Weed invasion of crop fields, such as maize, is a major threat leading to yield reductions or crop right-offs for smallholder farming, especially in developing countries. A synoptic view and timeous detection of weed invasions can save the crop. The sustainable development goals (SDGs) have identified food security as a major focus point. The objectives of this study are to: (1) assess the precision of mapping maize-weed infestations using multi-temporal, unmanned aerial vehicle (UAV), and PlanetScope data by utilizing machine learning algorithms, and (2) determine the optimal timing during the maize growing season for effective weed detection. UAV and PlanetScope satellite imagery were used to map weeds using machine learning algorithms—random forest (RF) and support vector machine (SVM). The input features included spectral bands, color space channels, and various vegetation indices derived from the datasets. Furthermore, principal component analysis (PCA) was used to produce principal components (PCs) that served as inputs for the classification. In this study, eight experiments are conducted, four experiments each for UAV and PlanetScope datasets spanning four months. Experiment 1 utilized all bands with the RF classifier, experiment 2 used all bands with SVM, experiment 3 employed PCs with RF, and experiment 4 utilized PCs with SVM. The results reveal that PlanetScope achieves accuracies below 49% in all four experiments. The best overall performance was observed for experiment 1 using the UAV based on the highest mean accuracy score (>0.88), which included the overall accuracy, precision, recall, F1 score, and cross-validation scores. The findings highlight the critical role of spectral information, color spaces, and vegetation indices in accurately identifying weeds during the mid-to-late stages of maize crop growth, with the higher spatial resolution of UAV exhibiting a higher precision in the classification accuracy than the PlanetScope imagery. The most optimal stage for weed detection was found to be during the reproductive stage of the crop cycle based on the best F1 scores being indicated for the maize and weeds class. This study provides pivotal information about the spatial distribution of weeds in maize fields and this information is essential for sustainable weed management in agricultural activities.

Optimizing the prediction of maize (Zea mays L.) yields in smallholder farming systems enhances crop management and thus contributes to reducing hunger and achieving one of the Sustainable Development Goals (SDG 2—zero hunger). This research investigated the capability of unmanned aerial vehicle (UAV)-derived data and machine learning algorithms to estimate maize yield and evaluate its spatiotemporal variability through the phenological cycle of the crop in Bronkhorstspruit, South Africa, where UAV data collection took over four dates (pre-flowering, flowering, grain filling, and maturity). The five spectral bands (red, green, blue, near-infrared, and red-edge) of the UAV data, vegetation indices, and grey-level co-occurrence matrix textural features were computed from the bands. Feature selection relied on the correlation between these features and the measured maize yield to estimate maize yield at each growth period. Crop yield prediction was then conducted using our machine learning (ML) regression models, including Random Forest, Gradient Boosting (GradBoost), Categorical Boosting, and Extreme Gradient Boosting. The GradBoost regression showed the best overall model accuracy with R² ranging from 0.05 to 0.67 and root mean square error from 1.93 to 2.9 t/ha. The yield variability across the growing season indicated that overall higher yield values were predicted in the grain-filling and mature growth stages for both maize fields. An analysis of variance using Welch’s test indicated statistically significant differences in maize yields from the pre-flowering to mature growing stages of the crop (p-value < 0.01). These findings show the utility of UAV data and advanced modelling in detecting yield variations across space and time within smallholder farming environments. Assessing the spatiotemporal variability of maize yields in such environments accurately and timely improves decision-making, essential for ensuring sustainable crop production.

Mapping smallholder maize farms in complex and uneven rural terrain is a major barrier to accurately documenting the spatial representation of the farming units. Remote sensing technologies rely on various satellite products for differentiating maize cropland cover from other land cover types. The potential for multi-temporal Sentinel-1 synthetic aperture radar (SAR), Sentinel-2, digital elevation model (DEM) and precipitation data obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) version 2.0 was investigated for mapping maize crop distributions during the growing seasons, 2015–2021, in the Sekhukhune municipal area of Limpopo, a province in South Africa. Sentinel-1 variables, including monthly VH, VV, VV+VH (V = vertical, H = horizontal) polarization band data and data issuing from the principal component analysis of VH polarization were integrated with Sentinel-2-derived normalized difference vegetation index (NDVI), DEM terrain, and precipitation data. The random forest (RF) algorithm was applied to distinguish maize crops from four other land cover types, including bare soil, natural vegetation, built-up area, and water. The findings indicated that the models that used only Sentinel-1 data as input data had overall accuracies below 71%. The best performing models producing overall accuracies above 83% for 2015–2021 were those where Sentinel-1 (VV+VH) data were integrated with all the ancillary data. Overall, the McNemar test indicated enhanced performance for models where all other ancillary input data had been incorporated. The results of our study show considerable temporal variation in maize area estimates, with 59 240.84 ha in the 2018/2019 growing season compared to 18 462.51 ha in the 2020/2021 growing season. The spatial information gathered through these models proved to be valuable and is essential for addressing food security, one of the objectives of the Sustainable Development Goals.