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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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X-ray driven peanut trait estimation: computer vision aided agri-system transformation.

Martha Domhoefer^1,2, Debarati Chakraborty¹, Eva Hufnagel³

¹Crops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, India.

|June 6, 2022

Summary

This summary is machine-generated.

X-ray technology accurately assesses peanut pod and kernel weight, crucial for pricing. This rapid, objective method can improve food safety and farmer payments by reducing delays and fungal contamination risks.

Keywords:

Convolutional neural network (CNN)Kernel weight Peanut production Shelling percentage Technology-driven system transformation X-ray

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Area of Science:

Agricultural Science
Food Technology
Imaging Technology

Background:

Manual peanut quality assessment in India is slow and prone to errors.
Procurement delays and poor storage lead to fungal contamination and food safety issues.
Current methods lack objectivity and efficiency in evaluating raw peanut pods.

Purpose of the Study:

To investigate the efficacy of X-ray technology for rapid peanut quality assessment.
To determine if X-ray imaging can accurately estimate kernel and shell weight for price determination.
To explore a technological solution for improving peanut procurement processes.

Main Methods:

Generated 1752 2D X-ray projections of peanut pods using computed tomography (CT).
Employed X-ray image transformation (XRT) and a convolutional neural network (CNN) for feature prediction.
Validated predictions against gravimetric measurements of kernel and shell weight.

Main Results:

Both XRT and CNN accurately predicted kernel weight (R² > 0.93).
CNN showed higher accuracy in predicting shell weight (R² = 0.91) compared to XRT (R² = 0.78).
X-ray based methods demonstrated high correlation with actual weight measurements across diverse peanut varieties.

Conclusions:

X-ray technology offers a viable solution for objective and rapid peanut quality assessment.
This technology can streamline procurement, reduce post-harvest losses, and ensure fair farmer compensation.
The system has potential applications in crop improvement programs for high-throughput cultivar selection.