed placement uniformity was calculated as the percentage of plants whose spacing fell within ±20% of the recommended intra-row spacing for each crop. Using this metric, the precision sowing plot achieved an average spacing uniformity of approximately 95%, whereas the conventional sowing plot showed a considerably lower uniformity of about 70%.
       
The improved uniformity observed under precision sowing indicates that GPS-guided planting systems can significantly reduce irregular seed spacing, which commonly occurs in conventional sowing due to manual seed distribution or uneven planter operation. More consistent spacing contributes to uniform crop establishment and reduces intra-row competition among plants.
 
Germination rate
 
Germination performance was evaluated 15 days after sowing using quadrat-based plant counts within each plot (Table 5). Across the three legume crops studied, chickpea, soybean and pigeon pea, germination rates were consistently higher in the precision sowing plot compared with the conventional sowing plot.

Table 5: Germination rate for precision and conventional methods.


       
The observed germination rate under precision sowing ranged between 85-90%, whereas the conventional sowing plot showed germination rates ranging from 70-80%. These results suggest that improved seed placement and consistent sowing depth provided by GPS-guided planting may enhance seed-to-soil contact and create more favorable conditions for seed germination.
       
The highest germination rate was observed in soybean under precision sowing, while pigeon pea exhibited the lowest germination under conventional sowing. Overall, the results indicate that improved spatial distribution of seeds contributes to better crop establishment across different legume species.
 
Plant population density
 
Table 6 presents the plant population density achieved under precision and conventional sowing methods. Plant population density was measured 30 days after sowing within the same quadrats used for germination assessment. The precision sowing plot consistently showed higher plant population densities across the three crops compared with the conventional sowing plot. Average plant population density in the precision sowing plot ranged from 18 to 22 plants per square meter, while the conventional sowing plot showed lower densities ranging from 15 to 18 plants per square meter.

Table 6: Plant population density for precision and conventional procedures.


       
The higher plant density observed in the precision sowing plot reflects more efficient seed placement and reduced seed loss during sowing. Uniform plant spacing also helps minimize competition for water, nutrients and sunlight during early growth stages.
 
Yield
 
Grain yield was measured at crop maturity and converted to kilograms per hectare based on the harvested area (Fig 1). Across the three legume crops evaluated, the precision sowing plot consistently produced higher yields than the conventional sowing plot. Average yields in the precision sowing plot ranged from 1500 to 1600 kg/ha, while the conventional sowing plot produced yields between 1300 and 1400 kg/ha.

Fig 1: Bar chart comparing crop yield under precision and conventional sowing.


       
Across crops, precision sowing resulted in an approximate 12-15% increase in yield compared with conventional sowing. The yield advantage is likely associated with improved germination, better plant spacing and more consistent crop stands established by the precision planting system.
 
Resource efficiency
 
In terms of resource efficiency, the precision sowing plot had only 6% seed wastage, whereas the conventional sowing plot experienced 17% seed wastage (Fig 2). The precision sowing method significantly reduced seed wastage by ensuring that seeds were placed accurately, reducing overlap and underplanting. This not only cuts down on seed costs but also contributes to more sustainable farming by minimizing input waste.

Fig 2: Pie charts comparing seed wastage in precision and conventional sowing.


       
The reduction in seed wastage highlights a key advantage of precision sowing: Enhanced input efficiency. In a country like India, where agricultural input costs are a significant burden on farmers, such savings are critical for improving the economic sustainability of farming operations.
       
The results of this study indicate that GPS-guided precision sowing may improve several aspects of crop establishment and productivity under the conditions of the present field trial. Notable improvements were observed in seed placement accuracy, germination rate, plant population density, yield and resource efficiency. These findings align with precision agriculture research that highlights the potential benefits of spatial accuracy and optimized input application.
       
Seed placement accuracy increased from 70% under conventional sowing to 95% with GPS-guided methods. This demonstrates the system’s ability to maintain consistent row spacing and depth. Better placement reduces gaps and seed clustering, which often cause uneven stands in traditional systems. Previous studies confirm that precision seeding improves spatial uniformity, leading to stronger early crop vigor and consistent emergence (Pareek et al., 2022). Senthilkumar et al., (2025) reported that GPS- and AI-enabled seeding robots enhanced depth by 28.57%, spacing by 33.33% and yield by 30.77%, while reducing water and fertilizer use by 25%. Our results support these findings, showing that precise placement directly affects crop performance and input efficiency.
       
