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Transforming the Agriculture: Promises and Challenges of Generative AI

Crop sprayer and crop

According to Qin Zhang in his book Precision Agriculture Technology for Crops Farming, the main objectives of agriculture today are to operate in an environmentally, economically and socio-politically sustainable way. These are significant challenges for a sector that has been managed since ancient times using a trial-and-error approach. Historically, it has focused more on slave management than on soil management. . Now the sector is being transformed by Generative AI, which can be very useful through prediction based on data taken in the field which can increase both production and reduce costs and planted land. As the article “Understanding the potential applications of Artificial Intelligence in the Agriculture Sector” explains, precision agriculture uses technologies such as the use of sensors, image capture, GPS position to collect data on important variables both in the soil and in water quality, weather prediction, pest control, etc.. These data are analyzed and processed using mathematical, statistical and probabilistic treatments to have the greatest precision in the predictions made. After this, an automated mathematical model known as machine learning is used. These generative AI models enable the simulation of processes and the prediction of the most likely outcomes in crop production. These models must have feedback as often as possible and add as much data as needed to improve their accuracy. Obviously, it must be taken into account that the algorithms can have biases and need high data reliability. However, the widespread use of AI in agriculture raises concerns about inequality, job displacement in rural areas, high implementation costs, and ethical challenges. Finally, we can say that Generative AI is revolutionizing agriculture by providing more accurate crop yield predictions, but its effectiveness is hindered by challenges such as data quality, the need for robust validation models, a lack of territory-wide internet coverage and job reductions.

Promises

IoT and smart sensors for precision farming

Generative AI may be able to predict important parameters for better manage of farms. For each of the different crops, it is important to control variables so that each of these crops are efficient. Weather predictions, pest detection, soil quality, irrigation and water quality are some of the tasks that generative AI can perform for agriculture. Due to current environmental conditions, farmers find it difficult to predict rainfall, soil conditions, and groundwater levels. AI has the potential to revolutionize agricultural activities. Businesses can use big data, AI, and ML technologies to anticipate pricing, calculate tomato output and yield and identify pest and disease infestations. They can advise farmers on demand levels, crop varieties to plant for the best profit, the usage of pesticides, and future pricing patterns. Finally, through the use of machine learning models and quality data input, the best predictions can be made to help farmers in a world changing due to changing climate conditions, deforestation and the increase in the human population. In their report called “Precision Agriculture Benefits and Challenges for Technology Adoption and Use” the United States Government Accountability Office argues that in recent times, farmers have had many technologies at their disposal to apply precision agriculture, such as soil sensors and targeted spraying systems. These are some important technologies in the field that available 30 years ago, but the farmers did not use them. This demonstrates that the agriculture field does not optimize their resources and can be driven to take control over their process, but it is a long-term job because the process needs a great quantity of data to build a good model so that the predictions generated by AI are as accurate as possible.

Farmers can also improve their profits through generative AI. There are three main benefits of using site specific crop management. The main one is the reduction of costs and the increase of farm income; the second is the improvement of workplace safety conditions and the improvement of quality of life; and the third is the reduction of negative environmental impacts. For example, a USDA study showed that corn production increased from 130 bushels in 1996 to 183 bushels in 2016. The study found that from 2001 to 2017, the use of precision agriculture technologies increased. It estimated that yield maps and soil maps improved corn production efficiency in the U.S. by 7-8 percent. Over time, U.S. government agencies, such as the USDA, have provided evidence that precision agriculture fosters a new approach that benefits both farmers and the environment by reducing inputs and increasing production.

Challenges

The most important challenges of generative AI in agriculture are data quality, improvement of data processing models, the existence of the Internet and connectivity between system components and the reduction of jobs in the agricultural area.

Data Quality

For intelligent farms using generative AI, the entry of quality data is necessary and essential. Lack of reliable data and lack of understanding of what can be done with the data is a major challenge for precision agriculture because many farmers do not have access to data and prefer to continue with their old practices. Many farmers do not want to make an initial investment in training on the technologies even though they know there are many benefits later on. In addition, for prediction models to be more accurate, they need a large amount of data and feedback with a continuous supply of data to become increasingly better.

Machine Learning Models

Knowing the methods of data analysis is essential. Another major challenge is that even though quality data is collected in good quantity, there is little software that allows for the analysis of this data. In addition, farmers do not have enough confidence to use these algorithms due to a lack of training. In order to make a decision based on an algorithm's prediction, it is necessary to know how the algorithm is evaluated, what its accuracy is, what its metrics are, and this represents a limitation for the adoption of precision agriculture techniques.

Lack of Connectivity

Animated Image of How Satellite Internet Works

For the use of generative AI equipment, it is essential to have the internet in rural areas. Most connected sensors in the field, automated equipment and dosing equipment need a way to communicate and this is possible thanks to the internet. In many rural areas there are no internet networks and this becomes a major limitation for the use of this technology. At least 17 percent of rural Americans lack access to fixed broadband, compared to only 1 percent of Americans in urban areas. It is important to improve precision agriculture and have a good connection to the internet to connect all devices or equipment.

How to Face these Challenges

Although there are difficulties, as mentioned above, there is a growing wave of data science students who are making it possible to find solutions to the problem of missing data, how to assume data, what data can be ignored, etc. The same is happening in the field of machine learning and prediction models. More and more data of better quality is being taken, and the way of understanding it and the models for performing analysis and predictions are being improved. This computer revolution is here to stay and is in continuous improvement, making better the quality of data and its processing and leading farmers to enhance the yield of their crops and save on costs and inputs. In paper called “Artificial intelligence on the agro-industry in the United States of America” published in AIMS Agriculture and Food, explain that AI technology is poised to transform agriculture, offering benefits such as pest control, improved food quality, optimized logistics, and sustainable practices. These innovations aim to increase efficiency and productivity while addressing challenges like labor shortages and resource management. In states like California and Iowa, AI-driven tools such as soil monitoring and automated robots optimize water usage and boost yields. Additionally, U.S. government initiatives, such as USDA  investments in smart farming, accelerate AI adoption. Ensuring AI sustainability requires balancing technological advancement with ethical, inclusive, and regulatory frameworks. Education and the development of a skilled workforce are essential to support the responsible deployment of AI. Despite obstacles like data security and moral issues, AI holds the promise of significant improvement.

In conclusion, generative AI in agriculture will bring great benefits such as increased food production and reduced input costs, but there is still a long way to go; technological issues such as data management, the accuracy of prediction models and lack of internet coverage must be addressed, as well as ethical issues such as job reduction. Even so, the important influence that generative AI will have in the field of agriculture cannot be denied.