# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 1 Introduction  
The almond tree, _Prunus dulcis_ (var. _dulcis_ (_Rosaceae_)), is a globally important nut tree [1]. Originating from the Middle East and South Asia, almonds are extremely important for the human diet due to their high protein content, good fats, and essential micronutrients,including vitamin E and magnesium. Moreover, almond consumption provides diverse health benefits, from improving cholesterol levels and cardiovascular health, to potentially reducing cancer risks [2]. Almond production is very important for the economy in many regions of the world [3]. Almonds are mainly produced in the United States of America (1,858,010 tons), Australia (360,328 tons), and Spain (245,990 tons) [4]. Climate change poses significant risks to crop yields, due to changing weather patterns and more regular risky weather events, such as floods, droughs, and heat waves [5]. These events can have a negative impact on almond productivity and quality [6], compromising stock supplies and promoting price fluctuations. Given these circumstances, precise crop yield prediction has become indispensable, as it equips policymakers and market participants with essential tools to effectively mitigate these risks. Through the analysis of historical data and current agricultural conditions, it becomes possible to develop models for estimating seasonal yield forecasts and evaluating potential supply shortages or surpluses [7]. Furthermore, this information may assist governmental agencies in making informed decisions regarding trade policies, food aid, and agricultural investments [8]. Crop yield prediction is a difficult undertaking that requires integrating several factors, including weather, soil properties, pest and disease incidence, and management practices [9]. More precise modelling techniques for forecasting agricultural yield have recently been developed, due to developments in technology and data analysis. Simple statistical models (i.e., linear regressions) remain the most popular approach for predicting agricultural yield, providing helpful information for decisionmakers [10]. However, machine learning (ML) algorithms have become a promising approach, as they can increase prediction accuracy by finding patterns and relationships in the data [11]. Due to these facts, machine learning (ML) is currently one of the most important subfields of artificial intelligence (AI) [12]. Remote sensing (RS) is another promising field of research that may potentially benefit crop yield prediction. Advances in RS technologies have made it possible to monitor crop development and health in real time from aerial viewpoints, which has enhanced crop production forecasting. RS technologies provide detailed information on the crop conditions, including plant biomass, water content, and nutrient status, which can be utilized to make more precise predictions about future yields [13]. Numerous studies emphasize the significance of ML and RS in predicting crop yield. [PERSON] et al. [14] developed a systematic literature review to detect prevalent models, features, and evaluation parameters in crop yield prediction. The authors observed that linear regression (LR) and neural networks (NN) are frequently applied models, along with random forest (RF) and support vector machines (SVM). Moreover, rainfall, temperature, and soil type are the main features implemented, along with vegetation indices (VIs), such as the normalized difference vegetation index (NDVI) [15] and the enhanced vegetation index (EVI) [16]. [PERSON] et al. [17] highlighted the application of various RS technologies alongside multi- and hyperspectral data, radar, and LiDAR data in crop monitoring and yield prediction. They identified the NDVI, the EVI, and the soil-adjusted vegetation index (SAVI) [18] as commonly used VIs. Similarly, [PERSON] et al.