A Deep Learning-Based Novel Approach for Weed Growth Estimation

Mishra, Abhinav; Harnal, Shilpi; Mohiuddin, Khalid; Gautam, Vinay; Nasr, Osman A.; Goyal, Nitin; Alwetaishi, Mamdooh; Singh, Aman

doi:10.32604/iasc.2022.020174

Cited by 31 publications

(7 citation statements)

References 32 publications

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“…The article presents experimental results that demonstrate that the proposed approach improves the performance of the convolutional neural network model and solves the overfitting problem, resulting in better classification accuracy of RGB images. Anand Muni Mishra et al discuss [33] the growing popularity of automating agricultural food production to detect and identify weeds in crops. Weeds compete for nutrients, space, and resources with target crops, and their growth rate is assessed in different types of soil in the rabi crop field.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

et al. 2023

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Sunflower is a crop that has many economic values and ornamental usages. However, its production can be hampered due to various diseases such as downy mildew, gray mold, and leaf scars, and it is challenging for farmers to identify disease-prone conditions with traditional approaches. Thus, a computerized model composed of vision, artificial intelligence, and machine learning is the demand of the age to detect diseases in plants efficiently. In this paper, we develop a hybrid model with transfer learning (TL) and a simple CNN using a small dataset for detecting sunflower diseases. Out of the eight models tested on the dataset of four different classes (downy mildew, gray mold, leaf scars, and fresh leaf), the VGG19 + CNN hybrid model achieves the best results in terms of precision, recall, F1-score, accuracy, Hamming loss, Matthews coefficient, Jaccard score, and Cohen’s kappa metrics. The experimental outcomes show that the proposed model provides better precision, recall, and accuracy than other approaches on the benchmark dataset.

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Section: Related Workmentioning

confidence: 99%

Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

et al. 2023

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“…In this study, a total of nine assessment criteria are utilized to analyze and compare the performance of the Hybrid-DSCNN model with different proposed models. "Precision", "Recall", "Dice coe cient", "F1-Score", "Average Precision", "Mean Average Precision", "Intersection over Union (IoU)", "Mean Intersection over Union (mIoU)" and "Accuracy" [39] are the metrics utilized for the analysis.…”

Section: Performance Metricmentioning

confidence: 99%

Comparative Analysis of Segmentation Models to Detect Leaf Diseases in Tomato Plant

Harnal

Gautam

Singh

et al. 2022

Preprint

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In agriculture around 22% of crop yield loss is due to living and non-living organisms such as biotic and abiotic stress/disease. The early-stage diagnosis of these stresses is an important issue for farmers through naked eyes. Using computer vision technologies can detect the pattern and clustering of diseases at an early stage. However, inthe last few years, computer vision-based deep learning technology is helpful for the diagnosis of biotic stress (single biotic and multi biotic) in tomato plant leaves.In this work, the PlantVillage dataset is gathered for the segmentation of object detection. The labeled, enhanced and augmented data has been used for training the model. The proposed hybrid Deep Segmentation Convolutional Neural Network (Hybrid-DSCNN)model has been segmenting the diseased objects in the tomato plant. This Hybrid-DSCNN is assembled usingU-Net and Seg-Net pre-trained models with instance segmentation for better detection of objects. The result of the proposed Hybrid DSCNN model is compared with other modified U-Net, M-SegNet, and modified U-SegNet in terms of Accuracy, Precision, Recall, Intersection over Union (IoU), and mean Intersection over Union (mIoU). The proposed model processed 1004 images in 30ns,whichis better than other compared models. The accuracy achieved using the proposed model is 98.24%,which is far better than other modified segmentation models. In this paper, the semantic segmented data has been recognized for the single and multiple leaf diseases for identification and classification.

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“…This literature has categorized three different learning techniques first is weed identification, and the second is Deep and transfer learning. Mishra et al (2022) has been discussed the different types of biennial and perennials, a monocot, and broad leaves weeds species and also described biological control methods. It has also described the morphological and texture property of common perennial weeds such as "Paspalum Distichum", "Cynodon Dactylon", "Scirput Maritimus", and "Cyperus Rotundus" in Paddy crop agriculture.…”

Section: Literature Studymentioning

confidence: 99%

“…The author has applied different CNN techniques for image data classification and compares the technique based on the performance of the model. There are a few performance parameters that have been discussed by the author in terms of Precision, Recall, F1-score, Accuracy, Absolute Error (AE), and Mean Absolute Error (MAE) [11]. X Ma.…”

Section: Literature Studymentioning

confidence: 99%

Self-Supervised Overlapped Multiple Weed and Crop Species Leaf Segmentation under Complex Light Condition

Mishra

Singh

Singh³

2022

Preprint

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Weeds are unwanted plants that compete with target crops and absorbed the required nutrients from the soil, sunlight, air, etc. The farmers are suffering from weed identification detection due to the homogeneous morphological feature of weed and crop leaves. Computer vision is a sophisticated technique which widely used for weed and crop leaves identification and detection in the agricultural field. This work has used the three different datasets such as “Deep weed”, “Crop Weed Filed Image Dataset” (CWFID), and “Leaf Segmentation Challenge” (LSC), and collected 5090 images for training the model. In this work we have used three grass species as Setariaverticillata, Digitariasanguinalis, Echinochloa crus-Galli, and three broad leaf species as Cerastiumvulgatum L.,Chenopodiumalbums, Amaranthusretroflexus in Vignamungo crops. The first dataset includes 2000, the second 1720 images, and 1370 images from the third dataset. We have proposed a deep learning segmentation model as PSPNet-U-SegNet for data classification and compared the data accuracy from existing segmentation models such as U-SegNet, and U-SegNet.Theresult has been shown the deep weed dataset has achieved98.97% data accuracy and 8.9m IoU. Our findings demonstrate that adding more datasets to the actual field picture collection improves network performance while requiring less manual annotation work.

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A Deep Learning-Based Novel Approach for Weed Growth Estimation

Cited by 31 publications

References 32 publications

Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI

Comparative Analysis of Segmentation Models to Detect Leaf Diseases in Tomato Plant

Self-Supervised Overlapped Multiple Weed and Crop Species Leaf Segmentation under Complex Light Condition

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