Introduction
By 2025, global spending on agricultural systems and technologies, including AI, is expected to increase revenue. Besides, the spending directed towards AI solutions and technologies in agriculture is expected to rise from $1 billion to $4 billion (Columbus, 2021). This will see a 25.5% Compound Annual Growth Rate as expressed by Markets&Markets. It is important to note that IoT sensors, AI and Machine Learning provide raw data to algorithms that improve agricultural efficiencies, reduce production costs and improve yield. Ai is making a significant impact on different domains around the agricultural industry. Soon, the world’s population is expected to get to nine billion, which means an increase of 70 percent in agricultural production (Eli-Chukwu, 2019). As this population rises and demand also plummets, it is sensible to use more innovative approaches to improve efficiency around agriculture.
While the agricultural industry turns to intelligent technologies for better pest control and soil monitoring, many use cases are yet to be fully implemented by most firms in the industry. First, AI greatly helps in weather forecasting. Due to the change in climatic conditions and increased pollution worldwide, it became so difficult for farmers to analyze weather conditions. However, AI brings new capabilities around forecasting as it helps farmers analyze current and future weather conditions hence knowing which crop to plant and when to sow seeds.
AI has also improved the agricultural industry since farmers do not have to use old methods to monitor crop and soil health. Due to the importance of nutrition in soil and soil, their health must be monitored most appropriately to ensure a good quality crop is harvested. It is important to note that increasing deforestation is degrading soil quality. As such, there is a great need to invent solutions that can determine soil quality. Recently, a German IT company developed an application known as Plantix through AI. This application can identify a nutrient deficiency in the soil, including diseases and plant pests. This AI-based application is a great idea for farmers as they will now be able to plan on the fertilizer to improve the harvest’s quality. This application uses image-recognition-based technology; hence farmers can capture the images of plants. This is a good improvement in the world of farmers due to the advancement of technology. Today, more applications are being developed to achieve the same purpose. As such, the life of farmers is expected to be better in terms of accuracy in their planting patterns and crop rotation.
Also, farmers can analyze the health of their crops using drones. For instance, several companies have come up with drone-based imaging solutions aimed at monitoring the health of crops. These drones help capture data from the plantation and transfer the data through USB to a medium where specialists analyze it (Jain, 2020). Mainly, algorithms are used in this process as the drones can capture images and give detailed reports about the health of the farm. Like the AI-based applications, drone technology can identify bacteria and pests, helping farmers deal with the challenges early enough.
The difference between a failed harvest and a profitable year is the timely information on sowing time. To deal with this, AI has enabled the use of predictive analytics tools for better precision. When a farmer knows when it is the right time to sow and how they can obtain maximum yield from their crops, that plays a significant advantage in the lives of farmers.
The biggest worry in using AI analytics for many farmers is the predictions and forecasts involved. While AI improves accuracy around agriculture, that does not assure farmers. Also, farmers face a big worry of price fluctuation. This is due to the unfair prices in the market. As such, farmers cannot develop a complete schedule of production patterns (Alreshidi, 2019). However, the problem is highly prevalent in tomatoes with a pretty limited shelf time. It is important to note that companies that use weather data and satellite imagery to monitor and assess the acreage of crop health in real-time come in handy in defining patterns and yields. Today, farmers can say 60 percent of their woes are solved through AI technologies. However, the main question remains how 40 percent can be accounted for (Eli-Chukwu, 2019).
With improved technology comes more cost of production. AI technology prices must be subsidized to enable farmers to acquire the correct prices in the market. The expert systems developed by AI come in handy in easing the lives of farmers. They have brought about capabilities in agricultural management as they can provide interpreted, integrated and site-specific data. However, the development of these expert systems in the agriculture industry is relatively recent, and these systems are still rare. Even though AI has brought about significant improvements in the agricultural industry, its use is still below average compared to its potential and impact in the current agricultural scene. Thus, there is still more to be done to improve agricultural activities using AI while eliminating the challenges present (Munoz, 2020).
An essential attribute of expert systems is accomplishing tasks in a concise time. Many of these systems are known to fail in accuracy or response time. Besides, a system delay affects the farmer’s selection of the strategy. In this case, strategy is mainly hypothesized based on two factors. First, the effort needed to synchronize systems and the accuracy level is needed. Farmers seeking to maximize accuracy while minimizing effort choose between the three capabilities offered by AI systems. These capabilities include monitoring, pacing, and automatic performances.
