Nocera Santiago, Gustavo2024-11-062024https://hdl.handle.net/2097/44670Estimating yield before harvesting can lead to important decisions related to crop management and use inputs. For the sorghum (Sorghum bicolor L.), yield estimation before harvest is laborious and time-consuming, mainly due to the fact that it relies on the panicle (head) being removed, threshed, and the grains being counted manually or machine-assisted. In this scenario, the integration of computer vision, artificial intelligence algorithms, and mobile applications represents a potential alternative to manual counting of grains on an on-farm scale. Consequently, this study aimed to develop an improved method for grain counting and, subsequently, yield estimation prior to harvesting, by combining artificial intelligence techniques with the development of a mobile application. Chapter 2 assesses the development of artificial intelligence techniques for the automated counting of grains in on-farm images captured using a mobile phone. In the aforementioned chapter, a total of 648 sorghum panicle images were benchmarked using a mobile phone in a field setting. Subsequently, the number of grains in 198 of these images was manually counted. Two frameworks were used to train segmentation models from the images: Yolov8 and Detectron2, with an average precision of 89% and 75%, respectively. Later, we tested three density models for the counting task: MCNN, TCNN-Seed, and Sorghum-Net (developed by this study), with the last overperforming the other two models with 17% of Mean Average Precision Error (MAPE). Lastly, a polynomial linear model was used to relate the counting obtained from the density model to the whole panicle, resulting in a MAPE of 17%. In Chapter 3, the best models from Chapter 2 (Yolov8, Sorghum-Net, and the polynomial model) were embedded into a mobile application for utilization in on-farm applications. We developed two branches of the mobile application: one operating locally and the other assisted by a web server. The user can obtain the yield estimation by inputting information on plant population and crop condition. A laboratory and a field validation were performed, yielding a relative root mean square error of 22.4% and 21.4%, respectively. The outcomes from this study can be employed by farmers, crop insurance companies, agronomists, breeders, and researchers to rapidly estimate and forecast sorghum grain yield before harvesting.en-USSorghumDeep learningMobile applicationCountingEstimate yieldPhenotypingThesis