Due to the rising demand, soy farmers are increasingly pressured to produce more to cover the global demand. This means that farmers are likely to adopt large-scale farms hence the need for technology. Technology is currently the driver for most activities, including agriculture, and soy is one of the industries likely to benefit from technology.

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The application of technology is diverse and can be used for soy agricultural functions such as soil preparation, irrigation, data collection, pest control, etc. These technology benefits can drive soy production due to flexibility, reliability, convenience, and cost-saving advantages. Technology can also help address various challenges likely to face soy farmers, such as higher expenses, proper management, etc. Here are some innovative solutions likely to propel soy farming and production.

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1. Innovative Processing

Technology is currently changing soy processing and manufacturing. The integration of technology in soy production has led to the creation of different products which are more nutritious and beneficial to consumers. For instance, technological processes can be integrated to create some of the most innovative soy products to boost certain nutritional values like soy protein.

Some of the most innovative soy products are ideal for people who need a high concentration of nutrients, such as athletes. For instance, highly concentrated soy protein powders suit athletes needing more muscles and healing injuries and wounds. Other products, such as oils and soy sauce, can be ideal for helping women to deal with the risk of cancers and osteoporosis, especially for older women.

Due to the innovation, there are more products anticipated for the future. Some of these products address various needs, such as global hunger concerns. For instance, innovative soy chunks and meat are suitable for addressing global hunger and limiting animal meat supply.

2. Soy Farm Smart Systems

Due to the increased application of artificial intelligence in agriculture, farmers can rely on different smart farming systems controlled by technology. One of the smart systems is the smart vehicles, including tractors, spray systems, and bots used in farms. These systems are also likely to collect data while they execute their primary functions. For instance, smart tractors can be used for field preparation while collecting critical soil data.

Other smart systems include irrigation and fertilization systems which can be used based on the data collected from the soil samples. Integrating smart systems is necessary for cost and time savings, helping farmers increase profitability by reducing expenses for needs such as human labor.

3. Drone and Satellite Imaging

Drones are already integrated as part of agricultural and crop farming practices. Currently, drones are used to monitor farms and collect data about the nature of the crops. Drones can also collect soil data, including moisture, aeration, and fertilizer concentration.

Some farmers also use modified drones for pest control, such as spraying the farms. Once the drones collect data and notice pests in certain regions, the data is transmitted to data analytics systems, redirecting the drone to spray the area. The drones provide real-time data and a view of the farm enabling the team to take preventive measures to control pests within the farm.

Besides the reliance on drones, farmers are also adopting other measures such as stateliest. The satellites are ideal for monitoring big farms. Due to the increased number of satellites, such as private satellites, farmers can get clear space images of the fields. Field images can be analyzed, and reports generated based on observed images.

Satellites and drones can capture thermal images, which can help determine healthy vs unhealthy crops. The results can be used to identify areas with limited moisture or other areas with pests. This will be used for informed decisions such as irrigation and pest control.

4. Clean Energy Logistics

The global soy supply chain is one of the most complex and complicated, consisting of various players. However, one of the most crucial supply chain players is the logistics companies which play a significant role in the movement of soy products globally from farms to consumers.

Due to the impact of the logistics system, the soy supply chain has been classified as unsustainable due to carbon emissions. To promote sustainability, the soy supply chain can rely on clean energy logistics systems such as electric trucks and trains to move soy. This is one of the measures to promote sustainable supply chain practices.

5. Big Data and Analytics

Big data is changing the world, and agriculture is a targeted area. The introduction of big data is critical for soy production since it is an essential part of artificial intelligence. Big data analytics is critical for analyzing the data from various machines and systems used on the farm.

Once the data is processed, the outcomes and results can be used to control other smart farming systems. For instance, when the analyzed data indicate limited humidity or nutrients in certain parts of the farm, the results can trigger irrigation pumps and systems to support soy growth and development. For automated fertilizing systems, the results of low nutrients based on the crop condition will trigger instant soil fertilization.

The data analytics tools will rely on cloud systems and platforms to store data which can be used for future farming practices. Therefore, farmers can introduce new farming systems to help in data processing and analytics to help manage farms better and effectively. When introducing a data analytics system, there is a need for a streamlined artificial intelligence consisting of other smart farming systems needed by soy farmers.

Conclusion

Technology is shaping soy farming, supply chain, and soy-related businesses. It is critical for producing innovative soy products, transforming agricultural processes and boosting production volumes. Technology boosts these benefits through the applications of smart systems, artificial intelligence, big data analytics, etc.