A cost-effective maize ear phenotyping platform enables rapid categorization and quantification of kernels.

High-throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost-effective combination of a custom-built imaging platform and deep-learning-based computer vision pipeline. A minimal version of the maize (Zea mays) ear scanner was built with low-cost and readily available parts. The scanner rotates a maize ear while a digital camera captures a video of the surface of the ear, which is then digitally flattened into a two-dimensional projection. Segregating GFP and anthocyanin kernel phenotypes are clearly distinguishable in ear projections and can be manually annotated and analyzed using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390 000 kernels, identifying male-specific transmission defects across a wide range of GFP-marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme. Thus, by using this system, the quantification of transmission data and other ear and kernel phenotypes can be accelerated and scaled to generate large datasets for robust analyses.

Emerging contaminants in water used for maize irrigation: Economic and food safety losses associated with ciprofloxacin and glyphosate.

Chemicals used to assure agricultural production and the feasibility of planting sites often end up in bodies of water used for crop irrigation. In a pot study, we investigated the consequences associated with the irrigation of maize with water contaminated by ciprofloxacin (Cipro; 0, 0.2, 0.8, 1.4 and 2.0 µg l-1) and/or glyphosate (0, 5, 25 and 50 mg l-1) on yields and food safety. Glyphosate in concentrations ≥25 mg l-1 prevented plant establishment, regardless of Cipro presence. Evaluations made at the V5 stage of plants reveal that Cipro concentrations ≥0.8 µg l-1 and glyphosate decreased photosynthesis and induced changes in leaf anatomy and stem biophysical properties that may contribute to decreased kernel yields. When those chemicals were applied together, kernel yield reductions were accentuated, evidencing their interactive effects. Irrigation with contaminated water resulted in accumulations of Cipro and glyphosate (as well as its metabolite, aminomethylphosphonic acid) in plant tissues. Accumulation of these chemicals in plant tissues such as leaves and kernels is a problem, since they are used to feed animals and humans. Moreover, these chemicals are of potential toxicological concern, principally due to residue accumulations in the food chain. Specially, the antibiotic residue accumulations in maize tissues can assist the induction of antibiotic resistance in dangerous bacteria. Therefore, we point out the urgency of monitoring the quality of water used for crop irrigation to avoid economic and food-quality losses.

Field-based assessment of the mechanism of maize yield enhancement by Azospirillum lipoferum CRT1.

Plant Growth-Promoting Bacteria (PGPB) of the genus Azospirillum are known to enhance root growth and yield in many plant species including cereals. To probe the underlying mechanisms, correlations between modifications of yield and 6-leaf plantlet characteristics were estimated on maize in four fields with contrasting soil properties over two consecutive years using the commercial isolate A. lipoferum CRT1. In both years, plantlet metabolome, photosynthetic potential and organ morphology were found to display field- and inoculation-specific signatures. Metabolomic analyses revealed that A. lipoferum CRT1 mostly affected sugar metabolism with no suggested impact on N and P assimilation. Mineral nitrogen feeding increased yield but did not affect yield enhancement by the bacterial partner. However, greater improvements of leaf photosynthetic potential correlated with yield diminutions and larger plantlets in all of their proportions correlated with yield enhancements. Bacterial inoculation restored proper seed-to-adult plant ratio when it accidentally dropped below 80%. Only in these cases did it raise yield. All in all, securing mature plant density is hypothesized as being the primary driver of A. lipoferum CRT1-mediated yield enhancement in maize fields.

Root phenes that reduce the metabolic costs of soil exploration: opportunities for 21st century agriculture.

Crop genotypes with reduced metabolic costs of soil exploration would have improved water and nutrient acquisition. Three strategies to achieve this goal are (1) production of the optimum number of axial roots; (2) greater biomass allocation to root classes that are less metabolically demanding; and (3) reduction of the respiratory requirement of root tissue. An example of strategy 1 is the case of reduced crown root number in maize, which is associated with greater rooting depth, N capture and yield in low N soil. An example of strategy 2 is the case of increased hypocotyl-borne rooting in bean, which decreases root cost and increases P capture from low P soil. Examples of strategy 3 are the cases of increased formation of root cortical aerenchyma, decreased cortical cell file number and increased cortical cell size in maize, which decrease specific root respiration, increase rooting depth and increase water capture and yield under water stress. Root cortical aerenchyma also increases N capture and yield under N stress. Root phenes that reduce the metabolic cost of soil exploration are promising, underexploited avenues to the climate-resilient, resource-efficient crops that are urgently needed in global agriculture.

