In order to enable timely actions to prevent major losses of crops caused by lack of nutrients and, hence, increase the potential yield throughout the growing season while at the same time prevent excess fertilization with detrimental environmental consequences, early, non-invasive, and on-site detection of nutrient deficiency is required. Current non-invasive methods for assessing the nutrient status of crops deal in most cases with nitrogen (N) deficiency only and optical sensors to diagnose N deficiency, such as chlorophyll meters or canopy reflectance sensors, do not monitor N, but instead measure changes in leaf spectral properties that may or may not be caused by N deficiency. In this work, we study how well nutrient deficiency symptoms can be recognized in RGB images of sugar beets. To this end, we collected the Deep Nutrient Deficiency for Sugar Beet (DND-SB) dataset, which contains 5648 images of sugar beets growing on a long-term fertilizer experiment with nutrient deficiency plots comprising N, phosphorous (P), and potassium (K) deficiency, as well as the omission of liming (Ca), full fertilization, and no fertilization at all. We use the dataset to analyse the performance of five convolutional neural networks for recognizing nutrient deficiency symptoms and discuss their limitations.

The identification of environmentally-stable and globally-predictable resistance to potato late blight is challenged by the clonal and polyploid nature of the crop and the rapid evolution of the pathogen. A diversity panel of tetraploid potato germplasm bred for multiple resistance and quality traits was genotyped by genotyping by sequencing (GBS) and evaluated for late blight resistance in three countries where the International Potato Center (CIP) has established breeding work. Health-indexed, in vitro plants of 380 clones and varieties were distributed from CIP headquarters and tuber seed was produced centrally in Peru, China and Ethiopia. Phenotypes were recorded following field exposure to local isolates of Phytophthora infestans. QTL explaining resistance in four experiments conducted across the three countries were identified in chromosome IX, and environment-specific QTL were found in chromosomes III, V, and X. Different genetic models were evaluated for prediction ability to identify best performing germplasm in each and all environments. The best prediction ability (0.868) was identified with the genomic best linear unbiased predictors (GBLUPs) when using the diploid marker data and QTL-linked markers as fixed effects. Genotypes with high levels of resistance in all environments were identified from the B3, LBHT, and B3-LTVR populations. The results show that many of the advanced clones bred in Peru for high levels of late blight resistance maintain their resistance in Ethiopia and China, suggesting that the centralized selection strategy has been largely successful.

single paragraph of max 250 words): 27 The identification of environmentally stable and globally predictable resistance to potato late 28 blight is challenged by the crop's clonal and polyploid nature and the pathogen's rapid 29 evolution. Genome-wide analysis (GWA) of multi-environment trials can add precision to 30 breeding for complex traits. A diversity panel of tetraploid potato germplasm bread for multiple 31 resistance and quality traits was genotyped by genotyping by sequencing (GBS) and 32 phenotyped for late blight resistance in a trait observation network spanning three continents 33 addressed by the International Potato Center's (CIP's) breeding program. The aims of this 34 study were to (i) identify QTL underlying resistance in and across environments and (ii) 35 develop prediction models to support the global deployment and use of promising resistance 36 sources in local breeding and variety development programs. Health-indexed in vitro plants of 37 380 clones and varieties were distributed from CIP headquarters in Peru to China and Ethiopia 38 and tuber seed was produced centrally in each country. Phenotypes were recorded as rAUDPC 39 following field exposure to local isolates of Phytophthora infestans, Stringent filtering for 40 individual read depth >60 resulted in 3,239 tetraploid SNPs. Meanwhile, 55,748 diploid SNPs 41were identified using diploidized data and individual read depth>17. The kinship matrix was 42 utilized to obtain BLUP and identify best performing germplasm in each and all environments. 43