Domhoefer Martha, Chakraborty Debarati, Hufnagel Eva, Claußen Joelle, Wörlein Norbert, Voorhaar Marijn, Anbazhagan Krithika, Choudhary Sunita, Pasupuleti Janila, Baddam Rekha, Kholova Jana, Gerth Stefan
Crops Physiology & Modeling, Accelerated Crop Improvement Research Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, Telangana, India.
Universität Osnabrück, 49069, Osnabrück, Germany.
Plant Methods. 2022 Jun 6;18(1):76. doi: 10.1186/s13007-022-00909-8.
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.
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. Both methods predicted the kernel mass with R > 0.93 (XRT: R = 0.93 and mean error estimate (MAE) = 0.17, CNN: R = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R = 0.91, MAE = 0.09) compared to XRT (R = 0.78; MAE = 0.08).
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.
在印度，聚合商从小农户农场收购的生花生是带壳的，价格基于对花生荚和果仁基本特征的人工估算。这些生鲜农产品评估方法速度慢，且可能导致采购不规范。采购延迟加上缺乏储存设施，导致真菌污染，对许多地区的食品安全构成严重威胁。为填补这一空白，我们研究了X射线技术是否可用于快速评估对价格估算很重要的关键花生品质。
我们使用计算机断层扫描（CT）系统（CTportable160.90）生成了1752个单个花生荚的二维X射线投影。在这些投影中，我们预测了果仁重量和壳重量，这是农产品价格的重要指标。测试了两种特征预测方法：（i）X射线图像变换（XRT）和（ii）经过训练的卷积神经网络（CNN）。针对不同花生荚品种的果仁重量和壳重量的重量法测量，测试了这些方法的预测能力。两种方法预测果仁质量的相关系数R均>0.93（XRT：R = 0.93，平均误差估计值（MAE）= 0.17；CNN：R = 0.95，MAE = 0.14）。虽然与XRT（R = 0.78；MAE = 0.08）相比，CNN对壳重量的预测更准确（R = 0.91，MAE = 0.09）。
我们的研究表明，基于X射线的系统是估算关键花生农产品指标的一项相关技术选择（图1）。所得结果证明有必要进一步开展研究，使现有X射线系统适用于快速、准确和客观的花生采购流程。对农产品价值进行快速准确的估算，是避免因真菌污染造成收获后损失的必要前提，同时也能确保向农民公平支付款项。此外，同一技术还可通过完全跳过花生荚脱壳步骤，以高通量方式协助作物改良计划选择和培育具有更高经济价值的花生品种。本研究证明了该方法的技术可行性，是实现未来技术驱动型花生产系统转型的第一步。
