Search

Due to the increase in human population, the need for food crops is also increasing day by day. In each year, 10 to 30 % agricultural yield is lost due to the attack of pests and various diseases. Hence it is challenging to detect the diseased crop early to reduce the loss of crops and retain biodiversity. Several deep-learning models were proposed by researchers for the detection of plant diseases. In this work, we proposed a new energy-efficient convolution neural network architecture and compared it with two existing models, VGG19 and Inception V3. The proposed model is trained and tested by plant village, cassava, and rice data set. The performance analysis shows that the proposed model is able to detect the diseased leaf with 95.17 % accuracy for the plant village data set, 99.8 % for the rice data set, and 63 % for the cassava dataset. The comparison study shows that our proposed architecture performs better compared to the VGG19 and Inception V3 model for both the plant village data set and rice data set. It is also to be noted that for the cassava dataset, the performance of our proposed architecture is at par with the other two models. The experimental results show that the proposed model is 5 times faster than the Inception V3 model and 2 times faster than the VGG19 model on the plant village dataset. It shows the same behavior for the other two datasets also. [ABSTRACT FROM AUTHOR]

Plant disease identification is a crucial issue in agriculture, and with the advancement of deep learning techniques, early and accurate identification of plant diseases has become increasingly critical. In recent years, the rise of vision transformers has attracted significant attention from researchers in various vision-based application areas. We designed a model with an encoder–decoder architecture to efficiently classify plant diseases using a transfer learning approach, which effectively recognizes a large number of plant diseases in multiple crops. The model was tested on the "PlantVillage", "FGVC8", and "EMBRAPA" datasets, which contain leaf information from crops such as apples, soybeans, tomatoes, and potatoes. These datasets cover diseases caused by fungi, including rust, spot, and scab, as well as viral diseases such as leaf curl. The model's performance was rigorously evaluated on datasets, and the results demonstrated its high accuracy. The model achieved 99.9% accuracy on the "PlantVillage" dataset, 97.4% on the "EMBRAPA" dataset, and 91.5% on the "FGVC8" dataset, showcasing its competitiveness with other state-of-the-art models. This study provides a robust and reliable solution for plant disease classification and contributes to the advancement of precision agriculture. [ABSTRACT FROM AUTHOR]

Natural reserves, such as the Santurbán Moor in Colombia, are ecologically important but face significant threats from activities like mining and agriculture. Preserving biodiversity in these ecosystems is essential for maintaining ecological balance and promoting sustainable tourism practices. Identifying plant species in these reserves accurately is challenging due to environmental variability and species similarities, complicating conservation efforts and educational tourism promotion. This study aims to create and assess a mobile application based on deep learning, called FloraBan, to autonomously identify plant species in natural reserves, enhancing biodiversity conservation and encouraging sustainable and educational tourism practices. The application employs the EfficientNet Lite4 model, trained on a comprehensive dataset of plant images taken in various field conditions. Designed to work offline, the application is particularly useful in remote areas. The model evaluation revealed an accuracy exceeding 90% in classifying plant images. FloraBan was effective under various lighting conditions and complex backgrounds, offering detailed information about each species, including scientific name, family, and conservation status. The ability to function without internet connectivity is a significant benefit, especially in isolated regions like natural reserves. FloraBan represents a notable improvement in the field of automated plant identification, supporting botanical research and efforts to preserve biodiversity in the Santurbán Moor. Additionally, it encourages educational and responsible tourism practices, which align with sustainability goals, providing a useful tool for both tourists and conservationists. [ABSTRACT FROM AUTHOR]

Background: In the agricultural sector, the early identification of plant diseases presents a pressing challenge. Throughout the growing season, plants remain vulnerable to an array of diseases. Failure to detect these diseases at their early stages can significantly compromise the overall yield, thereby reducing profitability for farmers. To address this issue, several researchers have introduced standard methods that leverage machine learning and deep learning techniques. However, many of these methods offer limited classification accuracy and often necessitate extensive training parameter adjustments. Method: The objective of this study is to develop a new deep learning‐based technique for detecting and classifying plant diseases at earlier stages. Thus, this paper introduces a novel technique known as the deep belief network‐based enhanced kernel extreme learning machine (DBN‐EKELM) that identifies a disease automatically and performs effective classification. The initial phase involves data preprocessing to enhance quality of plant leaf images, facilitating the extraction of critical information. With the goal of achieving superior classification accuracy, this paper proposes the use of the DBN‐EKELM technique for optimal plant leaf disease detection. Given that KELM parameters are highly sensitive to minor variations, proper parameter tuning is essential and introduces a novel binary gaining sharing knowledge‐based optimization algorithm (NBGSK). Result: The efficacy of the proposed DBN‐EKELM method is evaluated by comparing its performance with other conventional methods, considering various measures like accuracy, precision, specificity, sensitivity and F‐measure. Conclusion: Experimental analyses demonstrate that the DBN‐EKELM technique achieves an impressive rate of approximately 98.2%, 97%, 98.1%, 97.4% as well as 97.8%, surpassing other standard methods. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Herbal leaves, known for their efficacy in treating a range of infectious diseases including cancer, asthma, and heart conditions, are still widely used by medical professionals. Traditionally, villagers have identified these plants visually, but given the similarity in appearance among various species, this method is prone to human error. Accurate identification of these plant species is critical for effective treatment. Hence, the development of an intelligent plant classification system is crucial to reduce the risk of misidentification and enhance treatment accuracy. This paper introduces the deep HybridNet with hybrid optimization module (DeepHybrid-OptNet) a novel deep learning framework for medicinal plant identification and classification. Merging convolutional and recurrent neural network architectures, deep HybridNet excels in extracting complex botanical features through channel-wise feature extraction modules in convolutional neural network (CNN) and feedback loop in recurrent neural network (RNN). The incorporation of a DeepHybrid-OptNet module enhances the model's learning efficiency and accuracy. Empirical results on the Mendley and folio dataset demonstrate the framework's superiority over existing methods in accuracy, precision, and recall making it a valuable asset for botany and herbal medicine research. [ABSTRACT FROM AUTHOR]

Nutrient deficiency identification in plants is crucial for precision agriculture. Over the past decade, the application of digital imagery and deep learning has been utilized to tackle challenges related to this task. Nevertheless, the constraints inherent in current models have significantly heightened the difficulty of this task. To overcome these limitations, this study proposes an ensemble transfer learning method for nutrient deficiency identification and yield-loss prediction in crops using plant-leaf images. The proposed method is evaluated through two experiments. The initial experiment canters on the classification of images according to their nutrient deficiency, while the second experiment determines the yield loss based on the severity of the deficient nutrient. The proposed ensemble transfer learning methodology achieves remarkable accuracy rates of 99% and 94% for the groundnut and rice datasets, respectively. Furthermore, the severity-based crop yield prediction method predicts yield loss of 32.5% and 28.34% for the rice and groundnut datasets, respectively. The study utilizes two different crop datasets that represent actual environmental conditions. The first dataset contains 4399 leaf images of the rice plant, while the second dataset contains 4550 leaf images of the groundnut plant. The proposed method outperforms existing models and demonstrates that digital imagery and Deep learning can play a significant role in addressing agricultural challenges within the context of precision agriculture. [ABSTRACT FROM AUTHOR]

The high performance of deep learning networks relies on large datasets and powerful computational resources. However, collecting enough diseased training samples is a daunting challenge. In addition, existing few-shot learning models tend to suffer from large size, which makes their deployment on edge devices difficult. To address these issues, this study proposes a pruning-based lightweight few-shot learning (Prune-FSL) approach, which aims to utilize a very small number of labeled samples to identify unknown classes of crop diseases and achieve lightweighting of the model. First, the disease few-shot learning model was built through a metric-based meta-learning framework to address the problem of sample scarcity. Second, a slimming pruning method was used to trim the network channels by the γ coefficients of the BN layer to achieve efficient network compression. Finally, a meta-learning pruning strategy was designed to enhance the generalization ability of the model. The experimental results show that with 80% parameter reduction, the Prune-FSL method reduces the Macs computation from 3.52 G to 0.14 G, and the model achieved an accuracy of 77.97% and 90.70% in 5-way 1-shot and 5-way 5-shot, respectively. The performance of the pruned model was also compared with other representative lightweight models, yielding a result that outperforms those of five mainstream lightweight networks, such as Shufflenet. It also achieves 18-year model performance with one-fifth the number of parameters. In addition, this study demonstrated that pruning after sparse pre-training was superior to the strategy of pruning after meta-learning, and this advantage becomes more significant as the network parameters are reduced. In addition, the experiments also showed that the performance of the model decreases as the number of ways increases and increases as the number of shots increases. Overall, this study presents a few-shot learning method for crop disease recognition for edge devices. The method not only has a lower number of parameters and higher performance but also outperforms existing related studies. It provides a feasible technical route for future small-sample disease recognition under edge device conditions. [ABSTRACT FROM AUTHOR]

The rapid evolution of digital technology and the increasing integration of artificial intelligence in agriculture have paved the way for groundbreaking solutions in plant identification. This research pioneers the development and training of a deep learning model to identify three aromatic plants—rosemary, mint, and bay leaf—using advanced computer-aided detection within the You Only Look Once (YOLO) framework. Employing the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, the study meticulously covers data understanding, preparation, modeling, evaluation, and deployment phases. The dataset, consisting of images from diverse devices and annotated with bounding boxes, was instrumental in the training process. The model's performance was evaluated using the mean average precision at a 50% intersection over union (mAP50), a metric that combines precision and recall. The results demonstrated that the model achieved a precision of 0.7 or higher for each herb, though recall values indicated potential over-detection, suggesting the need for database expansion and methodological enhancements. This research underscores the innovative potential of deep learning in aromatic plant identification and addresses both the challenges and advantages of this technique. The findings significantly advance the integration of artificial intelligence in agriculture, promoting greater efficiency and accuracy in plant identification. [ABSTRACT FROM AUTHOR]

Agriculture has undergone a remarkable transformation, transitioning from traditional methods that were used for centuries to technology-driven practices. The advent of image processing and computational intelligence has revolutionized crop production and plant health monitoring. From drones capturing detailed crop growth data to sensors meticulously measuring soil moisture levels, the possibilities are boundless. This review delves into the cutting-edge research advancements in the application of image processing and computational intelligence techniques for botanical fields, with a particular focus on plant health monitoring. First, it provides a comprehensive overview of the diverse imaging sensors employed in agriculture, including visible, near-infrared, thermal, and hyperspectral imaging. Subsequently, it carefully analyzes the advantages and limitations of each sensor type, along with illustrative examples of their utilization in plant health monitoring. The review further explores the application of machine learning and deep learning for automated plant disease identification, highlighting the critical need for standardized datasets, benchmarking protocols, and domain-specific knowledge for effective implementation. In conclusion, the review emphasizes the future challenges and trends in this rapidly evolving field. It serves as a valuable resource, providing insights into the latest trends in computer vision-based plant disease monitoring and identifying gaps that demand further attention from the scientific community. [ABSTRACT FROM AUTHOR]

Plant diseases pose a significant threat to the sustainability of the environment and global food security. With an increasing population density and the growing demand for plant-based food, the need to address the ongoing plant disease pandemic has become urgent. These issues may be resolved and a more precise and effective approach for early disease detection in smart agriculture can be provided through the use of the embedded systems, Internet of Things (IoT), cloud platforms, machine learning (ML), and deep learning (DL). This paper presents a summary of current work in this field, as well as novel ideas put forth to increase the precision and effectiveness of plant disease detection. In this survey paper, we present (a) a survey on various plant diseases, (b) a method to detect plant diseases using IoT, embedded systems, and cloud platforms that receive and process data in real-time, (c) a ML pipeline for disease identification, and (d) a method to detect plant diseases using deep learning. Framework, dataset, and hyperspectral imaging with DL models for plant disease identification (e). The analysis, challenges of plant disease and pest detection using DL, ML, embedded systems,and the IoT are described in this paper. These databases: ScienceDirect, Springer, IEEE Xplore, MDPI, Hindawi, Frontiers, and others were used in this comprehensive search. Researchers, policymakers, and other stakeholders interested in smart agriculture, plant disease detection, and sustainable food security may find this resource from this study useful. [ABSTRACT FROM AUTHOR]

In agriculture, crops are severely affected by illnesses, which reduce their production every year. The detection of plant diseases during their initial stages is critical and thus needs to be addressed. Researchers have been making significant progress in the development of automatic plant disease recognition techniques through the utilization of machine learning (ML), image processing, and deep learning (DL). This study analyses the recent advancements made by researchers in the field of ML techniques for identifying plant diseases. This study also examines various methods used by researchers to produce ML solutions, such as image preprocessing, segmentation, and feature extraction. This study highlights the challenges encountered while creating plant disease identification systems, such as small datasets, image capture conditions, and the generalizability of the models, and discusses possible solutions to cater to these problems. Still, the development of a solution that automatically detects various plant diseases for various plant species remains a big challenge. To address these challenges, there is a need to create a system that is trained on an extensive dataset that contains images of various types of diseases a plant can suffer from, and plant images should be taken at various stages of the disease's development. This study further presents an analysis of various methods used at different stages of plant disease identification. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants. In this context, the integration of attention-based deep learning models presents a promising avenue for enhancing the efficiency of stress detection, by enabling the identification of meaningful spectral channels. This study assesses the performance of deep learning models on two potato plant cultivars exposed to water-deficient conditions. It explores how various sampling strategies and biases impact the classification metrics by using a dual-sensor hyperspectral imaging systems (VNIR -Visible and Near-Infrared and SWIR—Short-Wave Infrared). Moreover, it focuses on pinpointing crucial wavelengths within the concatenated images indicative of water-deficient conditions. The proposed deep learning model yields encouraging results. In the context of binary classification, it achieved an area under the receiver operating characteristic curve (AUC-ROC—Area Under the Receiver Operating Characteristic Curve) of 0.74 (95% CI: 0.70, 0.78) and 0.64 (95% CI: 0.56, 0.69) for the KIS Krka and KIS Savinja varieties, respectively. Moreover, the corresponding F1 scores were 0.67 (95% CI: 0.64, 0.71) and 0.63 (95% CI: 0.56, 0.68). An evaluation of the performance of the datasets with deliberately introduced biases consistently demonstrated superior results in comparison to their non-biased equivalents. Notably, the ROC-AUC values exhibited significant improvements, registering a maximum increase of 10.8% for KIS Krka and 18.9% for KIS Savinja. The wavelengths of greatest significance were observed in the ranges of 475–580 nm, 660–730 nm, 940–970 nm, 1420–1510 nm, 1875–2040 nm, and 2350–2480 nm. These findings suggest that discerning between the two treatments is attainable, despite the absence of prominently manifested symptoms of drought stress in either cultivar through visual observation. The research outcomes carry significant implications for both precision agriculture and potato breeding. In precision agriculture, precise water monitoring enhances resource allocation, irrigation, yield, and loss prevention. Hyperspectral imaging holds potential to expedite drought-tolerant cultivar selection, thereby streamlining breeding for resilient potatoes adaptable to shifting climates. [ABSTRACT FROM AUTHOR]

This paper studies a deep learning-based plant detection algorithm. Based on convolution neural network theory and digital image processing technology, 9 species of planes are detected. In order to further improve the accuracy of the detection algorithm, the paper proposes to improve the quality of the images. The experimental results show that the image quality is better after applying the following three algorithms the black channel prioritization algorithm, the quadratic combination algorithm of gray world and perfect reflection, and the contrast-limited adaptive histogram equalization algorithm, which is more conducive to the detection. Then, deep learning theory is applied to classify the plants. Comparing the classification results of AlexNet and GoogLeNet backbone networks, the accuracy of the jellyfish classification task based on GoogLeNet backbone network is 96.21%, which is better than AlexNet. Finally, the Faster R-CNN algorithm is used to detect plants and its detection performance is analyzed based on the two backbone networks mentioned above. The results show that the Faster R-CNN algorithm based on GoogLeNet has better detection accuracy in the jellyfish detection task, with an average detection accuracy of 74.96%. [ABSTRACT FROM AUTHOR]

Plant diseases pose a significant threat to global agricultural productivity and food safety. Early detection and accurate identification of these diseases are essential for effective disease management strategies. Traditional plant disease identification mainly relies on manual observation and experienced expert judgement, which has the disadvantages of being time‐consuming, labour‐intensive and low efficiency. Given the above problems, this study proposes a method for identifying apple leaf diseases based on a convolutional neural network combining hybrid attention and bidirectional long short‐term memory (BiLSTM). Appropriate apple leaf disease samples were selected from multiple public data sets to form an experimental data set. Then, the data set is imported into the improved convolutional neural network for training. Based on the original ResNet18 model, a new convolutional neural network, AppleNet, is constructed by adding a hybrid attention module and modifying the classifier structure. The experimental results show that the average recognition accuracy of AppleNet is 94.66%, which is 2.47% higher than that of the ResNet18 network. In addition, the training time of the model is only slightly increased. The ablation experiment further verified the effectiveness of the model modification. Compared with other advanced models in recognition accuracy and model training time, the superiority of AppleNet is confirmed. This study verifies that deep learning has great potential and application prospects in plant disease identification and provides a new technical solution for intelligent and convenient plant disease identification. [ABSTRACT FROM AUTHOR]

Recently, computer vision and artificial intelligence are being used as enabling technologies for plant phenotyping studies, since they allow the analysis of large amounts of data gathered by the sensors. Plant phenotyping studies can be devoted to the evaluation of complex plant traits either on the aerial part of the plant as well as on the underground part, to extract meaningful information about the growth, development, tolerance, or resistance of the plant itself. All plant traits should be evaluated automatically and quantitatively measured in a non-destructive way. This paper describes a novel approach for identifying plant roots from images of the root system architecture using a convolutional neural network (CNN) that operates on small image patches calculating the probability that the center point of the patch is a root pixel. The underlying idea is that the CNN model should embed as much information as possible about the variability of the patches that can show chaotic and heterogeneous backgrounds. Results on a real dataset demonstrate the feasibility of the proposed approach, as it overcomes the current state of the art. • Root systems must be monitored to assess the growth and well-being of a plant. • State-of-the-art approaches mainly use classic ML or U-networks for segmentation. • CNNs can be used for monitoring considering patch-based information. • These models are simpler and faster, and provide better segmentation performance. [ABSTRACT FROM AUTHOR]

Medicinal plants provide immunity against diseases and can also be taken in a precautionary sense against them. It is pivotal to know the benefits of these plants against various ailments. The identification of these plants' essential properties can give a great impact on medicinal research and practice. This research focuses on identifying the cardinal properties of five plants namely- Aloe Vera, Fennel, Fenugreek, Mint, and Tulsi by using the concept of text analytic features and NLP functions. Text data on medicinal plants are extracted from the biomedical literature dataset. Text mining is used for the extraction of the implicit relations between medicinal plants and their biomedical properties. The intricate relationship between the keywords and the medicinal plants is captured using hypergraph clustering and dominating sets. The visualization of the correlation between the keywords and the plants is carried out by clustering. With an emphasis on their potential in preventative and medical care, this model lists the common characteristics and health advantages of medicinal plants. Strong clustering is indicated by the modularity score of 0.577, with five separate communities each reflecting a unique set of features. In order to facilitate future studies, these findings offer a methodical and data-driven viewpoint. [ABSTRACT FROM AUTHOR]

Tomatoes are edible berries that are utilized extensively around the world. Their scientific name is solanum lycopersicum. Many tomato varietals are primarily produced in temperate climates. Even though tomatoes are used as ingredients in food, they are botanically categorized as berries. Bacterial spot, Black mold, Gray spot, Late blight, and powdery mildew (PM) are the primary diseases that affect tomato plants. The most challenging process in this decade is also the early diagnosis of these disorders. In this study, the suggested method is applied to the assessment of disease severity and disease diagnosis in tomato plant images. There are various stages to the procedure. The tomato plant image is initially pre-processed using the Wavelet technique, and then the modified Watershed algorithm is used to segment the image using improved Double Sigmoid and Erosion (WSA-IDS&E). The segmented image is then processed in the feature extraction stage in order to extract features. The Gray Level Co-occurrence Matrix (GLCM), histogram, illness area, and pixel-based retrieved characteristics are fed into the CNN model, a deep learning method for identifying diseases. In addition, a novel training method, WUDHOA, which adjusts the CNN’s weight, is introduced in this study to improve the performance of deep learning techniques. In addition, the severity of the tomato plant illness is measured in order to improve disease identification outcomes, where this severity assessment is taken into account using a fresh evaluation. Finally, the performance of the detection outcome is compared with other existing works to prove its efficiency. The conventional techniques have the lowest specificity ratings, such as AEO = 0.8473, SSO = 0.8249, SHO = 0.8672, MFO = 0.8394, BES = 0.8962, WOA = 0.8749, and DHO = 0.8846, whereas the WUDHOA achieved a specificity of 0.9384. [ABSTRACT FROM AUTHOR]

This study introduces an online (supervised) learning method to design nonlinear auto-regressive moving average (NARMA) controllers for feedback-linearized nonlinear single-input single-output (SISO) systems. The algorithm ensures Schur stability of the overall closed-loop system and provides adaptiveness and robustness for the NARMA controllers. The first stage of the method derives, in a data-dependent way, a feedback-linearized model of the nonlinear plant by using its input and output sample pairs. The method's second stage, which constitutes the novel part of the presented study, builds up an online learning scheme for the linear auto-regressive moving average (ARMA) controller based on an already learned feedback-linearized model of the nonlinear plant. During online supervised learning, ARMA parameters of the feedback-linearized SISO plant model and the closed-loop ARMA model are computed by minimizing the plant identification and the closed-loop system tracking errors. Both errors are defined as ℓ 1 , ε , namely ε-insensitive loss functions that provide NARMA controller the robustness against noise and outliers. The proposed online learning control algorithm is applied to a rotary inverted pendulum model and to a real rotary inverted pendulum setup. The tracking performance of the developed controller is compared with those of the linear quadratic regulator and coupled sliding mode controller in terms of mean square error. [ABSTRACT FROM AUTHOR]

This study highlights the intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance in the context of plant disease identification. It emphasizes the importance of rigorous dataset evaluation and selection protocols to ensure reliable and generalizable classification outcomes. Through a comprehensive examination of publicly available plant disease datasets, focusing on their performance as measured by GLCM metrics, this research identified dataset_2 (D2), a database of leaf images, as the top performer across all GLCM analyses. These datasets were then utilized to train the DarkNet19 deep learning model, with D2 exhibiting superior performance in both GLCM analysis and DarkNet19 training (achieving about 91% testing accuracy) according to performance metrics such as accuracy, precision, recall, and F1-score. The datasets other than dataset_1 and 2 exhibited significantly low classification performance, particularly in supporting GLCM analysis. The findings underscore the need for transparency and rigor in dataset selection, particularly given the abundance of similar datasets in the literature and the growing trend of utilizing deep learning methods in future scientific research. [ABSTRACT FROM AUTHOR]

Traditional disease identification methods are time-consuming and necessitate specialized knowledge, making them unsuitable for large-scale crop production. In such circumstances, conventional methods of deep learning are impractical. Machine learning techniques, particularly few-shot learning, can help to overcome these challenges by allowing the development of accurate and efficient plant disease identification models with a small amount of training data. The study proposes a few-shot learning strategy for identifying plant diseases, which involves using a pre-trained model on the ImageNet dataset and refining it with a relatively similar type of dataset PlantCLEF2022 for plant-based features extraction. This fine-tuned model is used to extract embedding from plant leaf images to train and classify few shot scenario of plant disease identification. The classification head of the Convolutional Neural Networks (CNN) model is replaced with a Support Vector Machines (SVM) classifier to train with fewer example images in each class. Experiments are performed on popular PlantVillage dataset having 38 classes and PDD271 dataset having 271 classes. The proposed few-shot learning framework outperformed previous methods for few-shot plant disease classification, achieving an average accuracy of 88.4% at 10-shots and 75.5% at 5-shots on PlantVillage dataset. PDD271 dataset is relatively new in few-shot learning but have larger number of classes and the proposed framework provided an accuracy of 56.3% at single-shot, 67.5% for 3-shots and 74.20% for 5-shots scenario. [ABSTRACT FROM AUTHOR]

The potential applications of microCT scanning in the field of archaeobotany are only just beginning to be explored. The imaging technique can extract new archaeobotanical information from existing archaeobotanical collections as well as create new archaeobotanical assemblages within ancient ceramics and other artefact types. The technique could aid in answering archaeobotanical questions about the early histories of some of the world's most important food crops from geographical regions with amongst the poorest rates of archaeobotanical preservation and where ancient plant exploitation remains poorly understood. This paper reviews current uses of microCT imaging in the investigation of archaeobotanical questions, as well as in cognate fields of geosciences, geoarchaeology, botany and palaeobotany. The technique has to date been used in a small number of novel methodological studies to extract internal anatomical morphologies and three-dimensional quantitative data from a range of food crops, which includes sexually-propagated cereals and legumes, and asexually-propagated underground storage organs (USOs). The large three-dimensional, digital datasets produced by microCT scanning have been shown to aid in taxonomic identification of archaeobotanical specimens, as well as robustly assess domestication status. In the future, as scanning technology, computer processing power and data storage capacities continue to improve, the possible applications of microCT scanning to archaeobotanical studies will only increase with the development of machine and deep learning networks enabling the automation of analyses of large archaeobotanical assemblages. [ABSTRACT FROM AUTHOR]

