Advancing Horticultural Crop Loss Reduction Through Robotic and AI Technologies: Innovations, Applications, and Practical Implications

Abstract

Horticulture, a critical component of agriculture, encounters various challenges, including crop loss stemming from factors like pests, diseases, adverse weather conditions, and inefficient farming practices. The introduction of advanced technologies such as robotics and artificial intelligence (AI) holds great promise in mitigating crop losses and bolstering productivity in the field of horticulture. Robotic systems have been devised to automate labor-intensive tasks involved in horticulture, such as harvesting, pruning, and weeding. Equipped with sensors, cameras, and intelligent algorithms, these robots are capable of identifying ripe fruits, detecting and removing weeds, and performing precise pruning operations. For example, Peixoto et al. in 2015 employed fuzzy systems to create a model for controlling soybean aphids, significantly improving the timing of predator release and enhancing integrated pest management (IPM). By reducing the reliance on human labor and enhancing operational efficiency, the integration of robotic solutions contribute to the minimization of crop losses and the augmentation of yields. In horticulture crop loss reduction, AI plays a vital role when coupled with machine learning algorithms. By analyzing extensive volumes of data encompassing weather patterns, soil conditions, and occurrences of pests and diseases, AI systems can provide farmers with real-time insights and predictive models. This allows for proactive decision-making regarding optimal timing for pesticide application, irrigation scheduling, and disease detection. Consequently, farmers can adopt preventive measures, minimizing losses and optimizing resource utilization. For instance, Ji et al. in 2007 developed an artificial neural network (ANN)-based system for rice yield prediction in Fujian, China, improving accuracy over traditional models. Moreover, AI-powered imaging techniques, such as computer vision, enable the early detection of diseases, pests, and nutrient deficiencies in plants. Early detection empowers farmers to take prompt action, averting the further spread of diseases and minimizing crop losses. Tobal and Mokthar in 2014 pioneered an AI-assisted image processing method for weed identification, introducing an evolutionary ANN to optimize neural parameters using a genetic algorithm. However, the implementation of these technologies face challenges such as high initial costs, the need for technical expertise, and the integration of various data sources. Additionally, small-scale farmers may find it difficult to adopt these technologies due to financial and infrastructural constraints. By harnessing the potential of robotics and AI, the horticulture sector can overcome challenges related to crop losses caused by pests, diseases, adverse weather conditions, and inefficient farming practices. These technological applications offer a pathway to enhanced productivity, reduced losses, and greater sustainability in horticulture. As we move forward, it is imperative to continue advancing and integrating these technologies, fostering innovation and collaboration between technology developers and the farming community.