The germination rate was higher in precision plots (88%) compared to conventional plots (75%). This improvement is due to better seed-to-soil contact and consistent depth placement. Germination is sensitive to micro-environmental conditions, including moisture and soil structure. Irregular depth or spacing can delay or prevent emergence due to crusting, clods, or uneven moisture. Lamichhane et al., (2019) emphasized that accurate placement reduces these risks, creating uniform conditions for all seeds. Reed et al., (2022) also found that optimal depth and spacing improved legume emergence. Our results confirm that GPS-guided sowing reduces variability in emergence and supports a more uniform crop stand.
       
Plant population density reached 20 plants/m² in precision plots, compared to 16 plants/m² in conventional plots. This increase complements the higher germination rate and indicates successful early establishment. Uniform spacing reduces intra-row competition and ensures adequate access to light, nutrients and moisture for each plant. Similar findings in maize and wheat show that optimized spacing enhances canopy development and increases biomass accumulation and yield (Djaman et al., 2021; Ghimirey et al., 2024). Manu et al., (2024) demonstrated that aerial yield mapping and targeted management of low-yield zones could improve overall production by up to 20%. These results support the importance of stand consistency in maximizing crop potential.
       
Yield increased by 14.8%, from 1,350 kg/ha in conventional plots to 1,550 kg/ha in precision plots. This gain reflects improvements in early crop establishment, including better placement, higher germination and improved spacing. Precision sowing reduces plant stress and optimizes input use. Similar outcomes have been reported in cereals and legumes, where precision sowing reduced seedling variability and improved productivity (Feng et al., 2024). Seed wastage decreased from 17% in conventional plots to 6% in precision plots. This highlights a clear advantage in input efficiency. In India, where input costs are high and land is limited, reducing waste while maintaining yield is crucial. McDonald et al., (2024) also found that precision planting reduces input overuse without affecting crop productivity.
       
Overall, the observed improvements across measured parameters suggest that GPS-guided precision sowing has the potential to enhance crop establishment and input efficiency under practical farm conditions. However, the present study represents a single-season, single-location comparative field assessment and the lack of replicated experimental plots limits the ability to generalize the findings across diverse cropping systems or agroecological regions. Soil type, climate variability and management practices may influence the magnitude of these effects. Future research should incorporate replicated multi-location and multi-season trials to validate these observations and to better quantify the agronomic and economic benefits of precision sowing technologies. Integration with remote sensing tools (Manu et al., 2024) and germination prediction models (Lamichhane et al., 2019) may further support adaptive management and improved decision-making in precision agriculture systems.

CONCLUSION

This study indicates that GPS-guided precision sowing can improve seed placement accuracy, germination rate, plant population density, yield and resource efficiency compared with conventional sowing under the field conditions evaluated. Precision sowing resulted in an observed yield increase of approximately 14-15% and reduced seed wastage, suggesting potential advantages in crop establishment and input-use efficiency. These improvements highlight the value of accurate seed placement for optimizing plant spacing and improving early crop development. However, the findings should be interpreted cautiously because the study was conducted at a single location during one growing season and without replicated experimental plots. Therefore, the results represent a comparative field-scale observation rather than broadly generalizable evidence across cropping systems. Future research should include multi-location and multi-season trials with replicated designs to validate these findings. Integrating GPS-guided sowing with remote sensing, real-time sensors and economic assessment may further clarify its potential role in sustainable and precision-based agricultural systems.

ACKNOWLEDGEMENT

Thanks to the reviewers for their hard work.
 
Authors’ contributions
 
All authors contributed toward data analysis, drafting and revising the paper and agreed to be responsible for all the aspects of this work.
 
Funding details
 
This research received no external funding.
 
Data availability
 
The data analysed/generated in the present study will be made available from corresponding authors upon reasonable request.
 
Availability of data and materials
 
Not applicable.
 
