Robotic technologies are affording opportunities to revolutionize the production of specialty crops (fruits, vegetables, tree nuts, and horticulture). They offer the potential to automate tasks and save inputs such as labor, fertilizer, and pesticides. Specialty crops are well known for their high economic value and nutritional benefits, making their production particularly impactful. While previous review papers have discussed the evolution of agricultural robots in a general agricultural context, this review uniquely focuses on their application to specialty crops, a rapidly expanding area. Therefore, we aimed to develop a state-of-the-art review to scientifically contribute to the understanding of the following: (i) the primary areas of robots’ application for specialty crops; (ii) the specific benefits they offer; (iii) their current limitations; and (iv) opportunities for future investigation. We formulated a comprehensive search strategy, leveraging Scopus^® and Web of Science™ as databases and selecting “robot” and “specialty crops” as the main keywords. To follow a critical screening process, only peer-reviewed research papers were considered, resulting in the inclusion of 907 papers covering the period from 1988 to 2024. Each paper was thoroughly evaluated based on its title, abstract, keywords, methods, conclusions, and declarations. Our analysis revealed that interest in agricultural robots for specialty crops has significantly increased over the past decade, mainly driven by technological advancements in computer vision and recognition systems. Harvesting robots have arisen as the primary focus. Robots for spraying, pruning, weed control, pollination, transplanting, and fertilizing are emerging subjects to be addressed in further research and development (R&D) strategies. Ultimately, our findings serve to reveal the dynamics of agricultural robots in the world of specialty crops while supporting suitable practices for more sustainable and resilient agriculture, indicating a new era of innovation and efficiency in agriculture.

The assessment of blueberry fruit quality is traditionally conducted through laboratory equipment. Despite its high accuracy, this method remains destructive, labor-intensive, time-consuming, and costly. Consequently, there is a pressing need for innovative solutions such as image-based and artificial intelligence (AI)-driven analysis. To address these limitations, this study aimed to analyze whether an approach based on mobile image-based analysis combined with machine learning (ML) algorithms could develop a non-destructive framework for evaluating blueberry fruit quality, specifically focusing on total soluble solids (TSS) and firmness. Firstly, we collected numerous blueberry samples during the maturity stage to construct a comprehensive dataset. These samples were meticulously analyzed in a laboratory for diameter, TSS, firmness, and color. Simultaneously, RGB images were captured using a mobile device. These images were processed to extract spectral bands (red, green, and blue). Eight ML algorithms were employed to develop predictive models capable of predicting the qualitative parameters of the blueberries. Initially, correlation analysis demonstrated that RGB images suggestively contribute to fruit quality assessment (r < 0.41). However, the integration of ML algorithms significantly enhanced the predictive accuracy of these models (R² = 0.71–0.99, MAE = 0.003–0.28, and RMSE = 0.004–0.31). This innovative approach differs from existing methods by providing a non-destructive, cost-effective, and efficient alternative to assess blueberry fruit quality. Our findings, therefore, confirm the applicability of mobile images for supporting high-quality blueberry harvesting. These advancements can potentially reduce labor costs, increase yield quality, and support advancements in precision agriculture.

The use of unmanned aerial application systems (UAASs) for precision pesticide applications has increased alongside the demand for sustainable agricultural practices. However, limited studies have standardized the necessary flight parameters ensuring the optimal use of UAASs in specialty crops (e.g., fruits and vegetables). Thus, the objective of this study was to evaluate the effects of flight speed, droplet size, and application volume on the spray deposition of UAASs, creating guidelines to facilitate their use in specialty crops. Field experiments were conducted in a three-factorial experimental design of three flight speeds (i.e., 4, 7, and 10 m/s), three droplet sizes (i.e., 150, 250, and 350 µm), and two application volumes (i.e., 18.75 and 28.10 L/ha). Spraying droplet parameters (i.e., coverage, droplet density, and droplet spectra, and application uniformity), measured through the effective swath width, were recorded to assess spray deposition efficiency. Flight speed, droplet size, and application volume significantly influenced spray deposition. Treatments with slower flight speeds (4 m/s) and higher application volumes (28.10 L/ha) increased spray coverage, while droplet density was maximized at 4 m/s with the finest droplet size (150 µm), which are desirable characteristics for pesticide applications in specialty crops. Ultimately, the effective swath width and spray uniformity were maximized at a flight speed of 7.93 m/s with a droplet size of 350 µm. These results help optimize UAAS-based pesticide application, increasing efficiency and reducing environmental impact; however, understanding pesticide translocation dynamics (i.e., systemic or contact) on plants is key for growers to determine flight parameters.

Sugarcane harvester cutting blade wear increases ratoon damages and losses, impairing sugarcane regrowth. This study aimed to evaluate the quality of basal cut using ratoon damage and loss indexes and correlating them with effects on sugarcane regrowth. Harvest parameters such as plant height and position, damage and loss indexes, and stem length were evaluated every 30 min, following statistical process control (SPC) assumptions. Blade wear was examined during three harvesting shifts (0-8 h, 8-16 h, and 16-24 h). Sugarcane regrowth was assessed by counting number of tillers and measuring plant height and stem diameter. The second harvesting shift showed better quality in terms of damage and loss indexes, and plant height and stem diameter. Harvesting was considered out of control for parameters such as cutting height, number of tillers, plant height, and stem diameter. On the other side, it was regarded as under control for damage and loss indexes, and plant high and position. The first shift presented peripheral or no damages and weak to medium loss levels, whereas the second and third ones showed extreme damages and medium to strong losses.

The advancement of technology in agriculture has driven the use of drones for spraying, with their increasing adoption presenting challenges in calibration and volumetric distribution efficiency. This study aimed to evaluate the volumetric distribution of drone spraying by combining different operational parameters to determine spray swath and application uniformity. Experiments were conducted using a DJI T10 drone and a volumetric distribution table to assess the impact of different flight heights (2, 3, and 4 m), application volumes (8, 12, 16, and 20 L ha⁻¹), and nozzle types (FV 110 015, FL 110 010, and CO 080 010). Environmental conditions were monitored, and data were analyzed using histograms, analysis of variance (ANOVA) by F-test (p ≤ 0.05), and the Scott–Knott test (p ≤ 0.05) to group means. Results indicated that a lower application volume (8 L ha⁻¹) led to greater application uniformity and a narrower spray swath. Higher flight altitude (4 m) resulted in a wider spray swath and a normal distribution of spray deposition. Fine droplet nozzles (CO 080 010) enhanced uniformity, while very coarse droplets (FV 110 015) concentrated more volume in the center of the swath. Thus, using fine droplet nozzles (CO 080 010), lower application volume (8 and 12 L ha⁻¹), and higher flight altitude (4 m) as operational parameters maximizes drone spraying efficiency; however, this also increases drift potential.