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

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Relation http://oar.icrisat.org/12168/
https://plantmethods.biomedcentral.com/articles/10.1186/s13007-022-00909-8
https://doi.org/10.1186/s13007-022-00909-8
 
Title X‑ray driven peanut trait estimation: computer vision aided agri‑system transformation
 
Creator Domhoefer, M
Chakraborty, D
Hufnagel, E
Claußen, J
Wörlein, N
Voorhaar, M
Anbazhagan, K
Choudhary, S
Pasupuleti, J
Baddam, R
Kholova, J
Gerth, S
 
Description 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.
 
Publisher BioMed Central Ltd
 
Date 2022-06-06
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Rights cc_attribution
 
Identifier http://oar.icrisat.org/12168/1/Plant%20Methods_18_01-14_2022.pdf
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