Morisio Maurizio, Noris Emanuela, Pagliarani Chiara, Pavone Stefano, Moine Amedeo, Doumet José, Ardito Luca
Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy.
Institute for Sustainable Plant Protection, National Research Council, (IPSP-CNR), Strada delle Cacce, 73, 10135 Torino, Italy.
Sensors (Basel). 2025 Jan 6;25(1):288. doi: 10.3390/s25010288.
The increasing demand for hazelnut kernels is favoring an upsurge in hazelnut cultivation worldwide, but ongoing climate change threatens this crop, affecting yield decreases and subject to uncontrolled pathogen and parasite attacks. Technical advances in precision agriculture are expected to support farmers to more efficiently control the physio-pathological status of crops. Here, we report a straightforward approach to monitoring hazelnut trees in an open field, using aerial multispectral pictures taken by drones. A dataset of 4112 images, each having 2Mpixel resolution per tree and covering RGB, Red Edge, and near-infrared frequencies, was obtained from 185 hazelnut trees located in two different orchards of the Piedmont region (northern Italy). To increase accuracy, and especially to reduce false negatives, the image of each tree was divided into nine quadrants. For each quadrant, nine different vegetation indices (VIs) were computed, and in parallel, each tree quadrant was tagged as "healthy/unhealthy" by visual inspection. Three supervised binary classification algorithms were used to build models capable of predicting the status of the tree quadrant, using the VIs as predictors. Out of the nine VIs considered, only five (GNDVI, GCI, NDREI, NRI, and GI) were good predictors, while NDVI SAVI, RECI, and TCARI were not. Using them, a model accuracy of about 65%, with 13% false negatives was reached in a way that was rather independent of the algorithms, demonstrating that some VIs allow inferring the physio-pathological condition of these trees. These achievements support the use of drone-captured images for performing a rapid, non-destructive physiological characterization of hazelnut trees. This approach offers a sustainable strategy for supporting farmers in their decision-making process during agricultural practices.
对榛子果仁需求的不断增加，推动了全球榛子种植的热潮，但持续的气候变化威胁着这种作物，导致产量下降，并遭受病原体和寄生虫的无节制侵袭。精准农业的技术进步有望帮助农民更有效地控制作物的生理病理状况。在此，我们报告一种在开阔田地中监测榛子树的直接方法，即使用无人机拍摄的航空多光谱图像。从意大利北部皮埃蒙特地区两个不同果园的185棵榛子树上获取了一个包含4112张图像的数据集，每棵树的图像分辨率为200万像素，涵盖RGB、红边和近红外频率。为提高准确性，特别是减少假阴性，每棵树的图像被划分为九个象限。对于每个象限，计算了九种不同的植被指数（VI），同时通过目视检查将每个树象限标记为“健康/不健康”。使用三种监督二元分类算法，以VI作为预测因子，构建能够预测树象限状态的模型。在所考虑的九种VI中，只有五种（GNDVI、GCI、NDREI、NRI和GI）是良好的预测因子，而NDVI、SAVI、RECI和TCARI不是。使用这些指数，无论采用何种算法，模型准确率约为65%，假阴性率为13%，这表明某些VI能够推断这些树木的生理病理状况。这些成果支持使用无人机拍摄的图像对榛子树进行快速、无损的生理特征描述。这种方法为在农业实践中支持农民决策过程提供了一种可持续策略。
