Background:
Agriculture is a vital industry that plays a critical role in feeding the world's population. However, predicting crop yields and accurately estimating production costs can be a major challenge for farmers and agribusinesses. Inaccurate predictions can lead to wasted resources and reduced profits, while slow results can make it difficult for companies to make timely decisions about their operations.
Case Description:
Current precision agricultural practices are struggling with predicting crop yields and accurately estimating production costs.
Current methods for gathering data involve sending field workers to travel hundreds of miles and walk row by row, field by field, to gather representative sample crop data. This process is highly inefficient, time-consuming, and subject to human error. Additionally, the data gathered is limited to the fields that the workers are able to visit, which means important information about other areas of the farms will be missed.
The end result is that Precision Agriculture crop yield predictions and production costs are not sufficiently accurate and take too long to generate results. This has led to wasted resources and reduced profits for the company, and it has made it difficult for them to make timely and informed decisions about their operations.
Mapping fields and collecting data on a variety of factors, such as soil type, moisture levels, and crop health, leads to more informed decisions with respect to irrigation, fertilization, and pest control.
Challenges:
• Improving the accuracy of crop yield predictions and production costs
• Simplifying and speeding up the data gathering process
• Expanding field coverage without significantly increasing cost
• Gather, store, and process large amounts of field data
Solution:
To address these challenges, a series of small but highly directed steps were carried out:
Automating data collection and analysis: By using sensors, drones, and other IoT devices to gather data on crop health, soil conditions, and other factors that impact yield, much of the data gathering process was automated. To reduce time to deploy and afford the ability to back out and start over, some of the data IoTs were purchased from third-party providers. Overall, this would reduce the reliance on human data gathering, which can be time-consuming and prone to error, and allow the company to gather data more frequently, from a wider area, and do so in less time.
Implementing a geospatial data management system and using a cloud-based data lake: By mapping fields and collecting data on a variety of factors, such as soil type, moisture levels, and crop health, leads to more informed decisions with respect to irrigation, fertilization, and pest control. These data points generate vast amounts of data, and by using a cloud-based data lake, disparate voluminous data can easily be stored and prepared for down-stream analysis.
Leveraging the power of cloud-based machine learning (ML): By using cloud-based tools and services, agricultural data scientists are able to analyze data from multiple sources, including sensors, drones, and other technologies, and generate more accurate and timely predictions and cost estimates.
Conclusion:
Improving crop yield predictions and production costs is a major challenge in the agriculture industry. By automating data collection and analysis, leveraging the power of cloud ML, and implementing a geospatial data management system with a data lake, precision agriculture can significantly improve the accuracy and timeliness of predictions and cost estimates, leading to increased efficiency, profitability, and sustainability.
