Over the last decade, the use of unmanned aerial vehicles (UAVs) for plant phenotyping and field crop monitoring has significantly evolved and expanded. These technologies have been particularly valuable for monitoring crop growth and health and for managing abiotic and biotic stresses such as drought, fertilization deficiencies, disease, and bioaggressors. This paper provides a comprehensive review of the progress in UAV‐based plant phenotyping, with a focus on the current use and application of drone technology to gain information on plant growth, development, adaptation, and yield. We reviewed over 200 research articles and discuss the best tools and methodologies for different research purposes, the challenges that need to be overcome, and the major research gaps that remain. First, the review offers a critical focus on elucidating the distinct characteristics of UAV platforms, highlighting the diverse sensor technologies employed and shedding light on the nuances of UAV data acquisition and processing methodologies. Second, it presents a comprehensive analysis of the multiple applications of UAVs in field phenotyping, underscoring the transformative potential of integrating machine learning techniques for plant analysis. Third, it delves into the realm of machine learning applications for plant phenotyping, emphasizing its role in enhancing data analysis and interpretation. Furthermore, the paper extensively examines the open issues and research challenges within the domain, addressing the complexities and limitations faced during data acquisition, processing, and interpretation. Finally, it outlines the future trends and emerging technologies in the field of UAV‐based plant phenotyping, paving the way for innovative advancements and methodologies.

Meeting food demand for the growing population will require an increase to crop production despite climate changes and, more particularly, severe drought episodes. Sorghum is one of the cereals most adapted to drought that feed millions of people around the world. Valorizing its genetic diversity for crop improvement can benefit from extensive phenotyping. The current methods to evaluate plant biomass, leaves area and plants height involve destructive sampling and are not practical in breeding. Phenotyping relying on drone based imagery is a powerful approach in this context. The objective of this study was to develop and validate a high throughput field phenotyping method of sorghum growth traits under contrasted water conditions relying on drone based imagery. Experiments were conducted in Bambey (Senegal) in 2018 and 2019, to test the ability of multi-spectral sensing technologies on-board a UAV platform to calculate various vegetation indices to estimate plants characteristics. In total, ten (10) contrasted varieties of West African sorghum collection were selected and arranged in a randomized complete block design with three (3) replicates and two (2) water treatments (well-watered and drought stress). This study focused on plant biomass, leaf area index (LAI) and the plant height that were measured weekly from emergence to maturity. Drone flights were performed just before each destructive sampling and images were taken by multi-spectral and visible cameras. UAV-derived vegetation indices exhibited their capacity of estimating LAI and biomass in the 2018 calibration data set, in particular: normalized difference vegetative index (NDVI), corrected transformed vegetation index (CTVI), seconded modified soil-adjusted vegetation index (MSAVI2), green normalize difference vegetation index (GNDVI), and simple ratio (SR) (r² of 0.8 and 0.6 for LAI and biomass, respectively). Developed models were validated with 2019 data, showing a good performance (r² of 0.92 and 0.91 for LAI and biomass accordingly). Results were also promising regarding plant height estimation (RMSE = 9.88 cm). Regression plots between the image-based estimation and the measured plant height showed a r² of 0.83. The validation results were similar between water treatments. This study is the first successful application of drone based imagery for phenotyping sorghum growth and development in a West African context characterized by severe drought occurrence. The developed approach could be used as a decision support tool for breeding programs and as a tool to increase the throughput of sorghum genetic diversity characterization for adaptive traits.

