Turning measurements into decisions for emerging farmers is central; detailed maps alone do not establish value.
University, CSIR and ARC work links disease, water and yield observation with farmer-facing information systems.
30 sources
Combines ground, satellite and drone observations into farmer-facing decision support.
Highlights data sharing, quality and agronomic interpretation beyond image collection.
Combines thermal and spectral sensing with UAVs for crop and forest health monitoring.
Reports 2021 pilot training and multispectral maize disease and stress research.
Distinguishes private from other use and sets out registration, approval and operating-certificate requirements.
Industry guidance explains the separate aviation and pesticide-application obligations.
A thesis examines UAV/ML water management for smallholder sugarcane and highlights usable decision tools and training.
Compares ground measurements on 31 vines with multispectral UAV imagery in a 2.42-ha vineyard.
Sixteen participatory trials connect cane maturity measurement, smartphone decisions and drone ripening. Realising value also depends on harvest and mill-delivery coordination.
Swayimane study compares object-based and pixel-based crop classification, demonstrating why fragmented intercropping needs methods adapted to field structure.
Limpopo maize study compares classifiers and reports weaker phosphorus-deficiency classification, supporting ground diagnosis before treatment decisions.
Study tests growth-stage and spectral-variable choices for smallholder maize yield prediction, showing the importance of when imagery is collected.
Conference abstract compares UAV/PlanetScope crop and weed classification in Bronkhorstspruit; classification results do not establish herbicide efficacy or farm profits.
Four-date maize study reports stage-dependent prediction performance, supporting uncertainty displays rather than a single blanket AI accuracy claim.
Thesis compares moisture indicators and algorithms across growth stages, underlining the need for ground reference measurements.
Prototype links onboard weed detection to a spray module. Actuation demonstration does not establish field deposition quality or long-term economics.
2017 conference paper includes two technology-provider interviews, useful for industry history but not a large causal yield-impact experiment.
A graduate's crop water-stress project describes equipment delays, sensor integration, extensive ground measurements and local language barriers. Implementation requires people and field processes as well as an aircraft.
This thesis compares RGB and multispectral imagery on smallholder farms. Results support evaluating cheaper RGB for particular decisions while recognising different performance for crop classification and leaf-area estimation.
Thermal and multispectral research on taro shows that soil-background removal and growth stage affect water-status estimation. A thermal colour map alone is insufficient for an irrigation decision.
A Swayimane sugarcane study compares drone-derived indicators with evapotranspiration reference measurements. Scaling depends on how models transfer and how much local instrumentation they require.
Project report on drone-based crop and water monitoring discusses ground validation, remaining weed and water-quality questions, and satellite/cloud integration. Reading covered executive-summary and recommendation excerpts, not all 235 pages.
The 2026 report connects fine-resolution mapping with crop traits for neglected crops such as taro and sweet potato. Heterogeneous smallholder fields need appropriately scaled data. Executive-summary excerpts were read.
This study combines UAV crop mapping with cloud processing. Resolution, training labels and transfer to other farms remain material design choices; the paper states that study data are available on request.
This thesis explores UAV-based maize water stress across growth stages in Swayimane. Its framing reinforces the importance of stage-specific interpretation and local calibration.
The study combines optical/thermal UAV imagery and random forests to estimate maize leaf temperature and stomatal conductance throughout the crop cycle. The institutional abstract was read.
This review maps research on UAV applications for smallholder agriculture. The institutional abstract was read. A 2026 correction was found in search and should be examined before reusing detailed findings.
This article builds water-stress baselines from field canopy/air temperatures and humidity, then models stage-specific stress with UAV variables. It is related to the listed thesis and should not be counted as independent replication.
HSRC analyses adoption by firm size and age using AgriBIS. The sample is innovation-active firms, not all farmers; reported benefits among adopters do not establish causal effects of drones. Relevant article pages were read.
Candidate paper describes South African UAV and spectral datasets. Only search excerpts were available; data accessibility and reuse conditions still require verification from the full paper and distribution service.
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