Detailed Analysis of Beets at the Individual Scale

Detection for Precise Phenotyping

Phenotyping, which involves measuring the observable characteristics of an individual, is currently performed in two main ways:

• Quickly, by drone or satellite, at the micro-plot scale
• Manually, plant by plant

The current challenge lies in the easy and rapid quantification of increasingly complex traits to compare varieties. At the same time, the field of image processing via artificial intelligence has made great strides. The automatic detection of each beet plant from drone images paves the way for both precise and rapid measurements, thus revolutionizing phenotyping.

Artificial Intelligence for Beet Detection

Artificial intelligence (AI) is transforming many sectors, and agriculture is no exception. Beets are an important crop for the agri-food industry. However, their cultivation can be challenging and requires precise monitoring. Among the major challenges are distinguishing beets from weeds, monitoring beet growth, and detecting diseases and pests.

• Development of new technologies: New technologies, such as deep learning, will further improve the accuracy and efficiency of beet detection.
• Integration of AI into agricultural tools: AI will be increasingly integrated into agricultural tools, such as tractors and robots, making it easier for farmers to use.
• Better understanding of crops: AI will enable farmers to better understand their crops and manage them more optimally.

It also offers promising solutions to address these challenges by discussing beet detection through drone imaging. Drones equipped with cameras can capture images of beet fields. AI can then be used to analyze these images and accurately identify beets, monitor beet growth by analyzing images or other data, such as biomass, and detect diseases and pests at an early stage, allowing measures to be taken to limit damage.

Expanding Varietal Choice Horizons

Sophisticated algorithms then come into play to analyze the images and extract valuable information. The leaf area of each beet is measured precisely, allowing the calculation of development heterogeneity values. Research is underway to explore the monitoring of even more complex plant traits, such as leaf nitrogen content and disease tolerance.

By multiplying measurements, phenotyping becomes a valuable tool for agronomic research, contributing to the understanding of physiological mechanisms and interactions between genetics and the environment.