Crop and Weeds Classification for Precision Agriculture Using Context-Independent Pixel-Wise Segmentation

Fawakherji, Mulham; Youssef, Ali; Bloisi, Domenico Daniele; Pretto, Alberto; Nardi, Daniele

doi:10.1109/irc.2019.00029

Cited by 98 publications

(65 citation statements)

References 16 publications

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“…Deep learning has been widely used in agriculture, such as disease diagnosis [62,63], crop recognition and classification [64,65], leaf counting [66], etc. To a great extent, these methods have solved the agricultural tasks.…”

Section: Applicationsmentioning

confidence: 99%

“…Firstly, object and background are segmented by semantic segmentation methods, and then foreground objects are classified. A new method combining robust pixel-wise with a coarse-to-fine classifier based on CNN was proposed in [66]. Specifically, the segmentation network was based on a modified version of U-Net architecture.…”

Section: Recognition and Classificationmentioning

confidence: 99%

“…Besides, in dense agricultural scenes, for background processing with high similarity to the object, using semantic segmentation to extract the region of interest can achieve good results [66,78].…”

Section: (4) Solutions To Challengesmentioning

confidence: 99%

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Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Zhang

Liu

Gong

et al. 2020

Sensors

126

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Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.

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Section: Applicationsmentioning

confidence: 99%

Section: Recognition and Classificationmentioning

confidence: 99%

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Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Zhang

Liu

Gong

et al. 2020

Sensors

126

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“…Such an approach is data-intensive (the study used a dataset with more than 10,000 images), requiring sensors that can produce point clouds besides having to train an end-to-end segmentation model. Another study by [35] proposes a two-stage network that uses an end-to-end segmentation network to first create a binary vegetation mask. Vegetation blobs are then passed as patches to a deep VGG-16 network for classification.…”

Section: A: Supervised Learningmentioning

confidence: 99%

“…The method described by [35] is closest to the proposed approach. The authors utilize a deep learning-based method for weed identification.…”

Section: B: Transfer Learningmentioning

confidence: 99%

Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning

et al. 2021

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Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead, identifying weed-infested regions can aid selective chemical treatment of these regions. Advances in analyzing farm images have resulted in solutions to identify weed plants. However, a majority of these approaches are based on supervised learning methods which requires huge amount of manually annotated images. As a result, these supervised approaches are economically infeasible for the individual farmer because of the wide variety of plant species being cultivated. In this paper, we propose a deep learning-based semi-supervised approach for robust estimation of weed density and distribution across farmlands using only limited color images acquired from autonomous robots. This weed density and distribution can be useful in a site-specific weed management system for selective treatment of infected areas using autonomous robots. In this work, the foreground vegetation pixels containing crops and weeds are first identified using a Convolutional Neural Network (CNN) based unsupervised segmentation. Subsequently, the weed infected regions are identified using a fine-tuned CNN, eliminating the need for designing hand-crafted features. The approach is validated on two datasets of different crop/weed species (1) Crop Weed Field Image Dataset (CWFID), which consists of carrot plant images and the (2) Sugar Beets dataset. The proposed method is able to localize weed-infested regions a maximum recall of 0.99 and estimate weed density with a maximum accuracy of 82.13%. Hence, the proposed approach is shown to generalize to different plant species without the need for extensive labeled data.

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UAV Image Based Crop and Weed Distribution Estimation on Embedded GPU Boards

Fawakherji

Potena

Bloisi

et al. 2019

Computer Analysis of Images and Patterns

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Crop and Weeds Classification for Precision Agriculture Using Context-Independent Pixel-Wise Segmentation

Cited by 98 publications

References 16 publications

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review

Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning

UAV Image Based Crop and Weed Distribution Estimation on Embedded GPU Boards

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