Use of artificial intelligence
 
Not applicable.
 
Declarations
 
Authors declare that all works are original and this manuscript has not been published in any other journal.

CONFLICT OF INTEREST

Authors declare that they have no conflict of interest.

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    Full Research Article

    GPS-Guided Precision Sowing on the Productivity of Legume Crops

    W
    Wenzhe Shao1
    C
    Chich-Jen Shieh2,*
    1Faculty of Intelligence Technology, Shanghai Institute of Technology, No. 100 Haiquan Road, Shanghai, Fengxian District, 201418, China.
    2Excellent Center of Disruptive Innovative Technology for Education, Chulalongkorn University, Bangkok, Thailand.

    • Submitted24-12-2025|

    • Accepted16-04-2026|

    • First Online 24-04-2026|

    • doi 10.18805/LRF-926

    ABSTRACT

    Background: Precision agriculture has transformed the face of farming with modern technologies, including GPS guided sowing. Legume crops-which are critical for the purpose of sustainable agriculture owing to their nitrogen-dropping capacity, could be improved substantially with fine placement of seeds. However, the traditional sowing method usually causes non-uniform seed distribution that will influence germination rate and the efficiency of a crop in general. The other study reviewed the yield and efficiency of legume crop production using GPS guided precision sowing.

    Methods: A field experiment with GPS and precision seed GPS enabled for the planting of legume crops was done versus direct conventional sowing. Collected parameters, reproduced on seed placement, germination, plant population and final yield were recorded. The data was processed by statistical software for the evaluation of precision sowing impact on the performance of the crop.

    Result: The study found that GPS precision sowing drastically changed the seed uniform distribution, providing the highest germination and best plant spacing. Thus, legume yields increased on average by 15-20% over general sowing practices. It also increased resource efficiency with less seed going to waste and improved soil health. Overall, these results imply that the adoption of GPS guided sowing for legumes leads to higher productivity and sustainability.

    INTRODUCTION

    Precision agriculture (PA) aims to optimise the use of mechanical, human and natural resources while minimising disturbance to agroecosystems. Rising costs of seeds, fertilisers and crop protection chemicals have increased the importance of technologies that enhance resource-use efficiency and improve land productivity. With rapid population growth and increasing pressure on global food systems, agriculture faces major challenges that require more efficient and sustainable production methods (Kumar et al., 2017; Albiero et al., 2022). Climate change, urbanisation and the growing demand for high-quality food are further reducing available arable land and threatening agricultural productivity and environmental sustainability (Kadagonda and Yalla, 2023). Consequently, modernising agricultural systems through precision technologies has become increasingly important.
           
    Precision agriculture is supported by several technological innovations that enable site-specific crop management. These include aerial imaging, remote sensing, smart sensors, automated machinery and data-driven decision systems (Bauer et al., 2020). Such technologies facilitate monitoring and management across all stages of crop production-from soil preparation and sowing to crop growth monitoring and harvesting. By integrating diverse data sources, PA allows farmers to make more precise decisions regarding fertiliser application, irrigation management and pest control, thereby improving productivity while reducing environmental impacts (Ma et al., 2024; Koike et al., 2023; Ghuriani et al., 2023).
           
    Technological advancements in agricultural monitoring systems have also expanded rapidly. For example, precision livestock farming (PLF) technologies have evolved from simple electronic milk meters to sophisticated wearable sensors and integrated monitoring systems that track physiological and behavioural parameters in real time (Shah et al., 2017). Similarly, in crop production systems, drones, satellite imagery and machinery-mounted sensors enable continuous monitoring of fields and support targeted management interventions. In Europe, approximately 70% of fertiliser and pesticide application equipment is compatible with smart or ISO bus-enabled devices, although only about 25% of farmers actively utilise PA technologies (Rajeswari et al., 2017). Adoption rates vary among countries, with around 36% of farmers in Germany, Finland and Denmark reporting some experience with precision agriculture technologies. According to the European Parliament, PA represents a modern agricultural management approach that employs digital technologies to monitor and optimise crop production (Zhang et al., 2023; Bong-Hyun et al., 2024; Min et al., 2024). Adoption of these technologies is influenced by factors such as farm size, production systems, socio-economic conditions and technology accessibility (Avola et al., 2024).
           
