CNN based Synchronal recognition of Weeds in Farm Crops

Jogi, Yashaswini; Rao, Preethi N; Raksha,; Shetty, Sharadhi; Shreekari,

doi:10.1109/iceca49313.2020.9297449

Cited by 7 publications

(3 citation statements)

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“…In [ 116 ], the authors simply propose a framework for crop and weed classification using deep learning in real-time. They use Dicot and Monocot weeds.…”

Section: Summary Of Identified Articles In Slrmentioning

confidence: 99%

Weed Detection Using Deep Learning: A Systematic Literature Review

Murad

Mahmood

Forkan

et al. 2023

Sensors

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Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

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“…In [ 116 ], the authors simply propose a framework for crop and weed classification using deep learning in real-time. They use Dicot and Monocot weeds.…”

Section: Summary Of Identified Articles In Slrmentioning

confidence: 99%

Weed Detection Using Deep Learning: A Systematic Literature Review

Murad

Mahmood

Forkan

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…Jogi et al [41] studied in-crop weed identification with a deep learning algorithm. The study focuses on the real-time operations of weed detection to improve latency.…”

Section: Related Work a Decision Support Systems (Dss) For Pasture Ma...mentioning

confidence: 99%

“…Chegini et al [42] presented a system that utilizes weed density and bare patches as input to calculate a yield score for Deep learning In a robotic system for spraying Crop [41] Deep learning Not discussed Crop [27] Deep learning Producing notification for farmers Crop [2] Deep learning and optimisation algorithm Not discussed Crop [38] Deep learning In a robotic system for spraying Crop [40] Deep learning Not discussed Crop [39] Deep learning In an automated system but not explicitly discussed Crop [37] Deep learning Weed densities Desert [26] Machine learning and deep learning Not discussed Pasture [42] Deep learning Fuzzy inference system Pasture Our study Deep learning and fuzzy inference system DSS for weed management Pasture pasture areas. The researchers extracted two fuzzy variables from visual data of pastures and integrated them into a Decision Support System (DSS) model.…”

Section: Related Work a Decision Support Systems (Dss) For Pasture Ma...mentioning

confidence: 99%

An Agriprecision Decision Support System for Weed Management in Pastures

Chegini,

Naha,

Mahanti

et al. 2023

IEEE Access

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Pastures are a vital source of dairy products and cattle nutrition, and as such, play a significant role in New Zealand's agricultural economy. However, weeds can be a major problem for pastures, making it a challenge for dairy farmers to monitor and control them. Currently, most of the tasks for weed management are done manually, and farmers lack persistent technology for weed control. This motivated us to design, implement, and evaluate a Decision Support System (DSS) to detect weeds in pastures and provide decisions for the cleanup of weeds. Our proposed system uses two primary inputs: weeds and bare patches. We created a synthetic dataset to train a weed detection model and designed a fuzzy inference system to assess a pasture. We also used a neuro-fuzzy system in our DSS to evaluate our fuzzy model and tune its parameters for better functioning and accuracy. Our work aims to assist dairy farmers in better weed monitoring, as well as to provide 2D maps of weed density and yield score, which can be of significant value when no digital and meaningful images of pastures exist. The system can also support farmers in scheduling, recommending prohibitive tasks, and storing historical data for pasture analysis, collaborated by stakeholders.INDEX TERMS fuzzy systems, object detection, pasture management, decision making, decision support systems, fuzzy neural networks.

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Weedbot: Weed Easy Extraction Using Deep Learning with a Robotic System

Ilangovan,

Meena,

Sharmila Begum

2024

J. Phys.: Conf. Ser.

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Weeds refer to plants that a farmer does not intentionally cultivate because they interfere with or limit the growth and yield of crops. Due to their fast reproduction and spreading, weeds are well-known for quickly increasing in population over large areas. From the very beginning of the agricultural revolution, weed control used to be done by hand but as time and technology advanced so did methods for managing unwanted plants. Human Intervention or using a single-row weed cutter, weeds are removed from fields. Due to the difficulties of eliminating the background class from the image and increasing the accuracy of the navigation system of the bot, as well as the fact that it does not support the technique of extracting weeds from paddy fields, a vehicle method for removing weeds from the field has not been created. These difficulties have been tackled by combining computer vision with automation, minimizing the need for human exertion. The project aims to create a robotic chopper that deal with weeds and employ CNN (Convolutional Neural Network) algorithm for image categorization of weeds and crops. We use image processing techniques in the proposed system to identify weeds. The system is trained with 590 images of weeds and crops, and testing is performed with 254 images. The system obtained an accuracy of 80% towards processing weeds and then chopped by the robotic arm. The robotic arm cutter that has great precision and is simple to operate, inexpensive and does not require hazardous chemicals.

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CNN based Synchronal recognition of Weeds in Farm Crops

Cited by 7 publications

References 1 publication

Weed Detection Using Deep Learning: A Systematic Literature Review

Weed Detection Using Deep Learning: A Systematic Literature Review

An Agriprecision Decision Support System for Weed Management in Pastures

Weedbot: Weed Easy Extraction Using Deep Learning with a Robotic System

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