The impact of fungi on soil protist communities in European cereal croplands.

Protists, a crucial part of the soil food web, are increasingly acknowledged as significant influencers of nutrient cycling and plant performance in farmlands. While topographical and climatic factors are often considered to drive microbial communities on a continental scale, higher trophic levels like heterotrophic protists also rely on their food sources. In this context, bacterivores have received more attention than fungivores. Our study explored the connection between the community composition of protists (specifically Rhizaria and Cercozoa) and fungi across 156 cereal fields in Europe, spanning a latitudinal gradient of 3000 km. We employed a machine-learning approach to measure the significance of fungal communities in comparison to bacterial communities, soil abiotic factors, and climate as determinants of the Cercozoa community composition. Our findings indicate that climatic variables and fungal communities are the primary drivers of cercozoan communities, accounting for 70% of their community composition. Structural equation modelling (SEM) unveiled indirect climatic effects on the cercozoan communities through a change in the composition of the fungal communities. Our data also imply that fungivory might be more prevalent among protists than generally believed. This study uncovers a hidden facet of the soil food web, suggesting that the benefits of microbial diversity could be more effectively integrated into sustainable agriculture practices.

Geographic-Scale Coffee Cherry Counting with Smartphones and Deep Learning.

Deep learning and computer vision, using remote sensing and drones, are 2 promising nondestructive methods for plant monitoring and phenotyping. However, their applications are infeasible for many crop systems under tree canopies, such as coffee crops, making it challenging to perform plant monitoring and phenotyping at a large spatial scale at a low cost. This study aims to develop a geographic-scale monitoring method for coffee cherry counting, supported by an artificial intelligence (AI)-powered citizen science approach. The approach uses basic smartphones to take a few pictures of coffee trees; 2,968 trees were investigated with 8,904 pictures in Junín and Piura (Peru), Cauca, and Quindío (Colombia) in 2022, with the help of nearly 1,000 smallholder coffee farmers. Then, we trained and validated YOLO (You Only Look Once) v8 for detecting cherries in the dataset in Peru. An average number of cherries per picture was multiplied by the number of branches to estimate the total number of cherries per tree. The model's performance in Peru showed an R2 of 0.59. When the model was tested in Colombia, where different varieties are grown in different biogeoclimatic conditions, the model showed an R2 of 0.71. The overall performance in both countries reached an R2 of 0.72. The results suggest that the method can be applied to much broader scales and is transferable to other varieties, countries, and regions. To our knowledge, this is the first AI-powered method for counting coffee cherries and has the potential for a geographic-scale, multiyear, photo-based phenotypic monitoring for coffee crops in low-income countries worldwide.