Estimation of Maize Foliar Temperature and Stomatal Conductance as Indicators of Water Stress Based on Optical and Thermal Imagery Acquired Using an Unmanned Aerial Vehicle (UAV) Platform

Abstract

Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules.

Melatonin administration decrypts the inferential determinants of idiosyncratic foliar nastic movements

“…Scotophases (ZT 0 & 6 h) experienced a higher melatonin increase than photophases (ZT 12 & 18 h).This has been demonstrated in many studies that the thermal degradation of melatonin is more rapid than photodegradation (Bellmaine et al 2020;Sadak and Ramadan 2021;Song et al 2022). Because the number of stomata in plants functioned as a proxy for temperature (Marek et al, 2022;Brewer et al 2022;Steinthorsdottir et al 2022), it was critical to investigate how melatonin influenced stomatal behaviour on both the adaxial and abaxial leaf surfaces. The stomata in P. oleracea were paracytic (Fig.…”

Use of unmanned aerial vehicle-derived multi-spectral data for the early detection of multi-temporal maize leaf equivalent water thickness and fuel moisture content for the improved resilience of smallholder maize farming

“…30,69 These transitions are then detected from the red edge spectrum, which tends to shift toward the long-wavelength section. 3,10,67,[70][71][72] The impact of foliar moisture reduction and stress is spectrally similar to the impact of nitrogen reduction in terms of how they both impact photosynthesis, the foliar pigments and discoloration of the leaves, and overall plant productivity, which is sensitive to the rededge section of the electromagnetic spectrum. 73,74 This is not surprising because the macronutrient, N, is a solute taken in by plants from the soil as a solution in the solvent, water.…”

“…These transitions are then detected from the red edge spectrum, which tends to shift toward the long-wavelength section. 3 , 10 , 67 , 70 – 72 …”

Temporal field phenomics allows discovery of nature AND nurture, so can we saturate the phenome?

“…Sixth is discovering new traits or signatures for intervention that can be deployed on farm. For instance, detecting disease early to deploy pesticides 28,29 , or drought signatures for irrigation before yield is lost [30][31][32] .…”