Computer Vision and Image Processing for Precision Agriculture

Khatri, Narendra; Shinde, Gopal U.

doi:10.1002/9781119792109.ch11

Cited by 5 publications

(2 citation statements)

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“…An agricultural field may have several processes monitored and automated by means of Internet of Things (IoT) systems, which utilize a variety of sensors [9] or the use of computer vision in agriculture for detection, harvesting, sorting and grading, machine navigation, and field robotics [10]. A brief discussion of the possible integration of deep learning with computer vision technologies through agricultural automation, computer vision may enhance small-scale farming through high performance and precision.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It's possible that YOLOv8-S has a neck architecture that combines characteristics from several sizes. In YOLOv8 variations, PANet (Path Aggregation Network) [10] is frequently employed for this purpose. For object detection, the detection head is in charge of estimating bounding boxes, class probabilities, and confidence ratings.…”

Section: B Deep Learning Model Architecture -mentioning

confidence: 99%

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Precision Pest and Disease Detection System using Agricultural Robot

Inamdar,

Bhalerao,

Shinde

et al. 2023

Preprint

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The agricultural industry places a high priority on crop protection, especially when it comes to protecting cotton crops against pink bollworm infestations. Rapid disease identification is essential for effective crop management at the same time. In order to overcome these difficulties, this study offers a thorough process for utilizing the Robot Operating System (ROS) to deploy an autonomous robot. The robot is intended to identify infestations of pink bollworm in addition to counting and tracking red Disease that impact cotton harvests. Drones are used to check the condition of cotton fields, and the autonomous robot's path planning component is closely related to this process. Large fields are photographed by these drones in multispectral mode, and the robot's course planning technique is done by calculating the Normalized Difference Vegetation Index (NDVI) based on these images. This approach ensures targeted surveillance and intervention, optimizing the use of resources. A customized dataset was created especially for this application to improve the robot's detecting abilities. The dataset was utilized to train a YOLOV8 model, a state-of-the-art object detection architecture. Performance characteristics of the trained model are impressive; it has a mean Average Precision (mAP) of 67.1%, Precision of 67.9%, and Recall of 61.8%. These metrics highlight how well the model works to precisely locate and measure interesting occurrences in the cotton fields. In order to address the particular challenges presented by pink bollworm infestations and crop diseases in the context of cotton cultivation, this research contributes a comprehensive solution for autonomous crop monitoring and protection by seamlessly integrating ROS, drones, NDVI calculations, and a robust detection model.

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Section: Literature Reviewmentioning

confidence: 99%

Section: B Deep Learning Model Architecture -mentioning

confidence: 99%