    Among the various technologies supporting precision agriculture, global positioning system (GPS)-based guidance systems play a central role in improving the accuracy of field operations. GPS enables precise positioning of agricultural machinery, facilitating accurate field mapping, automated steering and controlled application of agricultural inputs. In sowing operations, GPS-guided systems allow precise row spacing, uniform seed distribution and consistent planting depth, thereby reducing overlaps and gaps during planting (Singh et al., 2021). Precision farming technologies have demonstrated considerable potential in improving resource efficiency; for example, Anastasiou (2023) reported that precision technologies can reduce herbicide use by up to 97%, insecticide use by 70% and weed densities by 89%.
           
    Legume crops represent an important component of sustainable agricultural systems due to their ecological and agronomic benefits. The Leguminosae family is the third-largest group of flowering plants, comprising approximately 800 genera and more than 20.000 species (Lu et al., 2024). Many legumes are cultivated as grain crops, while others contribute to soil fertility through biological nitrogen fixation. Despite their benefits, legumes occupy only a relatively small portion of global arable land, which is largely dominated by cereal production. Soybeans account for the majority of global legume cultivation, with approximately 117.72 million hectares cultivated in 2014, largely supported by specialised production systems. In contrast, the cultivation of many other legumes remains limited due to weak supply chains, market constraints and limited technological adoption (Kanwal et al., 2022; Dadrasi et al., 2024).
           
    Uniform plant establishment is particularly important for legume crops because their productivity depends strongly on plant density, spatial distribution and early root development. Uneven seed placement can result in irregular crop stands, increased plant competition and reduced nodulation efficiency, ultimately affecting yield stability. Optimal spacing between plants allows better access to light, nutrients and soil moisture while supporting effective symbiotic nitrogen fixation. However, traditional sowing methods often result in inconsistent seed placement and variable planting depth, which can negatively affect germination and early crop growth.
           
    Recent developments in precision agriculture technologies offer opportunities to improve planting accuracy and resource efficiency. For example, precision cultivation techniques integrating remote sensing, soil moisture sensors, GPS-guided machinery and data analytics have been used to optimise resource utilisation and enhance crop productivity (Chaudhari, 2024). Similarly, AI-based precision irrigation systems have demonstrated improved water-use efficiency and increased pea yields through real-time data-driven irrigation scheduling (Kim and Alzubi, 2024). Nevertheless, empirical research specifically examining the performance of GPS-guided precision sowing in legume production systems remains limited.
           
    Despite the potential advantages of GPS-guided sowing, several uncertainties remain regarding its agronomic performance and practical adoption. In particular, limited evidence exists on its influence on seed placement accuracy, plant establishment, nodulation and yield performance under different agro-climatic conditions. Furthermore, economic feasibility and accessibility remain important considerations for smallholder farmers and integration of GPS-guided sowing with other precision agriculture technologies requires further investigation.
           
    Therefore, the present study evaluates the performance of GPS-guided precision sowing in legumes compared with traditional sowing methods. The study analyses seed placement accuracy, germination rate, plant establishment, crop density and yield performance under field conditions. Additionally, it examines resource-use efficiency in terms of seed utilisation and input optimisation while considering potential economic and adoption barriers. The study addresses the following research questions
    •   How does GPS-guided sowing improve seed placement accuracy compared with traditional sowing methods?
    •   What are its effects on germination, early plant growth and crop yield?
    •   How does GPS-guided sowing influence resource efficiency, particularly seed utilisation?
    •   What economic and adoption barriers may limit the implementation of this technology?

    MATERIALS AND METHODS

    Study area
     
    The research was conducted in Madhya Pradesh, India, a major agricultural hub known for legume cultivation, particularly soybean, chickpea and pigeon pea. The selected experimental site had loamy soil with moderate fertility and received an average annual rainfall of 1.000-1.200 mm, typical of the semi-arid tropical climate.
     
