With the growing demand for efficient solutions to face the challenges posed by population growth and climate change, the use of unmanned aerial vehicles (UAVs) emerges as a promising solution for monitoring biophysical and physiological parameters in forage crops due to their ability to collect high-frequency and high-resolution data. This review addresses the main applications of UAVs in monitoring forage crop characteristics, in addition to evaluating advanced data processing techniques, including machine learning, to optimize the efficiency and sustainability of agricultural production systems. In this paper, the Scopus and Web of Science databases were used to identify the applications of UAVs in forage assessment. Based on inclusion and exclusion criteria, the search resulted in 590 articles, of which 463 were filtered for duplicates and 238 were selected after screening. An analysis of the data revealed an annual growth rate of 35.50% in the production of articles, evidencing the growing interest in the theme. In addition to 1086 authors, 93 journals and 4740 citations were reviewed. Finally, our results contribute to the scientific community by consolidating information on the use of UAVs in precision farming, offering a solid basis for future research and practical applications.

The objective of this study was to correlate the biophysical parameters of forage cactus with visible vegetation indices obtained by unmanned aerial vehicles (UAVs) and predict them with machine learning in different agricultural systems. Four experimental units were conducted. Units I and II had different plant spacings (0.10, 0.20, 0.30, 0.40, and 0.50 m) with East–West and North–South planting directions, respectively. Unit III had row spacings (1.00, 1.25, 1.50, and 1.75 m), and IV had cutting frequencies (6, 9, 12 + 6, and 18 months) with the clones “Orelha de Elefante Mexicana”, “Miúda”, and “IPA Sertânia”. Plant height and width, cladode area index, fresh and dry matter yield (FM and DM), dry matter content, and fifteen vegetation indices of the visible range were analyzed. The RGBVI and ExGR indices stood out for presenting greater correlations with FM and DM. The prediction analysis using the Random Forest algorithm, highlighting DM, which presented a mean absolute error of 1.39, 0.99, and 1.72 Mg ha⁻¹ in experimental units I and II, III, and IV, respectively. The results showed potential in the application of machine learning with RGB images for predictive analysis of the biophysical parameters of forage cactus.

The application of machine learning techniques to determine bioparameters, such as the leaf area index (LAI) and chlorophyll content, has shown significant potential, particularly with the use of unmanned aerial vehicles (UAVs). This study evaluated the use of RGB images obtained from UAVs to estimate bioparameters in sesame crops, utilizing machine learning techniques and data selection methods. The experiment was conducted at the Federal Rural University of Pernambuco and involved using a portable AccuPAR ceptometer to measure the LAI and spectrophotometry to determine photosynthetic pigments. Field images were captured using a DJI Mavic 2 Enterprise Dual remotely piloted aircraft equipped with RGB and thermal cameras. To manage the high dimensionality of the data, CRITIC and Pearson correlation methods were applied to select the most relevant indices for the XGBoost model. The data were divided into training, testing, and validation sets to ensure model generalization, with performance assessed using the R², MAE, and RMSE metrics. XGBoost effectively estimated the LAI, chlorophyll a, total chlorophyll, and carotenoids (R² > 0.7) but had limited performance for chlorophyll b. Pearson correlation was found to be the most effective data selection method for the algorithm.

The environmental changes in the Caatinga biome have already resulted in it reaching levels of approximately 50% of its original vegetation, making it the third most degraded biome in Brazil, due to inadequate grazing practices that are driven by the difficulty of monitoring and estimating the yield parameters of forage plants, especially in agroforestry systems (AFS) in this biome. This study aimed to compare the predictive ability of different indexes with regard to the biomass and leaf area index of forage crops (bushveld signal grass and buffel grass) in AFS in the Caatinga biome and to evaluate the influence of removing system components on model performance. The normalized green red difference index (NGRDI) and the visible atmospherically resistant index (VARI) showed higher correlations (p < 0.05) with the variables. In addition, removing trees from the orthomosaics was the approach that most favored the correlation values. The models based on classification and regression trees (CARTs) showed lower RMSE values, presenting values of 3020.86, 1201.75, and 0.20 for FB, DB, and LAI, respectively, as well as higher CCC values (0.94). Using NGRDI and VARI, removing trees from the images, and using CART are recommended in estimating biomass and leaf area index in agroforestry systems in the Caatinga biome.

Cacti of the genera Opuntia and Nopalea exhibit morphophysiological and biochemical characteristics that favor their adaptation to semiarid environments, such as crassulacean acid metabolism (CAM) and cladode succulence. These strategies reduce water loss and allow the maintenance of photosynthesis under stress conditions. In this study, we evaluated the seasonal variation in the physiological and photochemical responses of forage cactus clones grown in semiarid environments, considering the rainy, dry, and transition seasons. The net photosynthetic rate (P_n) and chlorophyll fluorescence parameters varied significantly as a function of water availability and microclimatic conditions. We found higher CO₂ assimilation rates during the rainy season, while the dry season resulted in a strong impairment of photosynthetic activity, with reductions of 65% in stomatal conductance, 37% in transpiration, 20% in maximum quantum efficiency of photosystem II, and 19% in the electron transport rate. Furthermore, during these periods, we observed an increase in initial fluorescence and non-photochemical dissipation, demonstrating the activation of photoprotective mechanisms against excess light energy. During the transition seasons, the cacti exhibited rapid adjustments in gas exchange and energy dissipation, indicating the adaptive plasticity of CAM pathway. The MIU (Nopalea cochenillifera (L.) Salm-Dyck), OEM (Opuntia stricta (Haw.) Haw.), and IPA (Nopalea cochenillifera (L.) Salm-Dyck) clones demonstrated greater resilience, maintaining greater stability in P_n, instantaneous water use efficiency, and photochemical parameters during the drought. In contrast, the OEA (Opuntia undulata Griffiths) clone showed high sensitivity to water and heat stress, with marked reductions in physiological and photochemical performance. In summary, the photosynthetic efficiency and chlorophyll fluorescence of CAM plants result from the interaction between water availability, air temperature, radiation, and genotypic traits. This study provides a new scientific basis for exploring the effects of environmental conditions on the carbon and biochemical metabolism of cacti grown in a semiarid environment.