Accelerating crop improvement for increased yield and better adaptation to changing climatic conditions is an issue of increasing urgency in order to satisfy the ever-increasing global food demand. However, the major bottleneck is the absence of high-throughput plant phenotyping methods for rapid and cost-effective data-driven variety selection and release in plant breeding. Traditional phenotyping methods that rely on trained experts are slow, costly, labor-intensive, subjective, and often require destructive sampling. We explore ways to improve the efficiency of crop phenotyping through the use of unmanned aerial vehicle (UAV)-based multispectral remotely sensed data in maize (Zea mays L.) varietal response to maize streak virus (MSV) disease. Twenty-five maize varieties grown in a trial with three replications were evaluated under artificial MSV inoculation. Ground scoring for MSV infection was carried out at mid-vegetative, flowering, and mid-grain filling on a scale of 1 (resistant) to 9 (susceptible). UAV-derived spectral data were acquired at these three different phenological stages in multispectral bands corresponding to Green (0.53–0.57 μm), Red (0.64–0.68 μm), Rededge (0.73–0.74 μm), and Near-Infrared (0.77–0.81 μm). The imagery captured was stitched together in Pix4Dmapper, which generates two types of multispectral orthomosaics: the NoAlpha and the transparent mosaics for each band. The NoAlpha imagery was used as input into QGIS to extract reflectance data. Six vegetation indices were derived for each variety: normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), Rededge NDVI (NDVI_rededge), Simple Ratio (SR), green Chlorophyll Index (CI_green), and Rededge Chlorophyll Index (CI_rededge). The Random Forest (RF) classifier was used to evaluate UAV-derived spectral and VIs with and without variable optimization. Correlations between the UAV-derived data and manual MSV scores were significant (R = 0.74–0.84). Varieties were classified into resistant, moderately resistant, and susceptible with overall classification accuracies of 77.3% (Kappa = 0.64) with optimized and 68.2% (Kappa = 0.51) without optimized variables, representing an improvement of ~13.3% due to variable optimization. The RF model selected GNDVI, CI_green, CI_rededge, and the Red band as the most important variables for classification. Mid-vegetative was the most ideal phenological stage for accurate varietal phenotyping and discrimination using UAV-derived multispectral data with RF under artificial MSV inoculation. The results provide a rapid UAV-based remote sensing solution that offers a step-change towards data availability at high spatial (submeter) and temporal (daily/weekly) resolution in varietal analysis for quick and robust high-throughput plant phenotyping, important for timely and unbiased data-driven variety selection and release in plant breeding programs, especially as climate change accelerates.

Seed security is vital for food security. Rapid-cycle, climate-adaptive breeding programs and seed systems that deliver new, elite varieties to farmers to replace obsolete ones can greatly improve the productivity of maize-based cropping systems in sub-Saharan Africa (SSA). Despite the importance and benefits of accelerated varietal turnover to climate change adaptation and food security, the rate of maize varietal replacement in SSA is slow. This review outlines the major bottlenecks, drivers, risks, and benefits of active replacement of maize varieties in eastern and southern Africa (ESA) and highlights strategies that are critical to varietal turnover. Although there is an upsurge of new seed companies in ESA and introduction of new varieties with better genetics in the market, some established seed companies continue to sell old (over 15-year-old) varieties. Several recently developed maize hybrids in ESA have shown significant genetic gains under farmers’ conditions. Empirical evidence also shows that timely replacement of old products results in better business success as it helps seed companies maintain or improve market share and brand relevance. Therefore, proactive management of product life cycles by seed companies benefits both the farmers and businesses alike, contributing to improved food security and adaptation to the changing climate.

Hybrid maize seed production in Africa is dependent upon manual detasseling of the female parental lines, often resulting in plant damage that can lead to reduced seed yields on those detasseled lines. Additionally, incomplete detasseling can result in hybrid purity issues that can lead to production fields being rejected. A unique nuclear genetic male sterility seed production technology, referred to as Ms44-SPT, was developed to avoid hybrid seed loss and to improve the purity and quality of hybrid maize production. Hybrid seed yield reduction following detasseling can be attributed to leaf loss. Our analyses showed an average 2.9 leaves are lost during the detasseling process, resulting in a seed yield reduction of 14.0%. These findings suggest that deploying the Ms44-SPT technology would avoid this seed yield loss. By simplifying hybrid production and increasing seed yields, Ms44-SPT could help drive hybrid replacement, providing smallholder farmers with better access to improved hybrids.

Fall armyworm (Spodoptera frugiperda (J.E. Smith)) has become a significant pest to maize production, causing huge yield losses in sub-Saharan Africa. This study evaluated three non-Bt maize hybrids with tolerance to fall armyworm (FAW) along with a commercial hybrid check for yield and agronomic performance under natural FAW infestation and chemical control conditions in both on-station and on-farm trials. Significant differences were observed among the hybrids with the FAW tolerant hybrids showing reduced leaf and ear damage compared to the commercial hybrid. These hybrids also exhibited high grain yield performance, outyielding the commercial check by 197–252%. Mean grain yield under FAW infestation ranged from 6009.88 to 7117.30 kg ha⁻¹ without chemical control, and even higher (8441.24 kg ha⁻¹) under limited chemical control. Stepwise multiple regression analyses identified ear aspect, husk cover and ear damage as key traits accounting for 98% of the total variation in grain yield under FAW infestation. Participatory variety selection showed high farmer preference for these hybrids. The availability of these hybrids to farmers promises to enhance food security, reduce the environmental impact of insecticides, alleviate cost burdens on farmers, and increase household income.