Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species

Abstract

Invasive aquatic plant species can expand rapidly throughout water bodies and cause severely adverse economic and ecological impacts. While mechanical, chemical, and biological methods exist for the identification and treatment of these invasive species, they are manually intensive, inefficient, costly, and can cause collateral ecological damage. To address current deficiencies in aquatic weed management, this paper details the development of a small fleet of fully autonomous boats capable of subsurface hydroacoustic imaging (to scan aquatic vegetation), machine learning (for automated weed identification), and herbicide deployment (for vegetation control). These capabilities aim to minimize manual labor and provide more efficient, safe (reduced chemical exposure to personnel), and timely weed management. Geotagged hydroacoustic imagery of three aquatic plant varieties (Hydrilla, Cabomba, and Coontail) was collected and used to create a software pipeline for subsurface aquatic weed classification and distribution mapping. Employing deep learning, the novel software achieved a classification accuracy of 99.06% after training.

Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

“…All search results were filtered based on five criteria: (1) the study must use remote sensing imagery and platform as the primary data input with at least three spectral bands (red, green, and blue), (2) the study must discuss the application of remote sensing techniques in weed detection, (3) the document must have reported the research conducted, (4) the included documents have been published up to the quarter of 2021, and (5) the articles must be in English.…”

“…It is undoubtful that weeds, also known as invasive plants, have their roles in the ecosystem. However, their presence in crops such as rice, oil palm, rubber, and other mass plantations influences productivity, causes significant economic consequences, decreases land prices, and reduces company profits [1]. Moreover, the current trend shows that farmers worldwide are strongly dependent on herbicides used to control weeds; other control measures include cultural, physical, biological, and mechanical methods [2].…”

Meeting sustainable development goals via robotics and autonomous systems

“…The limited information that is available regarding how RAS may impact the SDGs tends to centre on individual SDGs. Positive impacts include how RAS can improve health through surgical procedures enhancements 13 and integrated nursing care 14 , transform agriculture through changes in weed control practices 15 , and contribute to biodiversity conservation through the control of invasive species 16 – 18 . There is also some concern about how RAS could change the job market 19 , 20 , influence pollution and waste 21 , be detrimental to biodiversity conservation by directly replacing living components of nature, such as pollinators 22 , and could increase carbon emissions from transport if implemented too widely 23 .…”

Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms

“…Besides the classical machine learning algorithms, rich information included in the ultra-high resolution images can be well utilized by the CNN-based deep learning models, with which a series of features are automatically extracted using the convolutional layers [43]. Various studies used machine learning-based [39,40] and CNNbased [44,45] classification methods for encroaching species mapping. The CNN-based classification methods gained promising results for encroaching species mapping [46].…”