AI-Enabled Droplet Detection and Tracking for Agricultural Spraying Systems

his work leverages recent advancements in com- puter vision and deep learning to detect and track the motion of droplets captured by a camera. While classical computer vision techniques have been employed for detection and tracking, those approaches have limitations and are not trivially extended to droplets. We approach the problems of droplet detection and tracking through a data-driven framework, in which an annotated database of droplet images is built and object detection and tracking models are trained on this database. The accuracy of the model is evaluated and the whole process is discussed. At this point, droplet geometric properties can be extracted. This information is critical in understanding the effectiveness of a system that is spraying the droplets

Deep-Learning Framework for Optimal Selection of Soil Sampling Sites

This work leverages the recent advancements of deep learning in image processing to find optimal locations that present the important characteristics of a field. The data for training are collected at different fields in local farms with five features: aspect, flow accumulation, slope, NDVI (normalized difference vegetation index), and yield. The soil sampling dataset is challenging because the ground truth is highly imbalanced binary images. Therefore, we approached the problem with two methods, the first approach involves utilizing a state-of-the-art model with the convolutional neural network (CNN) backbone, while the second is to innovate a deep-learning design grounded in the concepts of transformer and self-attention. Our framework is constructed with an encoder-decoder architecture with the self-attention mechanism as the backbone. In the encoder, the self-attention mechanism is the key feature extractor, which produces feature maps. In the decoder, we introduce atrous convolution networks to concatenate, fuse the extracted features, and then export the optimal locations for soil sampling. Currently, the model has achieved impressive results on the testing dataset, with a mean accuracy of 99.52%, a mean Intersection over Union (IoU) of 57.35%, and a mean Dice Coefficient of 71.47%, while the performance metrics of the state-of-the-art CNN-based model are 66.08%, 3.85%, and 1.98%, respectively. This indicates that our proposed model outperforms the CNN-based method on the soil-sampling dataset. To the best of our knowledge, our work is the first to provide a soil-sampling dataset with multiple attributes and leverage deep learning techniques to enable the automatic selection of soil-sampling sites. This work lays a foundation for novel applications of data science and machine-learning technologies to solve other emerging agricultural problems.Comment: This paper is the full version of a poster presented at the AI in Agriculture Conference 2023 in Orlando, FL, US

A deep-learning framework for spray pattern segmentation and estimation in agricultural spraying systems

Abstract This work focuses on leveraging deep learning for agricultural applications, especially for spray pattern segmentation and spray cone angle estimation. These two characteristics are important to understanding the sprayer system such as nozzles used in agriculture. The core of this work includes three deep-learning convolution-based models. These models are trained and their performances are compared. After the best model is selected based on its performance, it is used for spray region segmentation and spray cone angle estimation. The output from the selected model provides a binary image representing the spray region. This binary image is further processed using image processing to estimate the spray cone angle. The validation process is designed to compare results obtained from this work with manual measurements