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

Abstract

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 varieties 1 . Both methods predicted the kernel mass with R 2 > 0.93 (XRT: R 2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R 2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R 2 = 0.91, MAE = 0.09) compared to XRT (R 2 = 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.

Plant phenotyping relevance

X線画像と画像変換・CNNを用いて、落花生のカーネル重量・殻重量という器官形質を推定し、重量測定で性能検証しているため、植物形質取得法が中心です。

Code and data availability

The paper's ground-truth phenotyping dataset (gravimetric kernel/shell weights for 1752 X-ray-scanned peanut pods across 39 varieties) is paper-specific, but the authors only promise future deposition at www.icrisat.dataverse.org after peer review, and peanut varieties are available only upon request. No public URL is活

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