Deep learning plays a crucial role in addressing the challenge of plant disease identification in the field of agriculture. Detecting diseases in plants requires extensive effort, along with a comprehensive understanding of various plant diseases and increased processing time. Balancing both speed and accuracy in predicting leaf diseases in plants can significantly improve crop production and reduce environmental damage. In this paper, we examined deseases on popular plants in agriculture. We proposed a novel model to predict crop pathology on a feature space of global-local based on transformer aggregation. Paticular, we use refined feature of different layer to correlate semantics from high-level feature and low-level feature. Besides, to capture the extended temporal scale across the entire image, we employ a transformer to discern long-range dependencies among frames. Subsequently, the enhanced features incorporating these dependencies are inputted into a classifier for preliminary crop pathology prediction. The plant village dataset and VietNam strawberry disease (VNStr) dataset were utilized for training and disease classification in the experiments. Extensive experiments show that the proposed method outperforms by 99.18% and 94.05% accuracy in plant village and VNStr, respectivly. The model after being judged was applied on Android devices and therefore is easy to use. [ABSTRACT FROM AUTHOR]

Disease severity index (DIS) is a way of calculating the percentage of infection spread across the field. The percentage of infection in each leaf has been considered at a time stamp is being calculated and based on that disease, severity of disease spread is analyzed. With the advancement in machine learning and deep learning algorithms in the field of computer vision, identification and classification of diseases is effortless. Percentage of infection in a particular leaf, disease index (DI) is calculated using image processing techniques like Otsu threshold method. With this DI and scales, grading the severity of the infection across the field can be achieved. In this paper various scales used for grading severity of infection namely Horsfall-Barratt (H-B scale) quantitative ordinal scale, Amended 20% ordinal scale, and nearest percent estimates (NPEs) in muskmelon is explored, and based on the empirical results Amended 20% ordinal scale is most efficient method of estimating the DIS is to use the midpoint of the severity scope for each class with twenty percent adjusted to ordinal scale. The results show that the density of leaves is directly proportional to spread of diseases in muskmelon plant. [ABSTRACT FROM AUTHOR]

Background: Traditional Chinese Medicinal Plants (CMPs) hold a significant and core status for the healthcare system and cultural heritage in China. It has been practiced and refined with a history of exceeding thousands of years for health-protective affection and clinical treatment in China. It plays an indispensable role in the traditional health landscape and modern medical care. It is important to accurately identify CMPs for avoiding the affected clinical safety and medication efficacy by the different processed conditions and cultivation environment confusion. Results: In this study, we utilize a self-developed device to obtain high-resolution data. Furthermore, we constructed a visual multi-varieties CMPs image dataset. Firstly, a random local data enhancement preprocessing method is proposed to enrich the feature representation for imbalanced data by random cropping and random shadowing. Then, a novel hybrid supervised pre-training network is proposed to expand the integration of global features within Masked Autoencoders (MAE) by incorporating a parallel classification branch. It can effectively enhance the feature capture capabilities by integrating global features and local details. Besides, the newly designed losses are proposed to strengthen the training efficiency and improve the learning capacity, based on reconstruction loss and classification loss. Conclusions: Extensive experiments are performed on our dataset as well as the public dataset. Experimental results demonstrate that our method achieves the best performance among the state-of-the-art methods, highlighting the advantages of efficient implementation of plant technology and having good prospects for real-world applications. [ABSTRACT FROM AUTHOR]

Plant leaf diseases have become a serious concern for the agricultural industry, yet timely diagnosis and recognition are challenging in numerous regions of the globe owing to a shortage of automated crop disease identification methods. If plant diseases are not recognized in a prompt way, food insecurity will rise, affecting the country's income. Plant disease identification is critical for successful crop prevention and control of diseases, as well as farm production management and decision-making. Plant disease detection technologies aid in finding infected plants in their early phases and also help the user in cost-effectively expanding plant disease identification system to a variety of plants. This paper's major contribution is a stacked ensemble technique based on Machine learning and Deep learning techniques. This research article also elaborates on how plant disease detection framework will be realized using novel segmentation and feature extraction strategies for extracting significant features for classification. Once the features are extracted, they are transmitted to the cloud platform to implement web enabled automated monitoring system. The proposed stacked ensemble learning is evaluated by comparing different machine learning and Deep learning techniques models utilizing precision, recall, and F_score. When compared to traditional machine learning and deep learning techniques approaches, the findings show that the proposed technique achieves about 99% accuracy. [ABSTRACT FROM AUTHOR]

Agriculture is an essential sector that plays a necessary role in the economic improvement of a country. Prediction of plant diseases at the earliest stage may result in better yield and sustainable for growing population. The conventional method necessitates highly skilled inspectors to identify the phenotypic expression of different diseases. Alternatively, biochemical technologies offer more precise means of obtaining crop disease information by analyzing susceptible rice. However, these methods are time-consuming, expensive, reliant on laboratories, and require skilled professionals, rendering them unaffordable for most farmers. The paper aims to propose a solution to prevent infection at the earliest stage for the benefit of farmers. A novel crop disease detection model deploying a deep convolutional generative adversarial network (DC-GAN) and with multidimensional feature compensation Residual Neural Network (MDFC-ResNet) and named as DC-GAN-MDFC–ResNet, which aims at fine grained disease identification system detects from three aspects, bacterial leaf blight, leaf streak and panicle blight. Initially the input data undergone preprocessing using the several processes like data improvement, data normalization, and Singular value decomposition (SVD) to reduce the negative influence that the data set has on the training of the model. When compared to traditional convolution models, the suggested DC-GAN-MDFC–ResNet architecture exhibits in terms of highest classification accuracy, Segmentation free methodology and training stability. The experiments done in this work using Plant Village dataset which show the proposed technique offering improved recognition with the rate of 95.99% accuracy and generating higher quality samples compared to other well-known deep learning models. [ABSTRACT FROM AUTHOR]

This paper presents a robust deep learning method for fruit decay detection and plant identification. By addressing the limitations of previous studies that primarily focused on model accuracy, our approach aims to provide a more comprehensive solution that considers the challenges of robustness and limited data scenarios. The proposed method achieves exceptional accuracy of 99.93%, surpassing established models. In addition to its exceptional accuracy, the proposed method highlights the significance of robustness and adaptability in limited data scenarios. The proposed model exhibits strong performance even under the challenging conditions, such as intense lighting variations and partial image obstructions. Extensive evaluations demonstrate its robust performance, generalization ability, and minimal misclassifications. The inclusion of Class Activation Maps enhances the model's capability to identify distinguishing features between fresh and rotten fruits. This research has significant implications for fruit quality control, economic loss reduction, and applications in agriculture, transportation, and scientific research. The proposed method serves as a valuable resource for fruit and plant-related industries. It offers precise adaptation to specific data, customization of the network architecture, and effective training even with limited data. Overall, this research contributes to fruit quality control, economic loss reduction, and waste minimization. [ABSTRACT FROM AUTHOR]

Plants are acknowledged as being crucial because they are the main source of human energy generation due to their nutritional, therapeutic, and other benefits. Therefore, it is necessary to increase crop productivity. One of these significant factors contributing to reduced agricultural yields is the prevalence of bacterial, fungal, and viral illnesses. Applying techniques for plant disease identification can stop and treat these diseases. So, numerous machine learning (ML) and deep learning (DL) methods were created and tested by researchers to identify plant diseases. Therefore, this study gives a detailed discussion of the various research studies conducted in plant disease detection utilizing ML and DL-based techniques. This review offers research advancements in plant disease recognition from ML to DL techniques. Additionally, many datasets about plant diseases are thoroughly examined. It also addresses the difficulties and issues with the current systems. [ABSTRACT FROM AUTHOR]

Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding species. Consequently, the identification of leaf diseases is essential in this field of agriculture. Diseases brought on by bacteria, viruses, and fungi are a significant factor in reduced crop yields. Numerous machine learning models have been applied in the identification of plant diseases, however, with the recent developments in deep learning, this field of study seems to hold huge potential for improved accuracy. This study presents an effective method that uses image processing and deep learning approaches to distinguish between healthy and infected leaves. To effectively identify leaf diseases, we employed pre-trained models based on Convolutional Neural Networks (CNNs). There are four deep neural networks approaches used in this study: Convolutional Neural Network (CNN), Inception-V3, DenseNet-121, and VGG-16. Our focus was on optimizing the hyper-parameters of these deep learning models with prior training. For the evaluation of these deep neural networks, standard evaluation measures are used, such as F1-score, recall, precision, accuracy, and Area Under Curve (AUC). The overall outcomes show the better performance of Inception-V3 with an achieved accuracy of 95.5%, as well as the performance of DenseNet-121 with an accuracy of 94.4%. VGG-16 performed well as well, with an accuracy of 93.3%, and CNN achieved an accuracy of 91.9%. [ABSTRACT FROM AUTHOR]

Tobacco is a valuable crop, but its disease identification is rarely involved in existing works. In this work, we use few-shot learning (FSL) to identify abnormalities in tobacco. FSL is a solution for the data deficiency that has been an obstacle to using deep learning. However, weak feature representation caused by limited data is still a challenging issue in FSL. The weak feature representation leads to weak generalization and troubles in crossdomain. In this work, we propose a feature representation enhancement network (FREN) that enhances the feature representation through instance embedding and task adaptation. For instance embedding, global max pooling, and global average pooling are used together for adding more features, and Gaussian-like calibration is used for normalizing the feature distribution. For task adaptation, self-attention is adopted for task contextualization. Given the absence of publicly available data on tobacco, we created a tobacco leaf abnormality dataset (TLA), which includes 16 categories, two settings, and 1,430 images in total. In experiments, we use PlantVillage, which is the benchmark dataset for plant disease identification, to validate the superiority of FREN first. Subsequently, we use the proposed method and TLA to analyze and discuss the abnormality identification of tobacco. For the multi-symptom diseases that always have low accuracy, we propose a solution by dividing the samples into subcategories created by symptom. For the 10 categories of tomato in PlantVillage, the accuracy achieves 66.04% in 5-way, 1-shot tasks. For the two settings of the tobacco leaf abnormality dataset, the accuracies were achieved at 45.5% and 56.5%. By using the multisymptom solution, the best accuracy can be lifted to 60.7% in 16-way, 1-shot tasks and achieved at 81.8% in 16-way, 10-shot tasks. The results show that our method improves the performance greatly by enhancing feature representation, especially for tasks that contain categories with high similarity. The desensitization of data when crossing domains also validates that the FREN has a strong generalization ability. [ABSTRACT FROM AUTHOR]

Powdery mildew, dead arm and vineyard downy mildew diseases are frequently seen in the vineyards in the Gediz River Basin, West Anatolia of Turkey. These diseases can be detected early using artificial intelligence (AI)–based systems that can contribute to crop yields and also reduce the labour of the farmer and the amount of pesticides used. This article presents a dataset – namely, Hermos – for use in such AI-based systems. Hermos contains four classes of grape leaf images: leaves with powdery mildew, leaves with dead arm, leaves with downy mildew and healthy leaves. We have currently 492 images and 13,913 labels in the dataset. We have published Hermos in the Linked Open Data (LOD) cloud in order to make it easier for consumers to access, process and manipulate the data. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are of significance in tuning and buffering gene expression. Despite abundant analysis tools that have been developed in the last two decades, plant miRNA identification from next-generation sequencing (NGS) data remains challenging. Here, we show that we can train a convolutional neural network to accurately identify plant miRNAs from NGS data. Based on our methods, we also present a user-friendly pure Java-based software package called Small RNA-related Intelligent and Convenient Analysis Tools (SRICATs). SRICATs encompasses all the necessary steps for plant miRNA analysis. Our results indicate that SRICATs outperforms currently popular software tools on the test data from five plant species. For non-commercial users, SRICATs is freely available at https://sourceforge.net/projects/sricats. [ABSTRACT FROM AUTHOR]

In indigenous communities, ethno medicine has played a prominent role in healing for centuries and provides valuable insight into the use of traditional medicinal plants. Using local plant leaves as therapeutic agents, this research explores the importance of traditional Tamil medicine. Traditional healers and practitioners can benefit from technological advances and collaborate with modern healthcare systems through these advances. Based on deep learning models, a novel approach for recognizing leaves of traditional Tamil medicinal plants is presented. Pharmaceutical companies are increasingly paying attention to plants that contain medicinal factors that have fewer side effects during treatment. By identifying plants with medicinal factors and identifying their medicinal uses, this work addresses the identification of plants with medicinal factors. To classify medicinal plants and their uses, we will use deep learning models and image processing techniques to identify Tamil Traditional Medical Plant (TTMP). The input dataset for this work includes 18 different traditional Tamil plant leaves such as Azadirachta Indica (Neem), Ocimum Tenuiflorum (Tulsi) and Trigonella Foenum-graecum (Fenugreek). Pre-processing steps are performed on the input plant image, including RBZR Augmentation, noise removal and grayscale conversion, to detect the image area that is important for plant type identification. To identify the core area of the plant, this work using the HGAW Segmentation algorithm. Deep learning model is trained with segmented plants and their labels as inputs. The aim is to digitize and make accessible ancestral medical knowledge by incorporating deep learning-based leaf recognition with 96.71% accuracy for the proposed EEXR model. [ABSTRACT FROM AUTHOR]

Deep neural networks have demonstrated outstanding performances in agriculture production. Agriculture production is one of the most important sectors because it has a direct impact on the economy and social life of any society. Plant disease identification is a big challenge for agriculture production, for which we need a fast and accurate technique to identify plant disease. With the recent advancement in deep learning, we can develop a robust and accurate system. This research investigated the use of deep learning for accurate and fast tomato plant disease identification. In this research, we have used individual and merged datasets of tomato plants with 10 diseases (including healthy plants). The main aim of this work is to check the accuracy of the existing convolutional neural network models such as Visual Geometry Group, Residual Net, and DenseNet on tomato plant disease detection and then design a custom deep neural network model to give the best accuracy in case of the tomato plant. We have trained and tested our models with datasets containing over 18,000 and 25,000 images with 10 classes. We achieved over 99% accuracy with our custom model. This high accuracy was achieved with less training time and lower computational cost compared to other CNNs. This research demonstrates the potential of deep learning for efficient and accurate tomato plant disease detection, which can benefit farmers and contribute to improved agricultural production. The custom model's efficient performance makes it promising for practical implementation in real-world agricultural settings. [ABSTRACT FROM AUTHOR]

In the world economy, agriculture is an essential part among individuals to earn money. But, the farmers face more obstacles because more diseases naturally affect the health of the plant. Hence, to overcome this limitation, automatic plant disease identification techniques are essential to monitor the growth as well as disease-affected plants and leaves. In most plants, symptoms related to plant disease can be identified by considering the leaves. Moreover, plant disease identification became more essential with deep learning architecture. Hence, it is important to enhance the solution for resource-constrained portable devices like smartphones, but it become a complicated issue as the deeply structured methods utilize extensive resources for the analysis. Hence, this paper aims to develop an automated plant disease detection framework with an advanced set of plant features. The plant images are taken from online sources. The acquired plant images undergo image enhancement using the contrast enhancement process. The enhanced images are fed in the feature retrieval phase 1, where the segmentation takes place using the Centre Optimized K-means clustering (CO-KMC) with the Modified Random Variable-based Black Widow Optimization (MRV-BWO). Later, the segmented images obtain the colour, shape, and texture features and they are considered as the first set of features. The second set of features is retrieved from the enhanced images using Multi-scale Dilated Attention CNN (MSDA-CNN). These two features are fused by the weighted feature fusion process using the developed MRV-BWO. Then, the plant diseases are detected using the Hybridized Deep Neural Network with Recurrent Neural Network (HDNN-RNN), which comprises RNN and DNN models. The parameter optimization takes place in the hybrid HDNN-RNN for improving the detection efficiency using MRV-BWO. From the verification of the findings, the recommended model obtains a 96.47 precision and accuracy rate. Therefore, the simulation results of the offered model reveal that it achieves enriched efficacy when comparing other baseline approaches. [ABSTRACT FROM AUTHOR]

Accurate identification of plant diseases is a critical task in agricultural production. The existing deep learning crop disease recognition methods require a large number of labeled images for training, limiting the implementation of large-scale detection. To overcome this limitation, this study explores the application of self-supervised learning (SSL) in plant disease recognition. We propose a new model that combines a masked autoencoder (MAE) and a convolutional block attention module (CBAM) to alleviate the harsh requirements of large amounts of labeled data. The performance of the model was validated on the CCMT dataset and our collected dataset. The results show that the improved model achieves an accuracy of 95.35% and 99.61%, recall of 96.2% and 98.51%, and F1 values of 95.52% and 98.62% on the CCMT dataset and our collected dataset, respectively. Compared with ResNet50, ViT, and MAE, the accuracies on the CCMT dataset improved by 1.2%, 0.7%, and 0.8%, respectively, and the accuracy of our collected dataset improved by 1.3%, 1.6%, and 0.6%, respectively. Through experiments on 21 leaf diseases (early blight, late blight, leaf blight, leaf spot, etc.) of five crops, namely, potato, maize, tomato, cashew, and cassava, our model achieved accurate and rapid detection of plant disease categories. This study provides a reference for research work and engineering applications in crop disease detection. [ABSTRACT FROM AUTHOR]

• The state-of-the-art research on artificial intelligence assisted visual inspection systems for CH has been reviewed. • Several case studies on AI-applications to assists manual visual inspections of CH are highlighted. • This review is intended to broaden future studies and developments in this field. • A great potential exists in this field of AI-assisted visual inspections combining drones and IoT technologies. Applying computer science techniques such as artificial intelligence (AI), deep learning (DL), and computer vision (CV) on digital image data can help monitor and preserve cultural heritage (CH) sites. Defects such as weathering, removal of mortar, joint damage, discoloration, erosion, surface cracks, vegetation, seepage, and vandalism and their propagation with time adversely affect the structural health of CH sites. Several studies have reported damage detection in concrete and bridge structures using AI techniques. However, few studies have quantified defects in CH structures using the AI paradigm, and limited case studies exist for their applications. Hence, the application of AI-assisted visual inspections for CH sites needs to be explored. AI-assisted digital inspections assist inspection professionals and increase confidence levels in the damage assessment of CH buildings. This review summarizes the damage assessment techniques using image processing techniques, focusing mainly on DL techniques applied for CH conservation. Several case study applications of CH buildings are presented where AI can assist in traditional visual inspections. [ABSTRACT FROM AUTHOR]

Natural reserves, such as the Santurbán Moor in Colombia, are ecologically important but face significant threats from activities like mining and agriculture. Preserving biodiversity in these ecosystems is essential for maintaining ecological balance and promoting sustainable tourism practices. Identifying plant species in these reserves accurately is challenging due to environmental variability and species similarities, complicating conservation efforts and educational tourism promotion. This study aims to create and assess a mobile application based on deep learning, called FloraBan, to autonomously identify plant species in natural reserves, enhancing biodiversity conservation and encouraging sustainable and educational tourism practices. The application employs the EfficientNet Lite4 model, trained on a comprehensive dataset of plant images taken in various field conditions. Designed to work offline, the application is particularly useful in remote areas. The model evaluation revealed an accuracy exceeding 90% in classifying plant images. FloraBan was effective under various lighting conditions and complex backgrounds, offering detailed information about each species, including scientific name, family, and conservation status. The ability to function without internet connectivity is a significant benefit, especially in isolated regions like natural reserves. FloraBan represents a notable improvement in the field of automated plant identification, supporting botanical research and efforts to preserve biodiversity in the Santurbán Moor. Additionally, it encourages educational and responsible tourism practices, which align with sustainability goals, providing a useful tool for both tourists and conservationists.