An ultra-high density bin-map for rapid QTL mapping for tassel and ear architecture in a large F2 maize population.

BACKGROUND: Understanding genetic control of tassel and ear architecture in maize (Zea mays L. ssp. mays) is important due to their relationship with grain yield. High resolution QTL mapping is critical for understanding the underlying molecular basis of phenotypic variation. Advanced populations, such as recombinant inbred lines, have been broadly adopted for QTL mapping; however, construction of large advanced generation crop populations is time-consuming and costly. The rapidly declining cost of genotyping due to recent advances in next-generation sequencing technologies has generated new possibilities for QTL mapping using large early generation populations. RESULTS: A set of 708 F2 progeny derived from inbreds Chang7-2 and 787 were generated and genotyped by whole genome low-coverage genotyping-by-sequencing method (average 0.04×). A genetic map containing 6,533 bin-markers was constructed based on the parental SNPs and a sliding-window method, spanning a total genetic distance of 1,396 cM. The high quality and accuracy of this map was validated by the identification of two well-studied genes, r1, a qualitative trait locus for color of silk (chromosome 10) and ba1 for tassel branch number (chromosome 3). Three traits of tassel and ear architecture were evaluated in this population, a total of 10 QTL were detected using a permutation-based-significance threshold, seven of which overlapped with reported QTL. Three genes (GRMZM2G316366, GRMZM2G492156 and GRMZM5G805008) encoding MADS-box domain proteins and a BTB/POZ domain protein were located in the small intervals of qTBN5 and qTBN7 (~800 Kb and 1.6 Mb in length, respectively) and may be involved in patterning of tassel architecture. The small physical intervals of most QTL indicate high-resolution mapping is obtainable with this method. CONCLUSIONS: We constructed an ultra-high-dentisy linkage map for the large early generation population in maize. Our study provides an efficient approach for fast detection of quantitative loci responsible for complex trait variation with high accuracy, thus helping to dissect the underlying molecular basis of phenotypic variation and accelerate improvement of crop breeding in a cost-effective fashion.

Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor.

In this study, the evaluation of the accuracy and performance of a light detection and ranging (LIDAR) sensor for vegetation using distance and reflection measurements aiming to detect and discriminate maize plants and weeds from soil surface was done. The study continues a previous work carried out in a maize field in Spain with a LIDAR sensor using exclusively one index, the height profile. The current system uses a combination of the two mentioned indexes. The experiment was carried out in a maize field at growth stage 12-14, at 16 different locations selected to represent the widest possible density of three weeds: Echinochloa crus-galli (L.) P.Beauv., Lamium purpureum L., Galium aparine L.and Veronica persica Poir.. A terrestrial LIDAR sensor was mounted on a tripod pointing to the inter-row area, with its horizontal axis and the field of view pointing vertically downwards to the ground, scanning a vertical plane with the potential presence of vegetation. Immediately after the LIDAR data acquisition (distances and reflection measurements), actual heights of plants were estimated using an appropriate methodology. For that purpose, digital images were taken of each sampled area. Data showed a high correlation between LIDAR measured height and actual plant heights (R2 = 0.75). Binary logistic regression between weed presence/absence and the sensor readings (LIDAR height and reflection values) was used to validate the accuracy of the sensor. This permitted the discrimination of vegetation from the ground with an accuracy of up to 95%. In addition, a Canonical Discrimination Analysis (CDA) was able to discriminate mostly between soil and vegetation and, to a far lesser extent, between crop and weeds. The studied methodology arises as a good system for weed detection, which in combination with other principles, such as vision-based technologies, could improve the efficiency and accuracy of herbicide spraying.