# Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ
## 1 Introduction  
Sugarcane was the initial crop during the early days of Brazilian colonization, and today, it remains a pivotal component of the country's economy. Nearly all regions across Brazil engage in sugarcane cultivation, with the state of Sao Paulo prominently leading the nation in production. Brazil holds the global distinction as the largest sugar producer and the second-largest ethanol producer [1]. Consequently, sugarcane plays a crucial role in fostering economic growth, generating employment opportunities, and contributing substantially to the country's foreign exchange reserves [2].  
Sugarcane stands out as a semi-perennial crop, setting it apart from annual crops. Unlike most crops that follow an annual planting and harvesting cycle, sugarcane boasts a longer life span, typically ranging from three to six years before it is necessary to establish a new sugarcane crop; i.e., sugarcane fields are renewed, on average, every five years [3].  
This semi-perennial characteristic arises from its capacity to regenerate and yield stalks after harvest, thus ensuring its sustained presence in the field. However, maintaining the health and productivity of the crop over the years demands effective management practices, including proper fertilization, control of pests and diseases, and precise harvest management. Cultivating sugarcane also entails a substantial initial investment, particularly in tasks such as land preparation, planting, and the initial phases of crop development [4].  
Because it lasts for several years, farmers must consider and avoid planting failures in sugarcane fields, because this negatively interferes with the productivity of the crop over these several years of the life of the sugarcane field. The crop's economic return depends on good plantation maintenance, and it is also affected by the presence of planting failures [5].  
Among the factors that cause planting failures are the attack of pests such as nematodes, termites, and sugarcane boll weevil, among others. In addition, failures can be caused by weed infestations, trampling on the stumps, and miscalibrated or worn machinery in harvesting operations [6]. The identification of field gaps allows for the estimation of production breaks, and if gaps exceed 10% [7], gap filling operations are conducted by sugarcane producers [8] to maintain the planned planting density [9]. Gaps are considered empty spaces, measured from center-to-center of the culms at ground level, that are greater than 50 cm [10].  
Because sugarcane fields occupy large areas, manual identification of field gaps must be performed by sampling and without creating location maps, in addition to possible bias problems caused by the sampling system. Therefore, there are some works that identify field gaps based on an analysis of the imagery captured from unmanned aerial vehicles (UAVs) [11; 12]. UAVs cover large areas in a short time, justifying their use. UAV imagery differs from satellite imagery, having sufficient spatial and temporal resolution to identify various objects of interest, such as gaps in sugarcane crops. UAVs are also affordable for most producers, especially when compared with aircraft flights that are excessively expensive [13; 14].  
In this study, we utilized the deep learning neural network YOLOv5 [15] due to its lower computational demands compared to R-CNNs and because it also has a high accuracy. The neural network can identify gaps of any size, including those smaller than 50 cm. Subsequently, using ImageJ software, rural producers can determine the size of the field gaps to be displayed on the map in a parameterized manner.