Most of the 515,000 soybean growers in the USA, the largest soybean producer, practice precision agriculture due to rising fertilizer prices and environmental concerns about pesticide use. It is also necessary to stop deforestation to meet increasing demands for livestock feed. Since 80% of the global soybean production of 385.5 million tonnes in 2022 was allotted for feed, increasing the productivity of existing soybean farms using drone analytics can help solve economic issues, protect biodiversity, and limit climate change.
Benefits of using drones and AI analytics in soybean production
Using precision agriculture methods has helped soybean growers in all aspects of farming. Growers are using drones and AI analytics to cut manual scouting time. For example, a farmer reduced scouting time from 30 hours to 15 minutes weekly for a 160-acre farm.
According to the American Soybean Association, growers have increased productivity by 4% through precision agriculture by ensuring accurate spacing, improving fertilizer placement by 7%, and a well-planned harvest. Simultaneously, growers are addressing environmental concerns by cutting the use of herbicide by 9%, water by 4%, and fossil fuel by 6% (by limiting field passes). The savings in input costs serve to increase overall ROI for growers.
How drones and AI analytics work in soybean production
Agremo, an industry leader in drone data analysis software, uses AI, machine learning, and scientific indices to offer soybean analytics solutions for the entire crop season. Growers can implement all or one of its solutions that, include stand count, weed detection, plant vigor, waterlogging detection, canopy cover, and plant stress. Agremo has identified the optimal timings for its range of soybean analytics, as shown in the Guideline.
Agremo analytics analyze drone images to give growers and professionals objective, accurate insights into the small-scale heterogeneity in crop performance and health caused by soil type, nutrient deficiency, and stress to make data-driven decisions for farm management.
Steps to implement drones and AI analytics in soybean production
Soybean growers choose the Agremo analytic solution and take drone images at the recommended time. Growers can contact Agremo to take drone images of their farms and stitch the images to get field maps.
Agremo will analyze the drone imagery to generate AI reports and send them to the growers. The reports contain information and an analyzed map marked into zones based on crop status, health, or field conditions. Guided by data-driven insights, growers can make informed decisions and provide inputs only where necessary in the correct amounts in the demarcated management zones for fertilization, irrigation, or pest control. Basing farm operations on actual field conditions instead of general regional recommendations helps to optimize yields and reduce overuse and wastage of resources.
Examples of how different Agremo solutions can help soybean growers are given below
Agremo for soybean production
The various Agremo solutions discussed start from replanting to benchmarking for future seasons.
Missing plant detection – replanting
- Agremo AI analysis: Stand Count
- Period: After emergence
- Data source: Drone
- Required resolution: 2cm/pix
The target stand count can vary from 140,000 to 70,000 plants/acre based on row width. Generally, early-planted crops should have 100,000 plants/acre. Unlike in corn, some missing soybean plants do not critically affect yield since branching by standing crops fills gaps and can compensate for losses. Therefore, soybean rarely requires replanting. However, if the stand count is less than 75,000/acre for crops sown before mid-May and is less than 50,000-60,000/acre for crops sown at the end of May to June, the field does need replanting. Moreover, thin crops can attract weeds and increase control costs, and replanting may be advisable.
The Agremo Stand Count will give the exact plant number, and farmers can decide to replant based on plant numbers, cost of replanting, yield potential of planted and replanted crops, date of planting, and weed incidence.
Early yield estimation – benchmarking for forward contracting decisions and supply planning
- Agremo AI analysis: Stand Count
- Period: After the emergence
- Data source: Drone
- Required resolution: 2cm/pix
Even though replanting may not be necessary for soybean, a Stand Count can help a farmer identify the loss in crop and anticipate yields for contracting purposes. Agremo Stand count reports provide the precise number of seedlings that have emerged. They are an improvement over rough estimates from traditional methods, including the Hula Hoop technique, where results from small observations are extrapolated to the entire field.
Growers anticipate getting 90% of the potential yield from a population of 100,000 plants per acre in an early sown crop. A population of 140,000 plants/acre can give a 100% potential yield. The row width and sowing dates are also crucial in estimating yield; later sown crops have a lower yield potential. With even 50% gaps, a 78-84% yield can be expected.
Accurate reports from Agremo help in business and economic planning.
Herbicide distribution optimization using VRA or Spot spraying map
- Period: Germination and leaf development
- Data source: Drone
- Required resolution: 2cm/pix
Effective weed control in soybean is necessary to avoid yield and quality losses, the high cost of weed management, and herbicide-resistance buildup.
If uncontrolled, early and late-season weeds can lead to a substantial yield loss of up to 49.5% in soybean. Most yield reduction occurs in the first six weeks because weeds compete for nutrients, water, and light. Vining late-season weeds interfere with harvesting operations and reduce yield. Also, 24 weed species, in the USA, have developed resistance to herbicides due to overuse of the same herbicides.
Best Management Practices and application of herbicide mixes give the best soybean yield.
Agremo Weed Detection can identify three stress intensity levels in plants and demarcate the exact infestation spots. Growers can make data-driven decisions on zones to treat and apply only the necessary amounts of herbicides. Growers will be able to improve crop growth while also cutting herbicide application costs.
Seed/hybrid and field zones evaluation for next season’s planting decisions (hybrid selection)
- Agremo AI analysis: Canopy Cover
- Period: Crop evaluation and treatment
- Data source: Drone/Satellite
- Required resolution: 2cm/pix
Agremo Canopy Cover Analysis compares the area covered with vegetation to the total field area, providing a farmer with in-depth information about their fields, seeds, and management practices. Farmers can identify areas of s to check for underlying problems causing uneven emergences like variable spacing, poor seed germination, or seedling vigor.
Soybean growers can use canopy coverage, a Leaf Area Index (LAI) measure, to choose better-performing seed brands or hybrids. Identifying problems like stress or irrigation needs in the same season would also be possible. More significant issues with the field can be tackled after the harvest to prevent problems in the future.
In-field yield estimates and checks for forward contracting decisions, supply planning, and equipment & storage capacity scaling
- Agremo AI analysis: Stand Count
- Period: Before harvest
- Data source: Drone/Satellite
- Required resolution: 2cm/pix
The crop stage of stand reduction is crucial for calculating yield estimates. To account for damages and losses during growth, Agremo Stand Count before harvest can help growers make an accurate yield estimation for contracting, planning harvest operations, and deciding on necessary equipment and storage facilities, besides making contracting decisions and supply planning.
Crop plant loss during the later vegetative stages results in heavier losses to yield, compared to loss after emergence or V3 (third node stage), as crops have less time for branching to compensate for plant losses in the field. Yield compensation also reduces as plant populations drop below 75,000 plants/acre.
Making Soybean Production Sustainable
The adoption of precision agriculture methods for soybean cultivation is well established in regions like the US for optimizing input application and reducing crop stress and the cost of farm operations. Drone analytics and other precision agriculture methods are seen as a means to increase productivity sustainably, aided by data-driven decisions. Many farmers hope the new methods will help them build operations their children will be interested in inheriting.
