Investigating the contribution of image time series observations to cauliflower harvest-readiness prediction.

Cauliflower cultivation is subject to high-quality control criteria during sales, which underlines the importance of accurate harvest timing. Using time series data for plant phenotyping can provide insights into the dynamic development of cauliflower and allow more accurate predictions of when the crop is ready for harvest than single-time observations. However, data acquisition on a daily or weekly basis is resource-intensive, making selection of acquisition days highly important.

The role of traditional ecological knowledge, given the transformation of pastoralism in Central and Eastern Mongolia.

Mongolian nomadic herders traditionally pass on ecological knowledge intergenerationally, mainly within families. However, little is known about how current societal transformation processes may impact the application and transfer of traditional ecological knowledge (TEK) amongst herders. Combining quantitative household survey data with qualitative interviews, we show that TEK is still widely applied amongst herders.

Data-driven crop growth simulation on time-varying generated images using multi-conditional generative adversarial networks.

Background: Image-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions.

Behind the Leaves: Estimation of Occluded Grapevine Berries With Conditional Generative Adversarial Networks.

The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to estimate the harvest is berry counting, as it can be approached non-destructively, and its process can be automated. In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest.