A study on plant recognition using conventional image processing and deep learning approaches

A density map-based method for counting wheat ears

Introduction

Field wheat ear counting is an important step in wheat yield estimation, and how to solve the problem of rapid and effective wheat ear counting in a field environment to ensure the stability of food supply and provide more reliable data support for agricultural management and policy making is a key concern in the current agricultural field.

Methods

There are still some bottlenecks and challenges in solving the dense wheat counting problem with the currently available methods. To address these issues, we propose a new method based on the YOLACT framework that aims to improve the accuracy and efficiency of dense wheat counting. Replacing the pooling layer in the CBAM module with a GeM pooling layer, and then introducing the density map into the FPN, these improvements together make our method better able to cope with the challenges in dense scenarios.

Results

Experiments show our model improves wheat ear counting performance in complex backgrounds. The improved attention mechanism reduces the RMSE from 1.75 to 1.57. Based on the improved CBAM, the R2 increases from 0.9615 to 0.9798 through pixel-level density estimation, the density map mechanism accurately discerns overlapping count targets, which can provide more granular information.

Discussion

The findings demonstrate the practical potential of our framework for intelligent agriculture applications.

Deep learning for medicinal plant species classification and recognition: a systematic review

Knowledge of medicinal plant species is necessary to preserve medicinal plants and safeguard biodiversity. The classification and identification of these plants by botanist experts are complex and time-consuming activities. This systematic review's main objective is to systematically assess the prior research efforts on the applications and usage of deep learning approaches in classifying and recognizing medicinal plant species. Our objective was to pinpoint systematic reviews following the PRISMA guidelines related to the classification and recognition of medicinal plant species through the utilization of deep learning techniques. This review encompassed studies published between January 2018 and December 2022. Initially, we identified 1644 studies through title, keyword, and abstract screening. After applying our eligibility criteria, we selected 31 studies for a thorough and critical review. The main findings of this reviews are (1) the selected studies were carried out in 16 different countries, and India leads in paper contributions with 29%, followed by Indonesia and Sri Lanka. (2) A private dataset has been used in 67.7% of the studies subjected to image augmentation and preprocessing techniques. (3) In 96.7% of the studies, researchers have employed plant leaf organs, with 74% of them utilizing leaf shapes for the classification and recognition of medicinal plant species. (4) Transfer learning with the pre-trained model was used in 83.8% of the studies as a future extraction technique. (5) Convolutional Neural Network (CNN) is used by 64.5% of the paper as a deep learning classifier. (6) The lack of a globally available and public dataset need for medicinal plants indigenous to a specific country and the trustworthiness of the deep learning approach for the classification and recognition of medicinal plants is an observable research gap in this literature review. Therefore, further investigations and collaboration between different stakeholders are required to fulfilling the aforementioned research gaps.

DIMPSAR: Dataset for Indian medicinal plant species analysis and recognition

Mobile-captured images of medicinal plants are widely used in various research investigations. Machine vision-based tasks such as the identification of plant species types for intelligent imaging device applications take a significant part in it. Botanists, farmers and researchers can reliably identify medicinal plants with the help of images captured using smartphones.  Mobile captured images can be used for quality control to make sure that the right plant species are being used in pharmaceutical products. In the field of education, pictures of medicinal plants and their usage can be used to educate learners, medical professionals, and the general public. Further, various research investigations in the area of chemistry, pharmacology, the therapeutic potential of medicinal plants, images can be employed. In this paper, we contribute a dataset of Indian medicinal plant species. The dataset is collected from different regions of Karnataka and Kerala. Datasets include characteristics such as multiple resolutions, varying illuminations, varying backgrounds, and seasons in the year. The datasets consist of 5900 images of forty plant species and single leaf images of eighty plant species consisting of 6900 samples obtained from real-time conditions using smartphones. The datasets contributed would be useful to researchers to investigate on development of algorithmic models based on image processing, machine learning, and deep learning concepts to educate about medicinal plants. The dataset can be accessed by anybody, without charge, at DOI:10.17632/748f8jkphb.2, 10.17632/748f8jkphb.3.

