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Precision agriculture aims to improve crop management using advanced analytical tools. In this context, the objective of this study is to develop an innovative predictive model to estimate the yield and morphological quality, such as the circularity and length–width ratio of potato tubers, based on phenotypic characteristics of plants and data captured through spectral cameras equipped on UAVs. For this purpose, the experiment was carried out at the Santa Ana Experimental Station in the central Peruvian Andes, where advanced potato clones were planted in December 2023 under three levels of fertilization. Random Forest, XGBoost, and Support Vector Machine models were used to predict yield and quality parameters, such as circularity and the length–width ratio. The results showed that Random Forest and XGBoost achieved high accuracy in yield prediction (R2 > 0.74). In contrast, the prediction of morphological quality was less accurate, with Random Forest standing out as the most reliable model (R2 = 0.55 for circularity). Spectral data significantly improved the predictive capacity compared to agronomic data alone. We conclude that integrating spectral indices and multitemporal data into predictive models improved the accuracy in estimating yield and certain morphological traits, offering key opportunities to optimize agricultural management.
Precision agriculture aims to improve crop management using advanced analytical tools. In this context, the objective of this study is to develop an innovative predictive model to estimate the yield and morphological quality, such as the circularity and length–width ratio of potato tubers, based on phenotypic characteristics of plants and data captured through spectral cameras equipped on UAVs. For this purpose, the experiment was carried out at the Santa Ana Experimental Station in the central Peruvian Andes, where advanced potato clones were planted in December 2023 under three levels of fertilization. Random Forest, XGBoost, and Support Vector Machine models were used to predict yield and quality parameters, such as circularity and the length–width ratio. The results showed that Random Forest and XGBoost achieved high accuracy in yield prediction (R2 > 0.74). In contrast, the prediction of morphological quality was less accurate, with Random Forest standing out as the most reliable model (R2 = 0.55 for circularity). Spectral data significantly improved the predictive capacity compared to agronomic data alone. We conclude that integrating spectral indices and multitemporal data into predictive models improved the accuracy in estimating yield and certain morphological traits, offering key opportunities to optimize agricultural management.
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.
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