Combined analysis of satellite and ground data for winter wheat yield forecasting

We built machine learning and image analysis tools in order to forecast winter wheat yield based on a rich multi dimensional tensor of agricultural information spanning different scales. This information consists of satellite multi-band images, local soil samples obtained from national databases, local weather as well as field data from 23 farms cultivating winter wheat in southern Sweden. This is inherently a large multi-scale problem due to the large temporal and spatial variation of the input data. We aggregate the data on spatially averaged features over grids which temporally span a seasonal timeline from seeding to harvest. Data cleaning is performed through interpolation for satellite images due to cloud obstructions. Furthermore data is heavily imbalanced since the amount of satellite information far exceeds that of the ground data. Data variance therefore can be an issue which we counter by using a decision tree approach. We find that the Light Gradient Boosting decision tree trained on 262 input features is able to predict winter wheat yield with 82% accuracy. Subsequently we employ game theory in order to better understand the relational importance of specific input features towards forecasting yield. Specifically we find that some of the most important features towards the resulting predictions are the percent clay and magnesium in the soil. Similarly the most important features from the satellite data are: a) the NORM index (Euclidean distance of all bands) computed in the second week of April, b) the NORM index computed in the middle of May as well as c) the second spectral band from the last week of June

Yield Prediction for Winter Wheat with Machine Learning Models Using Sentinel-1, Topography, and Weather Data

We train and compare the performance of two different machine learning algorithms to learn changes in winter wheat production for fields from the southwest of Sweden. As input to these algorithms, we use cloud-penetrating Sentinel-1 polarimetry radar data together with respective field topography and local weather over four different years. We note that all of the input data were freely available. During training, we used information on winter wheat production over the fields of interest which was available from participating farmers. The two machine learning models we trained are the Light Gradient-Boosting Machine and a Feed-forward Neural Network. Our results show that Sentinel-1 data contain valuable information which can be used for training to predict winter wheat yield once two important steps are taken: performing a critical transformation of each pixel in the images to align it to the training data and then following up with despeckling treatment. Using this approach, we were able to achieve a top root mean square error of 0.75 tons per hectare and a top accuracy of 86% using a k-fold method with k=5. More importantly, however, we established that Sentinel-1 data alone are sufficient to predict yield with an average root mean square error of 0.89 tons per hectare, making this method feasible to employ worldwide

Label Placement Challenges in City Wayfinding Map Production - Identification and Possible Solutions

Map label placement is an important task in map production, which needs to be automated since it is tedious and requires a significant amount of manual work. In this paper, we identify five cartographic labeling situations that present challenges by causing intensive manual work in map production of city wayfinding maps, e.g., label placement in high density areas, utilizing true label geometries in automated methods, and creating a good relationship between text labels and icons. We evaluate these challenges in an open source map labeling tool (QGIS), provide results from a preliminary study, and discuss if there are other techniques that could be applicable to solving these challenges. These techniques are based on quantified cartographic rules or on machine learning. We focus on deep learning for which we provide several examples of techniques from other application domains that might have a potential in map label placement. The aim of the paper is to explore those techniques and to recommend future practical studies for each of the identified five challenges in map production. We believe that targeting the revealed challenges using the proposed solutions will significantly raise the automation level for producing city wayfinding maps, thus, having a real, measurable impact on production time and costs