While the strength of an expert system is measured based on the volume of data, a sound system is aligned to the immense volume of data. However, the system must also remain responsive to unexpected events. Besides, deep knowledge of the system under use needs to be instilled in the farmers. However, they will always rely on field experts; hence their costs of running operations will increase.
The secret about any expert system is found in its execution. Since AI technologies are integrated with big data, there should be proper training for farmers who are always looking for accuracy and speed. Many farmers mainly rely on training to understand many issues around AI. Besides, it is not easy for a farmer who does not know a system to start working. Some of the systems that have been implemented are complicated and cannot be operated by farmers with basic knowledge. As such, there is a great need to address the implementation method underuse.
Today, data is considered valuable and something worth protecting. As such, there is a cost associated with protecting AI data. Many systems used in farms are internet-based, which reduces their usage capabilities when it comes to remote areas with limited internet connectivity. While the government supports farmers using web-based services with a lower tariff, there is little yet to be done; hence the full capabilities of AI systems have not been realized. Many governments have still not understood their assignment in the agricultural sector and its relation to AI. There is a need to involve these governments in understanding the solutions in place. When the government understands the solution, it will be easy to support farmers who are more than willing to adopt AI systems in their day-to-day operations.
While flexibility in farming operations is counted as an essential aspect of AI, it has not been perceived hence less progress. Most AI-based technologies seem to be integrating robotics technologies in an already integrated environment. However, most environments in farms have not been prepared for such systems. Thus, flexibility becomes a huge challenge for most farmers. While several subsystems can accompany an AI expert system, the subsystems need flexibility. They also need to have expansive capabilities that can accommodate more data collection. With more data, it is easy to draw conclusions. However, fewer data might cause there to be biased conclusions.
Farming encompasses a considerable number of uncertainties and choices. Every season, there is weather variation while the prices of crops fluctuate time after time. Other challenges facing farmers daily include weeds and the lack of viability in crops. Thus, farmers must cope with the newly introduced AI systems. Although the agricultural practice is broad, AI technologies must focus on the significant problems farmers are dealing with to show commitment to the course (Columbus, 2021). Therefore, it is paramount to review the different solutions presented to farmers and come up with a way to make them compatible with most farming practices.
Startups and companies responsible for AI technologies need to understand the factors that hold more importance than others in the lives of farmers. For instance, soil technology is critical, and many technologies should focus on improving the nutrients and health of the soil. Besides, crop production is also essential as it plays an essential role in various economies. Thus, as agriculture struggles to provide food for the growing population, AI technologies should aim to increase production in a friendly manner with many farmers.
Conclusion
A rising population worldwide will need more agricultural production to fulfill the growing demand. As discussed before, agricultural production is expected to increase by 70 percent to accommodate the vast population. However, only 10 percent has increased even with the intense use of AI. As such, the agricultural industry is staring at a disaster that needs a solution as soon as possible. Thus, farmers’ training needs to be intensified while the current technologies improve (Eli-Chukwu, 2019). In this case, AI technologies will need to be defined for more efficiency in the farming industry. These technologies will aid in the improvement of the current state of farming with the latest tools in the market. Autonomous systems and robotics are set to transform the global agricultural industry. They are set to impact the economic sector significantly through productivity improvement. Thus, AI technologies are the way to go, but more work will need to be done to sensitize farmers on best practices and train them on the basics of AI technologies.
References
Alreshidi, E. (2019). Smart sustainable agriculture (SSA) solution underpinned by internet of things (IoT) and artificial intelligence (AI). arXiv preprint arXiv:1906.03106.
Columbus L. (2021). 10 ways AI has the potential to improve agriculture in 2021. Retrieved from https://www.forbes.com/sites/louiscolumbus/2021/02/17/10-ways-ai-has-the-potential-to-improve-agriculture-in-2021/?sh=5564045e7f3b.
Eli-Chukwu, N. C. (2019). Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 9(4), 4377-4383.
Jain, P. (2020). Artificial intelligence in agriculture: Using modern day AI to solve traditional farming problems. Retrieved from https://www.analyticsvidhya.com/blog/2020/11/artificial-intelligence-in-agriculture-using-modern-day-ai-to-solve-traditional-farming-problems/.
Munoz, M. (2020). AI in agriculture: Is the grass greener? Retrieved from https://cmr.berkeley.edu/2020/03/ai-agriculture/.
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