AI-enabled droplet detection and tracking for agricultural spraying systems

Acharya, Praneel; Burgers, Travis; Nguyen, Kim-Doang

doi:10.1016/j.compag.2022.107325

Cited by 20 publications

(20 citation statements)

References 28 publications

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“…For example, in [20,21], deep learning algorithms were applied for apple detection and segmentation. For agricultural device optimization, the authors of [22][23][24] used deep learning techniques to estimate the cone angle of the spray and the droplet characteristics.…”

Section: Literature Review Of Methodologiesmentioning

confidence: 99%

Soil Sampling Map Optimization with a Dual Deep Learning Framework

Pham,

Nguyen

2024

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Soil sampling constitutes a fundamental process in agriculture, enabling precise soil analysis and optimal fertilization. The automated selection of accurate soil sampling locations representative of a given field is critical for informed soil treatment decisions. This study leverages recent advancements in deep learning to develop efficient tools for generating soil sampling maps. We proposed two models, namely UDL and UFN, which are the results of innovations in machine learning architecture design and integration. The models are meticulously trained on a comprehensive soil sampling dataset collected from local farms in South Dakota. The data include five key attributes: aspect, flow accumulation, slope, normalized difference vegetation index, and yield. The inputs to the models consist of multispectral images, and the ground truths are highly unbalanced binary images. To address this challenge, we innovate a feature extraction technique to find patterns and characteristics from the data before using these refined features for further processing and generating soil sampling maps. Our approach is centered around building a refiner that extracts fine features and a selector that utilizes these features to produce prediction maps containing the selected optimal soil sampling locations. Our experimental results demonstrate the superiority of our tools compared to existing methods. During testing, our proposed models exhibit outstanding performance, achieving the highest mean Intersection over Union of 60.82% and mean Dice Coefficient of 73.74%. The research not only introduces an innovative tool for soil sampling but also lays the foundation for the integration of traditional and modern soil sampling methods. This work provides a promising solution for precision agriculture and soil management.

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Section: Literature Review Of Methodologiesmentioning

confidence: 99%

Soil Sampling Map Optimization with a Dual Deep Learning Framework

Pham,

Nguyen

2024

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“…As for the testing set, it consists of images that are entirely unseen by the model, serving as an independent assessment of its generalization capabilities. Compared to the dataset utilized in our previous work 20 , the newly constructed dataset provides a substantial expansion, amounting to twelve times its previous size. Specifically, the training, validation, and testing subsets encompass 6124, 494, and 275 images, respectively.…”

Section: Data Organizationmentioning

confidence: 99%

“…Tracking of droplets, on the other hand, is crucial for extracting droplet velocity information from a droplet video. Work in 20 addresses this problem by employing the Kalman filter and correspondence matching. However, this previous method has certain limitations.…”

Section: Real-time Deep-learning Based Object Trackingmentioning

confidence: 99%

Deep-Learning Methods for Efficient Real-Time Droplet Tracking in Crop-Spraying Systems

Huynh,

Nguyen

2024

Preprint

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Spray systems in agriculture serve essential roles in the precision application of pesticides, fertilizers, and water, contributing to effective pest control, nutrient management, and irrigation. These systems enhance efficiency, reduce labor, and promote environmentally friendly practices by minimizing chemical waste and runoff. The efficacy of a spray is largely determined by the characteristics of its droplets, including their size and velocity. These parameters are not only pivotal in assessing spray retention, i.e. how much of the spray adheres to crops versus becoming environmental runoff, but also in understanding spray drift dynamics. This study introduces a real-time deep-learning-based approach for droplet detection and tracking, which significantly improves the accuracy and efficiency of measuring these droplet properties. Our methodology leverages advanced AI techniques to overcome the limitations of previous tracking frameworks, employing three novel deep-learning-based tracking methods. These methods are adept at handling challenges like droplet occlusion and varying velocities, ensuring precise tracking in real-time potentially on mobile platforms. The use of a high-speed camera, operating at 2000 frames per second, coupled with innovative automatic annotation tools, enables the creation of a large, accurately labeled droplet dataset for training and evaluation. The core of our framework lies in the ability to track droplets across frames, associating them temporally despite changes in appearance or occlusions. We utilize metrics including Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision (MOTP) to quantify the tracking algorithm's performance. Our approach is set to pave the way for innovations in agricultural spraying systems, offering a more efficient, accurate, and environmentally responsible method of applying sprays. This is a significant step toward sustainable agricultural practices.

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“…Agricultural nozzles’ effectiveness is determined by many different factors, among which the most important are droplet size and velocity and spray pattern . In earlier work 7 , we developed a tool for measuring droplet size and velocity. Thus, there is a critical need for precise measurements of spray pattern to study the efficacy of sprayer systems in the retention of agricultural spray, and adjust the spray pattern to achieve optimal retention, and hence minimize the chemical runoff that pollutes the soil-water-plant systems.…”

Section: Introductionmentioning

confidence: 99%

“…This is driven by the fact that deep-learning methods have recently shown impressive results in computer vision and machine learning. For example, deep learning has been implemented for successful classification 11 – 13 , detection 7 , 14 – 16 , segmentation 17 , 18 , and reinforcement learning 19 – 21 to name a few. Furthermore, this work focuses on using images taken from a common phone camera instead of an expensive high-speed camera which makes it more accessible.…”

Section: Introductionmentioning

confidence: 99%

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

Acharya

Burgers

Nguyen

2023

Sci Rep

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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.

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AI-enabled droplet detection and tracking for agricultural spraying systems

Cited by 20 publications

References 28 publications

Soil Sampling Map Optimization with a Dual Deep Learning Framework

Soil Sampling Map Optimization with a Dual Deep Learning Framework

Deep-Learning Methods for Efficient Real-Time Droplet Tracking in Crop-Spraying Systems

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

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