A comparative evaluation of convolutional neural networks, training image sizes, and deep learning optimizers for weed detection in alfalfa

Jie Yang, Muthukumar Bagavathiannan, Yundi Wang, Yong Chen, Jialin Yu

Weed Technology 2022 article cited by 30

Source Open-access PDF More in Climate & Environment

importance 4/10 ai assisted Smart Agriculture and AI precision agriculture stage: research

Problem

Farmers need automated ways to detect weeds growing among alfalfa crops so control measures can be targeted.

Why it matters

Weed competition reduces alfalfa yields and drives up weed-management costs.

What the AI did

Deep CNNs (AlexNet, GoogLeNet, VGGNet, ResNet) were trained on field images to classify weeds versus alfalfa, comparing image sizes and optimizers.

Results

200×200 pixel images gave best accuracy; AdaDelta/SGD best for AlexNet/GoogLeNet, AdaDelta best for VGGNet; overall accuracy figure truncated in abstract

Impact

Identifies practical CNN configurations for weed detection in alfalfa imagery.

Record

Venue	Weed Technology
Published	2022
Work type	article
Authors	Jie Yang, Muthukumar Bagavathiannan, Yundi Wang, Yong Chen, Jialin Yu
Citations	30 (OpenAlex, at import time)
References	67
Model or method	AlexNet, GoogLeNet, VGGNet, ResNet
Organization	not stated in source
Primary source	https://doi.org/10.1017/wet.2022.46
Open access	bronze
Keywords	weed detection, deep learning, CNN, alfalfa, optimizers, image size

Importance rating

4 / 10

real but narrower advance

Weed detection among alfalfa targets yield loss and weed-management costs for farmers; a real but narrower agricultural advance, with no scale figures stated in the abstract.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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