    Experimental design
     
    The experimental setup followed a comparative field trial approach rather than a randomized block design (RBD), as only two plots were used and treatments were not replicated across multiple blocks. Therefore, the study represents a field-scale comparative case study rather than a statistically replicated experiment. The experiment was conducted on a 10-hectare farm divided into two equal plots (5 ha each) representing two sowing approaches:
    •  Precision sowing plot (5 ha) - Seeds were sown using a GPS-enabled precision seeder (John Deere 1780 Planter equipped with SF3 guidance providing ±3 cm pass-to-pass accuracy, variable seed rate control and hydraulic downforce system), ensuring uniform seed placement depth and spacing.
    •  Conventional sowing plot (5 ha) - Seeds were sown using traditional manual or tractor-drawn sowing methods, which typically result in greater variability in seed placement.
           
    Because only one plot per treatment was available, treatment level replication was not possible. To characterize within-plot variability, ten randomly selected quadrats (1 m² each) were used within each plot to measure germination rate, plant population density and seed spacing. These quadrats represent subsamples rather than independent experimental replications. Table 1 summarizes the experi-mental design.

    Table 1: Experimental design.


     
    Crop selection and sowing parameters
     
    Table 2 summarizes the crop selection and sowing parameters used in this study.
    •  Legume crops studied: Chickpea (Cicer arietinum), Pigeon Pea (Cajanus cajan) and Soybean (Glycine max).
    •  Sowing time: Kharif season (June-July) for soybean and pigeon pea; Rabi season (October-November) for chickpea.
    •  Seed rate and spacing:
    o  Chickpea - 40 kg/ha, spacing 30 cm ×  10 cm
    o  Pigeon Pea - 15 kg/ha, spacing 60 cm × 20 cm
    o  Soybean - 80 kg/ha, spacing 45 cm × 5 cm

    Table 2: Crop selection and sowing parameters.


     
    Data collection parameters
     
    Table 3 presents the data collection parameters used in this study.

    Table 3: Data collection parameters.



    Seed placement uniformity - Seed placement was evaluated by physically measuring inter-plant spacing along crop rows after seedling emergence. Measurements were taken within ten randomly selected quadrats (1 m2 each) per plot. The distance between consecutive plants was measured using a measuring tape and spacing uniformity was calculated as the percentage of plants falling within ±20% of the recommended plant spacing.
     
    Germination rate (%) - Number of emerged seedlings per unit area after 15 days, counted in ten randomly selected quadrats (1 m² each) per plot.
     
    Plant population density - Number of plants per square meter measured 30 days after sowing within the sampling quadrats.
     
    Yield assessment (kg/ha) - Total grain yield measured at harvest and converted to kilograms per hectare based on the harvested plot area.
     
    Resource efficiency (Seed wastage) - Seed wastage was estimated by comparing the total quantity of seed sown with the number of successfully established plants. Seed wastage percentage (SW%) was calculated as:

     
    Data analysis
     
    Within each plot, ten randomly selected quadrats (1 m2) were used as subsampling units to characterize within-plot variability for germination rate, plant population density and seed spacing. These quadrats represent subsamples rather than independent experimental replications. Accordingly, the collected data were analysed using descriptive statistics and mean values and observed ranges were calculated for the measured parameters, including germination rate, plant density, yield, seed placement uniformity and seed wastage. The results therefore provide a comparative field-scale assessment of GPS-guided precision sowing and conventional sowing under practical farm conditions.

    RESULTS AND DISCUSSION

    Seed placement accuracy
     
    Table 4 shows that GPS-guided precision sowing substantially improved seed placement accuracy compared to conventional sowing methods. GPS-guided precision sowing produced noticeably more uniform seed placement compared with conventional sowing across the studied legume crops. Field measurements of plant spacing taken within randomly selected quadrats showed that seeds placed using the GPS-enabled precision seeder were distributed more consistently along crop rows.

    Table 4: Seed placement uniformity under precision and conventional sowing.


           
    Seed placement uniformity was calculated as the percentage of plants whose spacing fell within ±20% of the recommended intra-row spacing for each crop. Using this metric, the precision sowing plot achieved an average spacing uniformity of approximately 95%, whereas the conventional sowing plot showed a considerably lower uniformity of about 70%.
           
