Neural Network based Smart Weed Detection System

Siddiqui, Salman; Fatima, Neda; Ahmad, Anwar

doi:10.1109/iccisc52257.2021.9484925

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…The employed self-attention is computationally demanding due to the size of the spatial features. Siddiqui et al [47] explored data augmentation (DA) using CNN methods to distinguish weeds from crops. In another study, Khan et al [48] introduced a new cascaded encoder-decoder network (CED-Net) modifying the base network U-Net into four stages to distinguish between weeds and crops.…”

Section: Deep Feature-based Methodsmentioning

confidence: 99%

“…The strengths and weaknesses of the proposed framework for crop and weed segmentation relative to other techniques are listed in Table 1. SegNet + Enet [38] Fast and more accurate pixelwise predictions Images contain very small portions of crops and weeds U-Net and U-Net++ [40] Detecting weeds in the early stages of growth Uses a very small dataset and has no suitable real-time application Modified U-Net + modified VGG-16 [43] Effective result for distribution estimation problem with graphics processing unit (GPU)-based embedded board Not focusing on the exact location of weeds in the images UFAB [45] Reducing redundancy by strengthening the model diversity Unavailability of RGB and NIR input DA-Net [46] Expanding receptive field without affecting the computational cost Hard and time-consuming mechanism to parallelize the system using attention modules 4-layered CNN + data augmentation [47] Good for the early detection of weeds, improving production, and is easy to deploy because of the cheap cost…”

Section: Heterogeneous Data-based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Crop and Weed Segmentation and Fractal Dimension Estimation Using Small Training Data in Heterogeneous Data Environment

Akram,

Hong,

Kim

et al. 2024

Fractal Fract

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The segmentation of crops and weeds from camera-captured images is a demanding research area for advancing agricultural and smart farming systems. Previously, the segmentation of crops and weeds was conducted within a homogeneous data environment where training and testing data were from the same database. However, in the real-world application of advancing agricultural and smart farming systems, it is often the case of a heterogeneous data environment where a system trained with one database should be used for testing with a different database without additional training. This study pioneers the use of heterogeneous data for crop and weed segmentation, addressing the issue of degraded accuracy. Through adjusting the mean and standard deviation, we minimize the variability in pixel value and contrast, enhancing segmentation robustness. Unlike previous methods relying on extensive training data, our approach achieves real-world applicability with just one training sample for deep learning-based semantic segmentation. Moreover, we seamlessly integrated a method for estimating fractal dimensions into our system, incorporating it as an end-to-end task to provide important information on the distributional characteristics of crops and weeds. We evaluated our framework using the BoniRob dataset and the CWFID. When trained with the BoniRob dataset and tested with the CWFID, we obtained a mean intersection of union (mIoU) of 62% and an F1-score of 75.2%. Furthermore, when trained with the CWFID and tested with the BoniRob dataset, we obtained an mIoU of 63.7% and an F1-score of 74.3%. We confirmed that these values are higher than those obtained by state-of-the-art methods.

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Section: Deep Feature-based Methodsmentioning

confidence: 99%

Section: Heterogeneous Data-based Methodsmentioning

confidence: 99%

Crop and Weed Segmentation and Fractal Dimension Estimation Using Small Training Data in Heterogeneous Data Environment

Akram,

Hong,

Kim

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

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“…Because these models have accuracy levels of more than 95%, it is possible to utilize them for real-time deployments. Extensions to these models are explored in [43]- [46]. These references suggest the use of extended CNNs, deep CNNs, predictive Neural Networks, and Artificial Neural Networks for the estimation and prediction of various parameter sets.…”

Section: Review Of Existing Smart Irrigation Techniquesmentioning

confidence: 99%

Design of an efficient IoT based irrigation platform via fuzzy bioinspired multisensory analysis of on-field parameter sets

Bhoyar

Kanojia

Chourasia

et al. 2023

tjes

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Design of smart irrigation systems is a transdisciplinary work involving selecting optimal field-specific sensing devices, data capturing from these devices, their pre-processing, feature analysis, classification, IoT (Internet of Things) based actuation, and feedback operations. A wide variety of smart irrigation models, including IBM ThingSpeak, Tracxn, Farm Connect, Hydrawise, etc. have been proposed by researchers, and each of them have its own operational & deployment-specific characteristics. These models are either highly complex or have slower response time & low efficiency when applied to real-time irrigation scenarios. Moreover, these models have a higher cost, which limits their scalability levels. To overcome these issues, this text discusses design of a novel efficient IoT based irrigation platform via fuzzy bioinspired multisensory analysis of on-field parameter sets. The proposed platform uses low-cost components for sensing moisture levels, temperature levels, rain probability, NPK (Nitrogen, Phosphorous, and Potassium) levels, and soil types. These sensed values are processed via a Grey Wolf Optimizer (GWO) to identify optimum sensor types, and then a fuzzy decision layer is used for irrigation. This layer assists in the identification of efficient water flow for different crop types. The resulting growth of plants is fed-back into the model, and a Genetic Algorithm (GA) is applied to tune the fuzzy rules for better water-flow under multiple types of crops. Due to the integration of GWO with Fuzzy Logic controller, the proposed model improved yield efficiency by 8.5%, reduced computational delay by 4.9%, and reduced deployment cost compared to standard smart irrigation models. Due to these advantages, the proposed model is capable of deployment for a wide variety of real-time smart irrigation use cases.

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