Plant-CNN-ViT: Plant Classification with Ensemble of Convolutional Neural Networks and Vision Transformer

Plant leaf classification involves identifying and categorizing plant species based on leaf characteristics, such as patterns, shapes, textures, and veins. In recent years, research has been conducted to improve the accuracy of plant classification using machine learning techniques. This involves training models on large datasets of plant images and using them to identify different plant species. However, these models are limited by their reliance on large amounts of training data, which can be difficult to obtain for many plant species. To overcome this challenge, this paper proposes a Plant-CNN-ViT ensemble model that combines the strengths of four pre-trained models: Vision Transformer, ResNet-50, DenseNet-201, and Xception. Vision Transformer utilizes self-attention to capture dependencies and focus on important leaf features. ResNet-50 introduces residual connections, aiding in efficient training and hierarchical feature extraction. DenseNet-201 employs dense connections, facilitating information flow and capturing intricate leaf patterns. Xception uses separable convolutions, reducing the computational cost while capturing fine-grained details in leaf images. The proposed Plant-CNN-ViT was evaluated on four plant leaf datasets and achieved remarkable accuracy of 100.00%, 100.00%, 100.00%, and 99.83% on the Flavia dataset, Folio Leaf dataset, Swedish Leaf dataset, and MalayaKew Leaf dataset, respectively.

Machine learning classification of plant genotypes grown under different light conditions through the integration of multi-scale time-series data

In order to mitigate the effects of a changing climate, agriculture requires more effective evaluation, selection, and production of crop cultivars in order to accelerate genotype-to-phenotype connections and the selection of beneficial traits. Critically, plant growth and development are highly dependent on sunlight, with light energy providing plants with the energy required to photosynthesize as well as a means to directly intersect with the environment in order to develop. In plant analyses, machine learning and deep learning techniques have a proven ability to learn plant growth patterns, including detection of disease, plant stress, and growth using a variety of image data. To date, however, studies have not assessed machine learning and deep learning algorithms for their ability to differentiate a large cohort of genotypes grown under several growth conditions using time-series data automatically acquired across multiple scales (daily and developmentally). Here, we extensively evaluate a wide range of machine learning and deep learning algorithms for their ability to differentiate 17 well-characterized photoreceptor deficient genotypes differing in their light detection capabilities grown under several different light conditions. Using algorithm performance measurements of precision, recall, F1-Score, and accuracy, we find that Suport Vector Machine (SVM) maintains the greatest classification accuracy, while a combined ConvLSTM2D deep learning model produces the best genotype classification results across the different growth conditions. Our successful integration of time-series growth data across multiple scales, genotypes and growth conditions sets a new foundational baseline from which more complex plant science traits can be assessed for genotype-to-phenotype connections.

CART model to classify the drought status of diverse tomato genotypes by VPD, air temperature, and leaf-air temperature difference

Regular water management is crucial for the cultivation of tomato (Solanum lycopersicum L.). Inadequate irrigation leads to water stress and a reduction in tomato yield and quality. Therefore, it is important to develop an efficient classification method of the drought status of tomato for the timely application of irrigation. In this study, a simple classification and regression tree (CART) model that includes air temperature, vapor pressure deficit, and leaf-air temperature difference was established to classify the drought status of three tomato genotypes (i.e., cherry type 'Tainan ASVEG No. 19', large fruits breeding line '108290', and wild accession 'LA2093'). The results indicate that the proposed CART model exhibited a higher predictive sensitivity, specificity, geometric mean, and accuracy performance compared to the logistic model. In addition, the CART model was applicable not only to three tomato genotypes but across vegetative and reproductive stages. Furthermore, while the drought status was divided into low, medium, and high, the CART model provided a higher predictive performance than that of the logistic model. The results suggest that the drought status of tomato can be accurately classified by the proposed CART model. These results will provide a useful tool of the regular water management for tomato cultivation.