    The improved uniformity observed under precision sowing indicates that GPS-guided planting systems can significantly reduce irregular seed spacing, which commonly occurs in conventional sowing due to manual seed distribution or uneven planter operation. More consistent spacing contributes to uniform crop establishment and reduces intra-row competition among plants.
     
    Germination rate
     
    Germination performance was evaluated 15 days after sowing using quadrat-based plant counts within each plot (Table 5). Across the three legume crops studied, chickpea, soybean and pigeon pea, germination rates were consistently higher in the precision sowing plot compared with the conventional sowing plot.

    Table 5: Germination rate for precision and conventional methods.


           
    The observed germination rate under precision sowing ranged between 85-90%, whereas the conventional sowing plot showed germination rates ranging from 70-80%. These results suggest that improved seed placement and consistent sowing depth provided by GPS-guided planting may enhance seed-to-soil contact and create more favorable conditions for seed germination.
           
    The highest germination rate was observed in soybean under precision sowing, while pigeon pea exhibited the lowest germination under conventional sowing. Overall, the results indicate that improved spatial distribution of seeds contributes to better crop establishment across different legume species.
     
    Plant population density
     
    Table 6 presents the plant population density achieved under precision and conventional sowing methods. Plant population density was measured 30 days after sowing within the same quadrats used for germination assessment. The precision sowing plot consistently showed higher plant population densities across the three crops compared with the conventional sowing plot. Average plant population density in the precision sowing plot ranged from 18 to 22 plants per square meter, while the conventional sowing plot showed lower densities ranging from 15 to 18 plants per square meter.

    Table 6: Plant population density for precision and conventional procedures.


           
    The higher plant density observed in the precision sowing plot reflects more efficient seed placement and reduced seed loss during sowing. Uniform plant spacing also helps minimize competition for water, nutrients and sunlight during early growth stages.
     
    Yield
     
    Grain yield was measured at crop maturity and converted to kilograms per hectare based on the harvested area (Fig 1). Across the three legume crops evaluated, the precision sowing plot consistently produced higher yields than the conventional sowing plot. Average yields in the precision sowing plot ranged from 1500 to 1600 kg/ha, while the conventional sowing plot produced yields between 1300 and 1400 kg/ha.

    Fig 1: Bar chart comparing crop yield under precision and conventional sowing.


           
    Across crops, precision sowing resulted in an approximate 12-15% increase in yield compared with conventional sowing. The yield advantage is likely associated with improved germination, better plant spacing and more consistent crop stands established by the precision planting system.
     
    Resource efficiency
     
    In terms of resource efficiency, the precision sowing plot had only 6% seed wastage, whereas the conventional sowing plot experienced 17% seed wastage (Fig 2). The precision sowing method significantly reduced seed wastage by ensuring that seeds were placed accurately, reducing overlap and underplanting. This not only cuts down on seed costs but also contributes to more sustainable farming by minimizing input waste.

    Fig 2: Pie charts comparing seed wastage in precision and conventional sowing.


           
    The reduction in seed wastage highlights a key advantage of precision sowing: Enhanced input efficiency. In a country like India, where agricultural input costs are a significant burden on farmers, such savings are critical for improving the economic sustainability of farming operations.
           
    The results of this study indicate that GPS-guided precision sowing may improve several aspects of crop establishment and productivity under the conditions of the present field trial. Notable improvements were observed in seed placement accuracy, germination rate, plant population density, yield and resource efficiency. These findings align with precision agriculture research that highlights the potential benefits of spatial accuracy and optimized input application.
           
    Seed placement accuracy increased from 70% under conventional sowing to 95% with GPS-guided methods. This demonstrates the system’s ability to maintain consistent row spacing and depth. Better placement reduces gaps and seed clustering, which often cause uneven stands in traditional systems. Previous studies confirm that precision seeding improves spatial uniformity, leading to stronger early crop vigor and consistent emergence (Pareek et al., 2022). Senthilkumar et al., (2025) reported that GPS- and AI-enabled seeding robots enhanced depth by 28.57%, spacing by 33.33% and yield by 30.77%, while reducing water and fertilizer use by 25%. Our results support these findings, showing that precise placement directly affects crop performance and input efficiency.
           
