In the digital agriculture realm, generating accurate plot patterns of your field trial is crucial for optimal crop monitoring to understand plant dynamics through time. To achieve this, the accurate and precise mapping of your plots, also known as plot mapping or parcellaire generation, plays a pivotal role. In recent years, the integration of deep learning techniques has revolutionized the process, enabled automated plot map generation, and offered a myriad of benefits for agricultural research and production. In this blog post, we will explore the importance of plot mapping, the advantages of using deep learning for automation, and how it impacts the agricultural ecosystem.


Efficiency and Time Savings


Traditionally, creating plot maps was a labor-intensive task involving manual measurement and mapping techniques. However, with the advent of deep learning algorithms, this process has been significantly streamlined. Deep learning models can automatically segment and delineate agricultural land into distinct plots, saving valuable time and effort for farmers, agronomists, and researchers. By automating this process, researchers can allocate their resources more efficiently and focus on other critical aspects of field trial management.

Big plots trial - Automating plot mapping with digital tools is highly helpful.
Big plots trial - Automating plot mapping with digital tools is highly helpful.
Small plots trial - Plot map still could be done manually but automation make it even more easy.
Small plots trial - Plot map still could be done manually but automation make it even more easy.

Automation Brings Enhanced Precision


One of the remarkable advantages of using deep learning algorithms for plot map generation is the ability to achieve higher precision compared to manual mapping methods. Deep learning models can analyze aerial or satellite imagery, topographic data, and other relevant information to accurately identify plot boundaries.


Example of automated plot mapping of salad field from UAV imagery.


Accurate plot maps generated through deep learning algorithms have a direct impact on the processing of phenotypic data collected for each plot. Phenotypic data is the result of plant assessments like canopy development, stress and disease resilience, yield predictions and more, is essential for agricultural research and breeding programs. By having precise plot boundaries, researchers can associate specific phenotypic data with corresponding plots, enabling more accurate analysis and interpretation of plant assessment distribution within the entire field trial. This granular information facilitates the identification of patterns, trends, and correlations, ultimately leading to informed decision-making for crop research and production.


The Importance of Robust Training Datasets


To achieve reliable and versatile deep learning models for plot map generation, robust training datasets are paramount. High-quality datasets that encompass diverse geographical regions, crop types, and plot patterns variations and orientation are essential for training models to identify plot boundaries effectively within images captured in various environments. This emphasizes the need for collaboration and data sharing within the agricultural community to develop comprehensive datasets that can improve the accuracy and applicability of deep learning models for plot map generation.

Set of images of plots in various conditions used for training DL models.


Hiphen: Your Digital Phenotyping Partner


At Hiphen, we specialize in developing digital plant assessment solutions for agricultural research and production. We understand the significance of plot map generation and the transformative potential of deep learning in the agricultural sector. Our expertise lies in leveraging cutting-edge technologies to empower our clients with accurate and automated plot mapping alongside data acquisition and processing, enabling them to make better decisions and adapt to ever-changing environmental conditions. Through our tailored AI solutions, we strive to revolutionize the agriculture industry, promote sustainable practices, and enhance crop researchers productivity.

View of Hiphen's Cloverfield™ Data Platform.


Plot map generation holds immense importance for agricultural research and production. By utilizing deep learning algorithms, we can automate the process, saving time and improving precision. Accurate plot maps enable efficient resource allocation and precise data processing. However, it is vital to build robust training datasets to ensure the reliability and versatility of deep learning models. With Hiphen's AI methodologies, the agricultural industry can embrace innovation, make informed decisions, and adapt to the evolving needs.



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Topic brought to you by Rhianna MCANENY - Image Processing Specialist @Hiphen.