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

Domhoefer, M and Chakraborty, D and Hufnagel, E and Claußen, J and Wörlein, N and Voorhaar, M and Anbazhagan, K and Choudhary, S and Pasupuleti, J and Baddam, R and Kholova, J and Gerth, S (2022) X‑ray driven peanut trait estimation: computer vision aided agri‑system transformation. Plant Methods (TSI), 18. 01-12. ISSN 1746-4811

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Background: In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results: We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN ( R2 = 0.91, MAE = 0.09) compared to XRT ( R2 = 0.78; MAE = 0.08). Conclusion: Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.

Item Type: Article
Divisions: Global Research Program - Accelerated Crop Improvement
CRP: CGIAR Research Program on Grain Legumes and Dryland Cereals (GLDC)
Uncontrolled Keywords: Peanut production, Technology-driven system transformation, X-ray, Convolutional neural network (CNN), Kernel weight, Shelling percentage
Depositing User: Mr Nagaraju T
Date Deposited: 30 Aug 2023 06:16
Last Modified: 30 Aug 2023 06:16
URI: http://oar.icrisat.org/id/eprint/12168
Official URL: https://plantmethods.biomedcentral.com/articles/10...
Projects: Accelerated Varietal Improvement and Seed Delivery of Legumes and Cereals in Africa, CGIAR’s Crop to End Hunger initiative, CGIAR Research Program grant for Grain Legumes and Dryland Cereals
Funders: Czech University of Life Sciences Prague, CGIAR’s Crop to End Hunger initiative, USAID and including the Gates Foundation
Acknowledgement: The authors acknowledge Mr. T Nataraj from CCD team for providing the peanut harvest for the study, facilitating the farmers’ interview and market analysis. We are also thankful to Dr. Janila Pasupuleti for access to peanut material and information about breeders’ requirements and consumer preferences. We thank the staff of Fraunhofer, ICRISAT, and Center for Collective Development for their assistance in this study. \We are grateful to Dr. William Nelson for thorough language corrections of the text.
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