A machine learning approach for cross-domain plant identification using herbarium specimens

The preservation of plant specimens in herbaria has been carried out for centuries in efforts to study and confirm plant taxa. With the increasing collection of herbaria made available digitally, it is practical to use herbarium specimens for the automation of plant identification. They are also substantially more accessible and less expensive to obtain compared to field images. In fact, in remote and inaccessible habitats, field images of rare plant species are still immensely lacking. As a result, rare plant species identification is challenging due to the deficiency of training data. To address this problem, we investigate a cross-domain adaptation approach that allows knowledge transfer from a model learned from herbarium specimens to field images. We propose a model called Herbarium–Field Triplet Loss Network (HFTL network) to learn the mapping between herbarium and field domains. Specifically, the model is trained to maximize the embedding distance of different plant species and minimize the embedding distance of the same plant species given herbarium–field pairs. This paper presents the implementation and performance of the HFTL network to assess the herbarium–field similarity of plants. It corresponds to the cross-domain plant identification challenge in PlantCLEF 2020 and PlantCLEF 2021. Despite the lack of field images, our results show that the network can generalize and identify rare species. Our proposed HFTL network achieved a mean reciprocal rank score of 0.108 and 0.158 on the test set related to the species with few training field photographs in PlantCLEF 2020 and PlantCLEF 2021, respectively.

A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms

Computer vision is the science that enables computers and machines to see and perceive image content on a semantic level. It combines concepts, techniques, and ideas from various fields such as digital image processing, pattern matching, artificial intelligence, and computer graphics. A computer vision system is designed to model the human visual system on a functional basis as closely as possible. Deep learning and Convolutional Neural Networks (CNNs) in particular which are biologically inspired have significantly contributed to computer vision studies. This research develops a computer vision system that uses CNNs and handcrafted filters from Log-Gabor filters to identify medicinal plants based on their leaf textural features in an ensemble manner. The system was tested on a dataset developed from the Centre of Plant Medicine Research, Ghana (MyDataset) consisting of forty-nine (49) plant species. Using the concept of transfer learning, ten pretrained networks including Alexnet, GoogLeNet, DenseNet201, Inceptionv3, Mobilenetv2, Restnet18, Resnet50, Resnet101, vgg16, and vgg19 were used as feature extractors. The DenseNet201 architecture resulted with the best outcome of 87% accuracy and GoogLeNet with 79% preforming the worse averaged across six supervised learning algorithms. The proposed model (OTAMNet), created by fusing a Log-Gabor layer into the transition layers of the DenseNet201 architecture achieved 98% accuracy when tested on MyDataset. OTAMNet was tested on other benchmark datasets; Flavia, Swedish Leaf, MD2020, and the Folio dataset. The Flavia dataset achieved 99%, Swedish Leaf 100%, MD2020 99%, and the Folio dataset 97%. A false-positive rate of less than 0.1% was achieved in all cases.

Urban Plants Classification Using Deep-Learning Methodology: A Case Study on a New Dataset

Plant classification requires the eye of an expert in botanics when the subtle differences in stem or petals differentiate between different species. Hence, an accurate automatic plant classification might be of great assistance to a person who studies agriculture, travels, or explores rare species. This paper focuses on a specific task of urban plants classification. The possible practical application of this work is a tool which assists people, growing plants at home, to recognize new species and to provide the relevant caring instructions. Because urban species are barely covered by the benchmark datasets, these species cannot be accurately recognized by the state-of-the-art pre-trained classification models. This paper introduces a new dataset, Urban Planter, for plant species classification with 1500 images categorized into 15 categories. The dataset contains 15 urban species, which can be grown at home in any climate (mostly desert) and are barely covered by existing datasets. We performed an extensive analysis of this dataset, aimed at answering the following research questions: (1) Does the Urban Planter dataset provide enough information to train accurate deep learning models? (2) Can pre-trained classification models be successfully applied on Urban Planter, and is the pre-training on ImageNet beneficial in comparison to the pre-training on a much smaller but more relevant dataset? (3) Does two-step transfer learning further improve the classification accuracy? We report the results of experiments designed to answer these questions. In addition, we provide the link to the installation code of the alpha version and the demo video of the web app for urban plants classification based on the best evaluated model. To conclude, our contribution is three-fold: (1) We introduce a new dataset of urban plant images; (2) We report the results of an extensive case study with several state-of-the-art deep networks and different configurations for transfer learning; (3) We provide a web application based on the best evaluated model. In addition, we believe that, by extending our dataset in the future to eatable plants and assisting people to grow food at home, our research contributes to achieve the United Nations’ 2030 Agenda for Sustainable Development.

ECAENet: EfficientNet with efficient channel attention for plant species recognition

It is an essential and challenging task to accurately identify unknown plants from images without professional knowledge due to the large intra-class variance and small inter-class variance. Aiming at the problem of low accuracy and model complexity, a lightweight plant species recognition algorithm using EfficientNet with Efficient Channel Attention (ECAENet) is proposed. The proposed approach is based on EfficientNet, which used neural architecture search to gain a baseline network and uniformly scales all dimensions of depth, width, and resolution using a compound coefficient. To overcome Squeeze-and-Excitation block complexity, the proposed method replaces all the two fully-connected layers in the channel attention modules with a fast one-dimensional convolution with an adaptive kernel, which avoids dimensionality reduction and effectively learns the discriminative features. The experimental results demonstrate that our ECAENet achieves 99.56%, 99.75%, 98.40%, and 93.79% accuracy on the well-known Swedish Leaf, Flavia Leaf, Oxford Flowers, and Leafsnap datasets, respectively. In particular, our method achieves 3.6x fewer network parameters and 8.4x FLOPs than others with similar accuracy. Therefore, our method achieves better recognition performance compared to most of the existing plant recognition methods.

Classification of Plant Leaves Using New Compact Convolutional Neural Network Models

Precision crop safety relies on automated systems for detecting and classifying plants. This work proposes the detection and classification of nine species of plants of the PlantVillage dataset using the proposed developed compact convolutional neural networks and AlexNet with transfer learning. The models are trained using plant leaf data with different data augmentations. The data augmentation shows a significant improvement in classification accuracy. The proposed models are also used for the classification of 32 classes of the Flavia dataset. The proposed developed N1 model has a classification accuracy of 99.45%, N2 model has a classification accuracy of 99.65%, N3 model has a classification accuracy of 99.55%, and AlexNet has a classification accuracy of 99.73% for the PlantVillage dataset. In comparison to AlexNet, the proposed models are compact and need less training time. The proposed N1 model takes 34.58%, the proposed N2 model takes 18.25%, and the N3 model takes 20.23% less training time than AlexNet. The N1 model and N3 models are size 14.8 MB making it 92.67% compact, and the N2 model is 29.7 MB which makes it 85.29% compact as compared to AlexNet. The proposed models are giving good accuracy in classifying plant leaf, as well as diseases in tomato plant leaves.

Central Attention and a Dual Path Convolutional Neural Network in Real-World Tree Species Recognition

Identifying plants is not only the job of professionals, but also useful or essential for the plant lover and the general public. Although deep learning approaches for plant recognition are promising, driven by the success of convolutional neural networks (CNN), their performances are still far from the requirements of an in-field scenario. First, we propose a central attention concept that helps focus on the target instead of backgrounds in the image for tree species recognition. It could prevent model training from confused vision by establishing a dual path CNN deep learning framework, in which the central attention model combined with the CNN model based on InceptionV3 were employed to automatically extract the features. These two models were then learned together with a shared classification layer. Experimental results assessed the effectiveness of our proposed approach which outperformed each uni-path alone, and existing methods in the whole plant recognition system. Additionally, we created our own tree image database where each photo contained a wealth of information on the entire tree instead of an individual plant organ. Lastly, we developed a prototype system of an online/offline available tree species identification working on a consumer mobile platform that can identify the tree species not only by image recognition, but also detection and classification in real-time remotely.

Deep learning based intelligence cognitive vision drone for automatic plant diseases identification and spraying

The agriculture industry is of great importance in many countries and plays a considerable role in the national budget. Also, there is an increased interest in plantation and its effect on the environment. With vast areas suitable for farming, countries are always encouraging farmers through various programs to increase national farming production. However, the vast areas and large farms make it difficult for farmers and workers to continually monitor these broad areas to protect the plants from diseases and various weather conditions. A new concept dubbed Precision Farming has recently surfaced in which the latest technologies play an integral role in the farming process. In this paper, we propose a SMART Drone system equipped with high precision cameras, high computing power with proposed image processing methodologies, and connectivity for precision farming. The SMART system will automatically monitor vast farming areas with precision, identify infected plants, decide on the chemical and exact amount to spray. Besides, the system is connected to the cloud server for sending the images so that the cloud system can generate reports, including prediction on crop yield. The system is equipped with a user-friendly Human Computer Interface (HCI) for communication with the farm base. This multidrone system can process vast areas of farmland daily. The Image processing technique proposed in this paper is a modified ResNet architecture. The system is compared with deep CNN architecture and other machine learning based systems. The ResNet architecture achieves the highest average accuracy of 99.78% on a dataset consisting of 70,295 leaf images for 26 different diseases of 14 plants. The results obtained were compared with the CNN results applied in this paper and other similar techniques in previous literature. The comparisons indicate that the proposed ResNet architecture performs better compared to other similar techniques.

Wheat ear counting using K-means clustering segmentation and convolutional neural network

BACKGROUND

Wheat yield is influenced by the number of ears per unit area, and manual counting has traditionally been used to estimate wheat yield. To realize rapid and accurate wheat ear counting, K-means clustering was used for the automatic segmentation of wheat ear images captured by hand-held devices. The segmented data set was constructed by creating four categories of image labels: non-wheat ear, one wheat ear, two wheat ears, and three wheat ears, which was then was sent into the convolution neural network (CNN) model for training and testing to reduce the complexity of the model.

RESULTS

The recognition accuracy of non-wheat, one wheat, two wheat ears, and three wheat ears were 99.8, 97.5, 98.07, and 98.5%, respectively. The model R 2 reached 0.96, the root mean square error (RMSE) was 10.84 ears, the macro F1-score and micro F1-score both achieved 98.47%, and the best performance was observed during late grain-filling stage (R 2 = 0.99, RMSE = 3.24 ears). The model could also be applied to the UAV platform (R 2 = 0.97, RMSE = 9.47 ears).

CONCLUSIONS

The classification of segmented images as opposed to target recognition not only reduces the workload of manual annotation but also improves significantly the efficiency and accuracy of wheat ear counting, thus meeting the requirements of wheat yield estimation in the field environment.

Leaf Image Recognition Based on Bag of Features

Plants are ubiquitous in human life. Recognizing an unknown plant by its leaf image quickly is a very interesting and challenging research. With the development of image processing and pattern recognition, plant recognition based on image processing has become possible. Bag of features (BOF) is one of the most powerful models for classification, which has been used for many projects and studies. Dual-output pulse-coupled neural network (DPCNN) has shown a good ability for texture features in image processing such as image segmentation. In this paper, a method based on BOF and DPCNN (BOF_DP) is proposed for leaf classification. BOF_DP achieved satisfactory results in many leaf image datasets. As it is hard to get a satisfactory effect on the large dataset by a single feature, a method (BOF_SC) improved from bag of contour fragments is used for shape feature extraction. BOF_DP and LDA (linear discriminant analysis) algorithms are, respectively, employed for textual feature extraction and reducing the feature dimensionality. Finally, both features are used for classification by a linear support vector machine (SVM), and the proposed method obtained higher accuracy on several typical leaf datasets than existing methods.