Hybrid deep learning model for density and growth rate estimation on weed image dataset.

Abstract

Agriculture research is particularly essential since crop production is a challenge for farmers in India and around the world. 37% of the crop is impacted by invasive plants (weeds). Those unwelcome plants that interbreed with cultivated crops and decrease the purity of the crops are referred to here as weeds. A total of 2100 weed images were utilized to train the DCNN model in this study, including 500 images from the original dataset and 1600 images from the Crop Weed Field Image Dataset (CWFID), which includes broadleaf, a monocot, and dicot weeds. This research has proposed proposes hybrid Convolutional Neural Network models (HCNN) which have amalgamated the feature of the SegNet and U-Net CNN model for weed image segmentation. This work uses segmentation masks to exclude background and foreground vegetation to investigate weed growth and density estimation. To boost the identification weight of the weed leaf, furthermore, it has presented four distinct modified pooling layers and reduced the pooling layer of the classic segmentation model and loss function. According to the experimental results, our proposed algorithms achieved the best accuracy of 98.95%. The evaluation of financial misfortunes and impact due to weeds in farming is a critical perspective of considering which makes a difference in formulating suitable management methodologies against weeds.

Plant phenotyping relevance

雑草画像のセグメンテーションを基盤に、雑草の成長と密度を推定する深層学習手法を開発しており、植物状態の取得・抽出が研究の中心である。

Code and data availability

The paper uses the public CWFID dataset plus a primary weed image collection, but provides no authors' data deposit, code release, model checkpoint, or supplement with availability language. CWFID is a cited external dataset, not a paper-specific asset.

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