# Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ
## 5 Discussion and Conclusions  
Both analyses, for gaps larger than 4200 pixels2 and those larger than 2100 pixels2, outperformed those in [14]. The developed application shows promise for effectively identifying small field gaps between 50 and 100 cm. The YOLOv5 neural network successfully identified nearly all the field gaps, allowing users to customize the threshold for gap size consideration and choose whether to discard smaller gaps based on their specific needs. Figure 21: Three gaps not identified (false negatives). Figure 20: Gaps greater than 2100 pixels2 selected using the ImageJ script. Field gaps present a significant challenge to sugarcane productivity, especially given the crop's semi-perennial nature. These gaps not only affect the yield of the current year but also have repercussions on productivity in subsequent years. The identification and mapping of field gaps play a crucial role in informing replanting strategies and estimating productivity. Some studies have focused on identifying and mapping field gaps, but they often struggle to detect small gaps due to spatial resolution limitations and occlusions from sugarcane leaves [11; 12]. In our solution, using GSD imagery with a resolution of 1.4 cm per pixel, we successfully mapped almost all the small gaps of 50 cm or larger in an image of 2309 \(\times\) 2309 pixels. Since Imagej selects particles based on area, the 50 cm gaps were converted to an area of 2100 pixels2. The application presented in this work has the potential to be an innovative solution by integrating computer vision, machine learning, convolutional neural networks, and digital image processing. Recognizing objects in images is challenging due to variations in scale, as objects may appear at different distances, yet their sizes still need to be identified. For instance, when detecting planting failures, objects of various sizes can be considered failures, but economically significant failures are typically those longer than 50 cm. Convolutional neural networks are not inherently suited for this task, so using a digital image processing tool like ImageJ enables the accurate selection of empty spaces that truly qualify as failures. This approach also offers flexibility for producers to tailor the identification process, accommodating factors such as failures caused by improperly calibrated harvesting machines and other equipment. Since occlusions from leaves or weeds reduce the visible space between fillers, thereby concealing planting failures, we plan to treat these spaces as indicators of planting failures in future research. This approach will enhance the accuracy of identifying and mapping planting failures. Conceptualization, I.H.Y. and E.A.S. ; methodology, I.H.Y. ; scripting and software configuration, I.H.Y. and J.P.N.d.L. ; validation, I.H.Y. and J.P.N.d.L. ; research, I.H.Y. and E.A.S. ; sample annotation, J.P.N.d.L. and I.H.Y. ; resources, I.H.Y. and E.A.S. ; data curation, I.H.Y. ; writing--original draft preparation, I.H.Y. ; writing--review, E.A.S. and F.C.d.S. ; supervision, E.A.S. ; project administration, F.C.d.S. ; funding acquisition, F.C.d.S. All authors have read and agreed to the published version of the manuscript. The authors thank the Embrapa and Embracal project 30.21.90.004.00.00--Improvement of technical recommendations for correcting soil acidity and its phytotechnical implications in sugarcane fields for the support provided to carry out this work. Not applicable. The data presented in this study are available on request from the corresponding author. The authors thank Coplacana employees [PERSON], [PERSON], and [PERSON] for the technical support in the experimental areas located in Piracicaba, SP, Brazil. The authors also thank AgroAzul Company located in Sertaozinho/SP, Brazil for their excellent work in providing the imagery services. The authors declare no conflicts of interest. **Table A2.