Leaf / Plant Detection - Reads :)

Never stop collecting / identifying/analyzing perspectives :).

Paper #1 - Real-world plant species identification based on deep convolutional neural networks and visual attention

Key Notes

• Data augmentation method for deep learning
• Crop the image in terms with visual attention
• Considering flowers and fruits of plants are seasonal, some researchers believe that leaves are more suitable for identification
• Samples is also strict

• PlantCLEF. There are different view types and the samples are close to realistic scenarios.

• Image segmentation is carried out for generating the regions of interest (ROI) for recognition
• Comparisons between original images and final attention cropping results.

Paper #2 - Deep Learning in Agriculture: A Survey

Key Notes

• High occlusion, depth variation, and uncontrolled illumination, including high color similarity between fruit/foliage
• Rotations, cropping, scaling, transposing, mirroring
• DetectNet CNN
• Faster Region-based CNN, DetectNet CNN

Paper #3 - PlantDoc: A Dataset for Visual Plant Disease Detection

Key Notes

• The PlantVillage dataset contains images taken under controlled settings. 
• Final dataset having a total of 27 classes spanning over 13 species with 2,598 images
• model which can detect a leaf in an image and then classify it into the particular classes

Paper #4 - Leaf Classification Using Shape, Color, and Texture Features

Key Notes

• Texture, on its own does not have the capability of finding similar images, but it can be used to classify textured images from non-textured ones 
• Texture features can be extracted by using various methods. Gray-level occurrence matrices (GLCMs), Gabor Filter, and Local binary pattern (LBP) 

• Fourier descriptors, slimness ratio, roundness ratio, and dispersion are used to represent shape features

Paper #5 - Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks

Key Notes

• Feature engineering approaches such as Scale-invariant feature transform (SIFT), Bag of Word (Bow), Speeded-Up Robust Features (SURF), Gabor, Local Binary Pattern (LBP)

Take multiple images and deduce plant, flower, leaf

Paper #6 - Two-View Fine-grained Classification of Plant Species

• Three levels of abstraction: family, genus, and species
• The input of the SCNN model is the whole leaf image characterizing a global
• view in terms of problem representation. The output of this stage is a ranked list of the top-K genus candidates
• SCNN takes into account global features extracted from the entire leaf image (shape and color), while in the second view, local features based on texture and the plant veins are considered. 
• The output of the first stage is a ranked list of the top-k genus candidates.
• In the second stage, given the top-k genus candidates found in the first stage, a fine classification considering only the plant species which belong to such a genus is performed

• A coarse-to-fine classification is performed considering the hierarchical botanic taxonomy
• e. Finally, the genus (coarse classification) and species (fine classification) are combined to produce a final ranked list of the k-best hypotheses of plant species.

LifeCLEF 2015 and LeafSnap datasets.

Paper #7 - Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention

Key Notes

• We introduce a bottom-up approach based on detecting plant organs and fusing the predictions of a variable number of organ-based species classifiers
• Object detection has mostly had its use in plant analysis constrained to invasive species detection
• First, an object detector identifies and localizes plant organs, including leaves, flowers, fruit, stems, and regions with a high volume of leaves, termed “highdensity leaves” (HDL)
• These regions of interest (ROIs) are then individually passed into an organ-based species classifier
• Leaf, flower, fruit, stem, HDL

Paper #8 - COMPUTER-AIDED INTERPRETABLE FEATURES FOR LEAF IMAGE CLASSIFICATION

Key Notes

• i) Convert original image to RGB (Red-Green-Blue) image, ii) Gray scaling, iii) Gaussian smoothing, iv) Binary thresholding, v) Remove the stalk, vi) Closing holes, and vii) Resize the image.

• Gray-scaling is the process of converting an image to shades of gray from other colour spaces like RGB
• Image smoothing techniques help in smoothing images and removing intensity edges.
• Thresholding is a segmentation technique that is used to separate the foreground from its background

Paper #9 - A Review of Visual Descriptors and Classification Techniques Used in Leaf Species Identification

• Leaf Snap Dataset
• The Middle European Wood Database
• The Smithsonian Leaf Dataset
• The Austrian Federal Forest (AFF) Dataset
• Pl@ntNet

More Reads

• Flower Categorization using Deep Convolutional Neural Networks
• A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network
• Large-Scale Plant Classification with Deep Neural Networks

Keep Exploring!!!