Research Questions to Instrument to Coding Assignment
RQ to Instrument to Coding Assignment
Topic: AI Agriculture
Step One
1. Age Group
<20 20-25 26-30 >30
2. AI systems are being used in
Finance Retail/E-Commerce Telecommunication All of the Above
3. Do you think AI systems need to be used in Agriculture?
Yes No
4. Which of these countries is the forerunner of AI Agriculture?
USA Brazil UK India Japan
5. What type of AI tools/systems are used in Agriculture?
Sensors Machine Learning Computer Vision All of the Above
6. How can AI systems improve the field of Agriculture?
Protecting Crops Increased Production Soil Health Reduced Costs
Step Two
Kamal Adusumilli
1. Age Group
<20 X 20-25 26-30 >30
2. AI systems are being used in
Finance Retail/E-Commerce Telecommunication X All of the Above
3. Do you think AI systems need to be used in Agriculture?
X Yes No
4. Which of these countries is the forerunner of AI Agriculture?
X USA Brazil UK India Japan
5. What type of AI tools/systems are used in Agriculture?
Sensors Machine Learning Computer Vision X All of the Above
6. How can AI systems improve the field of Agriculture?
Protecting Crops Increased Production Soil Health X Reduced Costs
Stephen Courtney
1. Age Group
<20 X 20-25 26-30 >30
2. AI systems are being used in
Finance X Retail/E-Commerce Telecommunication All of the Above
3. Do you think AI systems need to be used in Agriculture?
Yes X No
4. Which of these countries is the forerunner of AI Agriculture?
USA Brazil X UK India Japan
5. What type of AI tools/systems are used in Agriculture?
Sensors X Machine Learning Computer Vision All of the Above
6. How can AI systems improve the field of Agriculture?
X Protecting Crops Increased Production Soil Health Reduced Costs
Nikitha Varma
1. Age Group
X <20 20-25 26-30 >30
2. AI systems are being used in
X Finance Retail/E-Commerce Telecommunication All of the Above
3. Do you think AI systems need to be used in Agriculture?
X Yes No
4. Which of these countries is the forerunner of AI Agriculture?
X USA Brazil UK India Japan
5. What type of AI tools/systems are used in Agriculture?
Sensors Machine Learning Computer Vision X All of the Above
6. How can AI systems improve the field of Agriculture?
Protecting Crops X Increased Production Soil Health Reduced Costs
Madhavi Sethu
1. Age Group
<20 20-25 X 26-30 >30
2. AI systems are being used in
Finance Retail/E-Commerce Telecommunication X All of the Above
3. Do you think AI systems need to be used in Agriculture?
X Yes No
4. Which of these countries is the forerunner of AI Agriculture?
USA X Brazil UK India Japan
5. What type of AI tools/systems are used in Agriculture?
Sensors Machine Learning Computer Vision X All of the Above
6. How can AI systems improve the field of Agriculture?
Protecting Crops X Increased Production Soil Health Reduced Costs
Abhinav Reddy
1. Age Group
<20 X 20-25 26-30 >30
2. AI systems are being used in
Finance X Retail/E-Commerce Telecommunication All of the Above
3. Do you think AI systems need to be used in Agriculture?
Yes X No
4. Which of these countries is the forerunner of AI Agriculture?
USA Brazil X UK India Japan
5. What type of AI tools/systems are used in Agriculture?
Sensors X Machine Learning Computer Vision All of the Above
6. How can AI systems improve the field of Agriculture?
X Protecting Crops Increased Production Soil Health Reduced Costs
Step Three
Nikitha Varma
1. What is your opinion of using AI systems in agriculture?
Agriculture plays an important part in improving the quality of life and revenue of a society. Therefore, I feel that AI systems are a necessity in agricultural production. With the help of AI systems, agriculture will be improved and updated using technology being used in the current era. The use of AI systems enhances agricultural productivity and improves the lives of farmers, and it has been proven in many countries. I feel that using AI technology will produce great results in agriculture.
2. Which AI system/tool provides the best results for farmers?
I feel that AI-based sensors provide the best support for farmers. Monitoring crops and protecting them from pests and rats is a tough task, and farmers spend most of their time during the harvest season doing the same. Therefore, this will help farmers spend less time and effort in monitoring pests. They can utilize their time to create more robust structures for improving their agricultural production.
3. What are the challenges in enabling AI Agriculture?
One of the biggest challenges in enabling AI agriculture is the fact that it is highly dependent on technology. Therefore, farmers need to gain more technical knowledge and capabilities to ensure the best results. This is a challenge for farmers who are in emerging countries. This is one of the most significant challenges that hinder AI agriculture in developing countries.
Madhavi Sethu
1. What is your opinion of using AI systems in agriculture?
I feel that AI systems improve agricultural production and farmer life. They have great potential in enhancing agricultural frameworks and improving the quality of life of farmers in a country. However, it is important to monitor the AI systems that are being used in agricultural systems. The main reason behind the same is that they can be easily misused.
2. Which AI system/tool provides the best results for farmers?
Machine learning has played a big part in AI-based agriculture and it provides farmers the ability to predict agricultural production and make necessary changes to enhance agricultural efficiency. With machine learning, farmers will be able to gain more yield from their crops and increase their harvest production significantly. Therefore, it is an important tool.
3. What are the challenges in enabling AI Agriculture?
There is a high level of cost involved in enabling AI agriculture. The use of sensors and tools that enable machine learning can become a costly investment for farmers in any country. There is also the fact that farmers might not reap the benefits of the AI systems instantly and have to have patience and financial acumen to enhance production.
Step Four
The research methodology section focuses on the collection of actionable data from all the information. For gathering actionable data, the section uses both quantitative and qualitative methods. With the help of quantitative checklist survey, the researcher will be able to gain an understanding of the participants’ perception and understand how they view AI agriculture. The main goal of this section is narrowing down the participants that can be used for the qualitative interview. The qualitative interview is focused on gaining data about AI use in agriculture and the benefits/challenges associated with the same. The questions were formed with the goal of exploring the concept of AI agriculture and gaining unique data from the same.
AI agriculture has gained recognition from various countries and have proven to be useful for farmers across the globe. The use of AI systems in agriculture has increased in the recent years and many farmers have started to understand the concept better. However, there is still some reservation among the farmer community, especially from farmers who are from developing countries. The goal of the dissertation is showcasing the importance of AI agriculture and improving the knowledge of the concept among farmers. The dissertation will explore the concept and focus on identifying the benefits and challenges that surround AI agriculture. This will help farmers and researchers get a better idea about the same. Through the research methodology, we can clearly see that participants understand the overall concept and know the inherent benefits and challenges associated with the same. The dissertation needs to focus on providing in-depth understanding of the AI agriculture by focusing on its benefits for developing countries. This will keep the study focused and help explore the challenges associated with the same. By focusing on the niche concept, the dissertation would also be able to help researchers expand their scope on the concept through further research.