# Similarity-Based Decision Support for Improving Agricultural Practices and Plant Growth  
[PERSON]  
1 Department of Automation, Technical University of Cluj-Napoca, Memorandumului, 40000 Cluj-Napoca, Romania 1 One of the key motivations for this study is to support small and resource-constrained farmers by demonstrating that classic and accessible algorithms, such as K-Nearest Neighbour and Approximate Nearest Neighbour with Locality Sensitive Hashing, can still yield valuable, personalised recommendations. Unlike complex or opaque models, these methods are interpretable, lightweight, and can be applied in settings with limited computational infrastructure. Similarity-based methods identify patterns in user behaviour to generate personalised recommendations. In agriculture, this can translate to insights such as optimal crop selection, resource management strategies, and pest control interventions. A notable benefit of user-user similarity-based methods lies in their ability to compare the profiles of farmers operating under similar agro-environmental conditions and infer actionable practices [4; 5]. This approach facilitates the dissemination of sustainable techniques and encourages innovation among smaller-scale farmers who might lack access to institutional research and advisory services. Machine learning-based decision support systems have already been studied for their potential to optimise farming decisions using historical and contextual data [6]. Studies have demonstrated the role of factors such as soil properties in yield prediction and decision-making [7]. For instance, conservation practices have led to increases in corn yield by up to 41% due to improved soil health [8], while global crop losses from pests and diseases remain substantial, amounting to 20-40% annually and approximately USD 220 billion in economic impact, respectively [9]. Such challenges underscore the value of data-driven support systems capable of mitigating risks and enhancing productivity. Further, similarity-based algorithms can be used to manage emerging threats by aggregating shared information from regional farmer networks. If multiple users report pest outbreaks or adverse climate patterns, the system can suggest countermeasures based on previously observed outcomes. This real-time adaptability provides a robust advantage over static advisory models. Among the core algorithms used in similarity-based systems are K-Nearest Neighbour (KNN) and Approximate Nearest Neighbour using Locality Sensitive Hashing (ANN-LSH). KNN is known for its interpretability and high accuracy in smaller datasets, while ANN-LSH offers scalability and faster processing for real-time applications. This research applies both algorithms to agricultural data, evaluating their performance on structured profiles generated with domain-relevant attributes [10]. A case study of SyrAgri, a web-based system deployed for Malian farmers, demonstrates the practicality of similarity-based decision support systems in agriculture, where contextual personalisation has been successfully implemented [11]. The novelty of this work lies in applying user-user similarity-based methods within an agricultural context, using both KNN and ANN-LSH to match farming profiles based on 13 environmental and operational attributes. Unlike traditional item-based models, this approach supports localised decision-making and lays the groundwork for future real-time capabilities. By integrating synthetically generated, literature-informed datasets, this study contributes a methodological foundation for building adaptable, intelligent agricultural similarity-based decision support systems. The following sections present the structure of this paper: Section 2 presents the materials and methods employed in this study, including data generation and algorithmic implementation. Section 3 discusses the obtained results, highlighting algorithm performance and practical insights. Section 4 provides a detailed discussion of the findings in relation to agricultural decision-making and system scalability.