Over the years, scientists have discovered bioactive chemicals in many of the plants that have been traditionally utilized as medicinal medicines. However, identifying plant species based on their physical characteristics can be difficult, and misidentification can have severe consequences, such as the use of the incorrect plant as a medicine. With the advent of machine learning techniques such as deep learning and federated learning, it is now possible to develop automated systems for the precise image-based classification of medicinal plants. Nevertheless, medicinal plant classification using deep learning techniques typically requires a large amount of data, which can be challenging to acquire and manage due to privacy concerns, data ownership, and geographic reasons. Federated learning provides a solution to this issue by enabling the training of a shared model on multiple devices without requiring centralized data storage. In this work, we assess and optimize the federared learning framework using two federated learning approaches, FedAvg and FedProx, and four state-of-the-art deep learning networks for the job of categorizing medicinal plants by distributing the original training set into two forms, IID and Non-IID. Ultimately, the accuracy of the optimal federated learning system is improved by 5.65% and 14.84% over the baseline on IID data and Non-IID data, respectively. Furthermore, the study brings up a new difficult arena for the task of classifying medicinal plants using Non-IID training data. [ABSTRACT FROM AUTHOR]

The use of deep learning models for plant identification has garnered significant attention in the research community and has yielded promising results. In this study, we evaluate the performance of four state-of-the-art pre-trained deep learning models, namely EfficientNetBO, EfficientNetV2-S, Vision Transformer (ViT), and Bidirectional Encoder Image Transformer (BEiT), on the VNPlant-200 dataset, a complex dataset that comprises various species of medicinal plants captured in natural settings. Our results show that BEiT achieved the highest accuracy of 99.14%, outperforming the other models evaluated on this benchmark. These findings prove the effectiveness of these models in plant recognition tasks, particularly in the context of medicinal plants. [ABSTRACT FROM AUTHOR]

Invading pests and diseases always degrade the quality and quantity of plants. Early and accurate identification of plant diseases is critical for plant health and growth. This work proposes a smartphone-based solution using a Vision Transformer (ViT) model for identifying healthy plants and unhealthy plants with diseases. The collected dataset of tomato leaves was used to collectively train Vision Transformer and Inception V3-based deep learning (DL) models to differentiate healthy and diseased plants. These models detected 10 different tomato disease classes from the dataset containing 10,010 images. The performance of the two DL models was compared. This work also presents a smartphone-based application (Android App) using a ViT-based model, which works on the basis of the self-attention mechanism and yielded a better performance (90.99% testing) than Inception V3 in our experimentation. The proposed ViT-SmartAgri is promising and can be implemented on a colossal scale for smart agriculture, thus inspiring future work in this area. [ABSTRACT FROM AUTHOR]

Invasive plant species pose significant biodiversity and ecosystem threats. Real-time identification of invasive plants is a crucial prerequisite for early and timely prevention. While deep learning has shown promising results in plant recognition, the use of deep learning models often involve a large number of parameters and high data requirements for training. Unfortunately, the available data for various invasive plant species are often limited. To address this challenge, this study proposes a lightweight deep learning model called IPMCNet for the identification of multiple invasive plant species. IPMCNet attains high recognition accuracy even with limited data and exhibits strong generalizability. Simultaneously, by employing depth-wise separable convolutional kernels, splitting channels, and eliminating fully connected layer, the model's parameter count is lower than that of some existing lightweight models. Additionally, the study explores the impact of different loss functions, and the insertion of various attention modules on the model's accuracy. The experimental results reveal that, compared with eight other existing neural network models, IPMCNet achieves the highest classification accuracy of 94.52%. Furthermore, the findings suggest that focal loss is the most effective loss function. The performance of the six attention modules is suboptimal, and their insertion leads to a decrease in model accuracy. [ABSTRACT FROM AUTHOR]

The identification of different plant genotypes, particularly during various developmental stages, is challenging due to the high genetic diversity of agricultural products. In this study, the efficacy of visible and infrared leaf spectroscopy in identifying five wheat genotypes was assessed across three main growth stages: leaf opening, tillering, and flowering. Soft independent modelling of class analogy (SIMCA) and artificial neural networks (ANN) were employed to develop growth-stage dependent and independent classification models based on spectral reflection. To handle the large amount of spectral data generated, a deep learning technique was first used to extract advanced features from the spectral data using the stacked autoencoder (SAE) approach. The results revealed that the tillering stage yielded the highest accuracy in predicting wheat genotypes when using the linear SIMCA approach. Furthermore, employing deep learning features extracted by SAE networks in the ANN classifier resulted in superior performance compared to the SIMCA classifier. During the leaf opening and tillering stages, the ANN classifier achieved perfect classification, with a weighted F1-score of 100 %, while during the flowering stage, it achieved a weighted F1-score of 98.02 %. In the combined dataset, the ANN classifier demonstrated impressive performance, with a weighted F1-score of 95.98 %. These findings suggest that in-field leaf spectroscopy techniques using deep learning feature extraction methods can be a suitable approach for identifying wheat plant genotypes during different growth stages. • In-field Vis-NIR leaf spectroscopy was examined for genotyping of wheat plants. • Growth-stage dependent and independent classification models were extracted. • Tillering stage was the most accurate in predicting wheat genotypes. • Better classifications were attained by ANN classifier fed by deep learning features. [ABSTRACT FROM AUTHOR]

Ethiopia is renowned for its rich biodiversity, supporting a diverse variety of medicinal plants with significant potential for therapeutic applications. In regions where modern healthcare facilities are scarce, traditional medicine emerges as a cost-effective and culturally aligned primary healthcare solution in developing countries. In Ethiopia, the majority of the population, around 80%, and for a significant proportion of their livestock, approximately 90% continue to prefer traditional medicine as their primary healthcare option. Nevertheless, the precise identification of specific plant parts and their associated uses has posed a formidable challenge due to the intricate nature of traditional healing practices. To address this challenge, we employed a majority based ensemble deep learning approach to identify medicinal plant parts and uses of Ethiopian indigenous medicinal plant species. The primary objective of this research is to achieve the precise identification of the parts and uses of Ethiopian medicinal plant species. To design our proposed model, EfficientNetB0, EfficientNetB2, and EfficientNetB4 were used as benchmark models and applied as a majority vote-based ensemble technique. This research underscores the potential of ensemble deep learning and transfer learning methodologies to accurately identify the parts and uses of Ethiopian indigenous medicinal plant species. Notably, our proposed EfficientNet-based ensemble deep learning approach demonstrated remarkable accuracy, achieving a significant test and validation accuracy of 99.96%. Future endeavors will prioritize expanding the dataset, refining feature-extraction techniques, and creating user-friendly interfaces to overcome current dataset limitations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]