Sorghum is the fifth most important cereal crop world-wide and feeds millions of people in the Sahel. However, it often faces early-stage water deficit due to false onsets of rainy seasons resulting in production decrease. Therefore, developing early drought tolerant material becomes a necessity but requires a good knowledge of adaptation mechanisms, which remains to be elucidated. The present study aimed at assessing the effects of early drought stress on ten elite sorghum varieties tested over two years (2018–2019) at the National Agronomic Research Centre (CNRA) of Bambey (Senegal, West Africa). Two different water regimes (well-watered and drought stress) were applied during the dry season. Water stress was applied by withholding irrigation 25 days after sowing for one month, followed by optimal irrigation until maturity. Soil moisture measurements were performed and allowed to follow the level of stress (down to a fraction of transpirable soil water (FTSW) of 0.30 at the end of stress). An agro-physio-morphological monitoring was carried out during the experiment. Results showed highly significant effects of early drought stress in sorghum plants growth by decreasing leaf appearance, biomass, height but also yield set up. The combined analysis of variance revealed highly significant differences (p ≤ 0.01) between varieties in the different environments for most characters. Under water deficit, the variability was less strong on leaf appearance and plant height at the end of stress. The adaptation responses were related to the capacity of varieties to grow up fast and complete their cycle rather, increase the dead leaves weight, reduce photosynthesis rate, stomatal conductance, leaf transpiration and increase the roots length density. However, varieties V1, V2, V8 and V9 showed promising behavior under stress and could be suitable for further application in West Africa for sorghum breeding and farming.

Although the green revolution adapted a handful of crops to homogeneous and high-input industrialized agriculture, much of the global population still relies on the local production of variable crop cultivars by low-input smallholder farms. This diversity of unhomogenized crops¹, like that of the grain and bioenergy crop sorghum^2,3,4,5, offers raw materials for genetic gain and cultivar improvement. However, breeding efforts can be constrained by highly specialized traits and breeding targets⁶. Here, to bridge this diversity, we constructed a 33-member pangenome reference and a diversity panel across 1,984 cultivars and landraces. We leveraged these resources to explore the complex interplay among historical contingency, ongoing adaptation and previously uncharacterized structural diversity. Specifically, our analyses conclusively demonstrated multiple nested and deeply diverged structural variants in the domestication gene SHATTERING1, which distinguish the previously established multicentric origin of sorghum. We then applied landscape genomics to reveal how gene flow and secondary contact created the complex genetic mosaic in contemporary breeding networks. As proof of concept for pangenome-accelerated trait discovery, we connected biosynthetic gene cluster structural variation to phenotypic leaf concentration of the cyanogenic glucoside dhurrin. Combined, these approaches will accelerate breeding and trait discovery and provide a framework for similar applications in other crops.

Sorghum [Sorghum bicolor (L.) Moench], a staple food crop in Mali, has low yields for several reasons including that many farmers do not have the financial resources to purchase state‐of‐the‐art genetics and fertilizers and information is not available on how to optimize yields for heirloom variety. To improve their economic and environmental sustainability, Mali farmers need to understand how to invest their limited resources. In many situations this involves the use of open‐pollinated varieties that have a range of tillering capabilities. This study determined the best population density and nitrogen (N) fertilization combinations for eight locally available sorghum varieties. The research was conducted in 2018 and 2019 and the experimental design was split‐split‐plot randomized block, the treatments were two plant densities (26,600 and 53,300 plants ha^–1), three N levels (0 kg ha^–1, 89 kg ha^–1, and 178 kg ha^–1), and eight varieties that had a range of tillering characteristics. Each treatment was replicated three times and six of the varieties were open pollinated. Results showed that each variety had a different yield response to plant density and N rate. For example, the tall guinea (hybrids FADDA and PABLO) and the short durra‐caudatum A12‐79 cultivar produced higher yields when planted at 53,300 plants ha^–1 and fertilized with 178 kg N ha^–1 N2D2 treatment. For the short guinea‐caudatum improved varieties C2_007‐03 and C2_075‐15 and caudatum GRINKAN and SOUMBA varieties the optimum seeding rate was 53,300 plants ha^–1 that were fertilized with 89 kg N ha^–1 (N1D2). For the tall local guinea TIEBILE variety the highest yields were observed when it was seeded at a rate of 26,600 plants ha^–1 and fertilized with 89 kg N ha^–1. Grain yield increase was associated with yield components and growth traits for eight varieties studied. Panicle numbers per square meter and chlorophyll index were associated with grain yield in guinea hybrid. Grain number per panicle and 1,000 grain weight were involved in increasing grain yield in caudatum varieties. Nitrogen rate and planting density combination in terms of grain yield varied with different sorghum [Sorghum bicolor (L.) Moench] varieties studied. Knowledge of optimum combinations will help producers decide which option to promote to booster sorghum production in Mali.

This work was conducted to study the performance of eight sorghum varieties that contrasted with intensified practices in the Sudano-Sahelian zone of Mali. Two experiments were carried out in 2018 and 2019 rainy seasons at Sotuba Agricultural Research Station in Mali. The experimental design used was a Split-split-plot with three replications and three factors including two plant densities (D1: 26666 plants ha-1 and D2: 53333 plants ha-1) as the main plot, three nitrogen levels (0, 41 and 82 kg N ha-1) as the subplot and varieties as the sub-subplot. Measurements focused on growth and physiological parameters, grain yield and yield components. The results showed that sorghum grain yield was positively correlated with straw yield, leaf area index, grain number per panicle, panicle number per m2, panicle weight per m2 in N0D1 (0 kg N ha-1 and 26666 plants ha-1) and N2D2 (178 kg N ha-1 and 53 333 plants ha-1). Furthermore, straw yield was positively correlated with the leaf area index and panicle weight m-² in N0D1 and in N2D2. Analysis of variance showed that plant density, nitrogen and variety effect on grain and straw yields were significant. The interaction density x nitrogen x variety effect was also significant on grain and straw yields. Grain and straw yields were high in the N2D2 treatment for eight varieties compared to the N0D1 treatment. GRINKAN, C2_075-15 and C2_007-03 varieties had the highest grain and straw yields in N0D1. These caudatum-type varieties could be recommended in less intensive sorghum production areas in Mali. The FADDA variety produced high grain and straw yields in N2D2. Guinea-type hybrid FADDA may be recommended for grain and straw production in intensive sorghum production areas in Mali. Key words: Mali, intensification, varieties, Nitrogen, planting density, yields. This work was conducted to study the performance of eight sorghum varieties that contrasted with intensified practices in the Sudano-Sahelian zone of Mali. Two experiments were carried out in 2018 and 2019 rainy seasons at Sotuba Agricultural Research Station in Mali. The experimental design used was a Split-split-plot with three replications and three factors including two plant densities (D1: 26666 plants ha-1 and D2: 53333 plants ha-1) as the main plot, three nitrogen levels (0, 41 and 82 kg N ha-1) as the subplot and varieties as the sub-subplot. Measurements focused on growth and physiological parameters, grain yield and yield components. The results showed that sorghum grain yield was positively correlated with straw yield, leaf area index, grain number per panicle, panicle number per m2, panicle weight per m2 in N0D1 (0 kg N ha-1 and 26666 plants ha-1) and N2D2 (178 kg N ha-1 and 53 333 plants ha-1). Furthermore, straw yield was positively correlated with the leaf area index and panicle weight m-² in N0D1 and in N2D2. Analysis of variance showed that plant density, nitrogen and variety effect on grain and straw yields were significant. The interaction density x nitrogen x variety effect was also significant on grain and straw yields. Grain and straw yields were high in the N2D2 treatment for eight varieties compared to the N0D1 treatment. GRINKAN, C2_075-15 and C2_007-03 varieties had the highest grain and straw yields in N0D1. These caudatum-type varieties could be recommended in less intensive sorghum production areas in Mali. The FADDA variety produced high grain and straw yields in N2D2. Guinea-type hybrid FADDA may be recommended for grain and straw production in intensive sorghum production areas in Mali. Key words: Mali, intensification, varieties, Nitrogen, planting density, yields.