    The germination rate was higher in precision plots (88%) compared to conventional plots (75%). This improvement is due to better seed-to-soil contact and consistent depth placement. Germination is sensitive to micro-environmental conditions, including moisture and soil structure. Irregular depth or spacing can delay or prevent emergence due to crusting, clods, or uneven moisture. Lamichhane et al., (2019) emphasized that accurate placement reduces these risks, creating uniform conditions for all seeds. Reed et al., (2022) also found that optimal depth and spacing improved legume emergence. Our results confirm that GPS-guided sowing reduces variability in emergence and supports a more uniform crop stand.
           
    Plant population density reached 20 plants/m² in precision plots, compared to 16 plants/m² in conventional plots. This increase complements the higher germination rate and indicates successful early establishment. Uniform spacing reduces intra-row competition and ensures adequate access to light, nutrients and moisture for each plant. Similar findings in maize and wheat show that optimized spacing enhances canopy development and increases biomass accumulation and yield (Djaman et al., 2021; Ghimirey et al., 2024). Manu et al., (2024) demonstrated that aerial yield mapping and targeted management of low-yield zones could improve overall production by up to 20%. These results support the importance of stand consistency in maximizing crop potential.
           
    Yield increased by 14.8%, from 1,350 kg/ha in conventional plots to 1,550 kg/ha in precision plots. This gain reflects improvements in early crop establishment, including better placement, higher germination and improved spacing. Precision sowing reduces plant stress and optimizes input use. Similar outcomes have been reported in cereals and legumes, where precision sowing reduced seedling variability and improved productivity (Feng et al., 2024). Seed wastage decreased from 17% in conventional plots to 6% in precision plots. This highlights a clear advantage in input efficiency. In India, where input costs are high and land is limited, reducing waste while maintaining yield is crucial. McDonald et al., (2024) also found that precision planting reduces input overuse without affecting crop productivity.
           
    Overall, the observed improvements across measured parameters suggest that GPS-guided precision sowing has the potential to enhance crop establishment and input efficiency under practical farm conditions. However, the present study represents a single-season, single-location comparative field assessment and the lack of replicated experimental plots limits the ability to generalize the findings across diverse cropping systems or agroecological regions. Soil type, climate variability and management practices may influence the magnitude of these effects. Future research should incorporate replicated multi-location and multi-season trials to validate these observations and to better quantify the agronomic and economic benefits of precision sowing technologies. Integration with remote sensing tools (Manu et al., 2024) and germination prediction models (Lamichhane et al., 2019) may further support adaptive management and improved decision-making in precision agriculture systems.

    CONCLUSION

    This study indicates that GPS-guided precision sowing can improve seed placement accuracy, germination rate, plant population density, yield and resource efficiency compared with conventional sowing under the field conditions evaluated. Precision sowing resulted in an observed yield increase of approximately 14-15% and reduced seed wastage, suggesting potential advantages in crop establishment and input-use efficiency. These improvements highlight the value of accurate seed placement for optimizing plant spacing and improving early crop development. However, the findings should be interpreted cautiously because the study was conducted at a single location during one growing season and without replicated experimental plots. Therefore, the results represent a comparative field-scale observation rather than broadly generalizable evidence across cropping systems. Future research should include multi-location and multi-season trials with replicated designs to validate these findings. Integrating GPS-guided sowing with remote sensing, real-time sensors and economic assessment may further clarify its potential role in sustainable and precision-based agricultural systems.

    ACKNOWLEDGEMENT

    Thanks to the reviewers for their hard work.
     
    Authors’ contributions
     
    All authors contributed toward data analysis, drafting and revising the paper and agreed to be responsible for all the aspects of this work.
     
    Funding details
     
    This research received no external funding.
     
    Data availability
     
    The data analysed/generated in the present study will be made available from corresponding authors upon reasonable request.
     
    Availability of data and materials
     
    Not applicable.
     
    Use of artificial intelligence
     
    Not applicable.
     
    Declarations
     
    Authors declare that all works are original and this manuscript has not been published in any other journal.

    CONFLICT OF INTEREST

    Authors declare that they have no conflict of interest.

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