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1. Estimating soil organic carbon from cell phone imagesSoil organic matter (SOM) is considered as the backbone of soil health and soil quality. Thus, its’ estimation is critical to support the development of management decision including precision agriculture. To overcome challenges of laborious, rather expensive and time-consuming laboratory measurements, recent advances in image acquisition systems provided a new dimension of image-based SOM prediction. However, challenges remain in using soil images taken directly in the field due to var... A. Biswas, Y. Fu, P. Taneja, S. Lin, P. Daggupati, H. Vasava |
2. Estimating greensnap yield damage with canopy reflectance: a case studyGrain yield reduction caused by storm-induced plant breakage (green snap) occurs often in corn fields. With climate change and an increasing frequency in the occurrence of extreme weather events, it is essential to develop methods that can accurately estimate green snap damage, so growers can be properly compensated by insurance companies for yield loss. Because plant breakage also affects crop canopy reflectance, this case study aimed to characterize the changes in crop canopy reflecta... G. Dias Paiao, T.J. Nigon, F.G. Fernández, C. Cummings, S.L. Naeve |
3. Spectral assessment of chickpea morpho-physiological traits from space, air and groundChickpea (Cicer arietinum) is an important grain legume in semi-arid regions and water-stress is a major constraint to its productivity. Area under chickpea cultivation is growing but climate change toward greater aridity results in higher precipitation instability and risks yields. The ability to assess water potential can support irrigation decisions. Thus, improved ability to spatially assess plants water status can promote more efficient irrigation. The cu... I. Herrmann, R. Sadeh, A. Avneri, Y. Tubul, R. Lati, S. Abbo, D.J. Bonfil, Z. Peleg |
4. An Ensemble-Based Deep Learning Approach for Early and Accurate Wheat Disease DetectionCrop diseases are the primarily cause for yield loss and a factor for food security issue around the globe. Crop diseases caused by pathogens pose a significant threat to global food security, the challenge become worst particularly in developing countries like Ethiopia. Rapid population growth and accurate disease identification is crucial for timely intervention and minimizing crop losses. However, traditional methods often rely on expert analysis, which can be time-consuming and resource-i... T. Aboneh, P. Rorissa |
5. Multivariate Regional Deep Learning Prediction of Soil Properties from Near-Infrared, Mid-Infrared and Their Combined SpectraArtificial neural network (ANN) models have been successfully used in infrared spectroscopy research for the prediction of soil properties. They often show better performance than conventional methods such as partial least squares regression (PLSR). In this study we develop and evaluate a multivariate extension of ANN for predicting correlated soil properties: total carbon (C), total nitrogen (N), clay, silt, and sand contents, using visible near-infrared (vis-NIR), mid-infrared (MIR) or comb... R. Nyawasha |