# Identifying Growth Patterns in Arid-Zone Onion Crops (Alium Cepa) Using Digital Image Processing
## 1 Introduction  
Currently, agri-food producers require technological tools to increase crop production levels to satisfy the food demand of the population [1; 2; 3]. Food security must be provided, since the world's population has a growth rate of approximately 1.09% per year, which demands greater requirements for the agricultural industry to provide increasingly higher yields [4]. However, agricultural production costs ignore the immediate negative impacts on other users [5]. In this context, traditional agricultural practices are causing environmental damage, leading to the degradation of natural resources such as soils due to the excessive use of fertilizers and pesticides that the farmer scatters homogeneously to avoid delays in product development [6; 7]. Precision agriculture (PA), a combination of technology and agronomy, offers a viable solution to the challenges faced by traditional farming practices. PA focuses on the integrated management of plots by using technology tools such as the Internet of Things (IoT), machine learning, and drones to gather data on crops' growth and health. PA seeks to identify the variations in the conditions of the plot to carry out differential spatial management. UAVs (unmanned aerial vehicles) have generated tremendous interest in crop monitoring due to the great potential they can offer in the agricultural sector to verify the parameters that affect the development of plants [8; 9]. Aerial images identify growth scans and areas of crop deterioration [10]. This technology helps soil conservation and sustainable agriculture, specifies the causes of soil damage, and identifies damage caused in the early stages of cultivation [11]. PA enables decision-making to improve production through an adequate understanding of the means of agricultural production [12]. Additionally, the current ability to process images with increasingly faster computers aids in analyzing some features of an object. The use of digital image processing (DPI) helps to solve the problems of image segmentation, object area, and characteristics on image-invariant scales, among others. According to [13], information can be obtained with the support of UAVs, which can later be used with different IPR techniques to obtain different behavior patterns in a given area. Various techniques, such as thresholding, different transformations of color spaces, normalized difference vegetation indices (INDVIs), and Excess Green Vegetation (ExG), among others, have been implemented to extract characteristics that define the state of crops such as grains and vegetables [13]. DPI has become an essential tool in precision agriculture. An example of its application is the use of the red-green-blue model (RGB) to carry out Lab color space transformations and the k-means algorithm to extract weeds from onion crops. For example, in [14], the authors mentioned that RGB space colors offer a very high spatial and temporal resolution. In this work, they performed a biomass monitoring system by processing RGB images obtained by a camera mounted on an unmanned aerial vehicle. They reported a 95% accuracy by implementing image analysis instead of invasive sensors. This application highlights the potential for using advanced technologies in PA. DPI enables farmers to detect crop problems early, allowing for targeted interventions and reducing costs while minimizing the environmental impacts. The application of DPI in precision agriculture has the potential to revolutionize the agricultural sector and ensure sustainable agriculture practices [15; 16]. Notably, image processing and computer vision applications in the agriculture sector have grown due to reduced equipment costs, increased computational power, and growing interest in non-destructive food-assessment methods [17]. In recent years, there has been increasing interest in using data analysis and machine learning techniques to identify the patterns affecting crop growth and productivity.