Explore the vast range of titles available.

Plant phenology plays a vital role in assessing climate change. To monitor this, individual plants are traditionally visited and observed by trained volunteers organized in national or international networks - in Germany, for example, by the German Weather Service, DWD. However, their number of observers is continuously decreasing. In this study, we explore the feasibility of using opportunistically captured plant observations, collected via the plant identification app Flora Incognita to determine the onset of flowering and, based on that, create interpolation maps comparable to those of the DWD. Therefore, the opportunistic observations of 17 species collected in 2020 and 2021 were assigned to “Flora Incognita stations” based on location and altitude in order to mimic the network of stations forming the data basis for the interpolation conducted by the DWD. From the distribution of observations, the percentile representing onset of flowering date was calculated using a parametric bootstrapping approach and then interpolated following the same process as applied by the DWD. Our results show that for frequently observed, herbaceous and conspicuous species, the patterns of onset of flowering were similar and comparable between both data sources. We argue that a prominent flowering stage is crucial for accurately determining the onset of flowering from opportunistic plant observations, and we discuss additional factors, such as species distribution, location bias and societal events contributing to the differences among species and phenology data. In conclusion, our study demonstrates that the phenological monitoring of certain species can benefit from incorporating opportunistic plant observations. Furthermore, we highlight the potential to expand the taxonomic range of monitored species for phenological stage assessment through opportunistic plant observation data.

Hart et al. (2023) conducted a study to evaluate the accuracy of five plant identification apps based on snapshot images as used in practice by field ecologists. Their results revealed varying accuracies per app, ranging from 86.9% to 46.4%. We explore the reasons why apps failed to deliver the expected result. We re‐evaluated the image dataset using another plant identification app (Flora Incognita) in order to understand the discrepancies between ground truth and app predictions. We found that mismatches between the given and returned labels can arise due to incorrect app prediction, incorrect ground truth, multiple species per image or taxonomical inconsistencies. For some images depicting early developmental plant stages, the ground truth could not be verified, resulting in some cases where both the ground truth and the app predictions could neither be confirmed nor refuted. After accounting for these aspects, Flora Incognita reached an accuracy of 98.8% on the same image dataset. Our results highlight the untapped potential of plant ID apps, as they can be highly accurate. As shown here, one area of application could be spotting misidentifications in scientific image collections, especially if multiple apps disagree with the given label. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog.

Plant identification apps make learning about plants more convenient. This study explored the participants' perceptions of using three plant identification apps, PlantNet, PictureThis, and LeafSnap, as potential educational tools. Problems experienced, differences in perceptions, and the participants' most preferred apps were also determined. Through purposive sampling, the study engaged 162 primary pre-service teachers in the Cordillera Administrative Region (CAR), Philippines. Data were collected through a developed questionnaire and analysed quantitatively. The questionnaire was reliable with an identified single component for perception. Participants first explored and used the apps to identify local plants, thereafter, responding through an online questionnaire. Results showed that participants strongly perceived the apps as engaging, helpful in plant identification, easy to browse, providing details, effective as emerging tools, and significant for scientific literacy, except for consistency of results. There were significant differences, but with small effect sizes, indicating negligible differences in the perceptions of male and female participants regarding the apps' consistency of results and effectiveness. Weak internet connection was the primary issue affecting the apps' utilization. The pre-service teachers preferred LeafSnap over PictureThis and PlantNet. Conclusively, this study affirmed the potential of the apps for students learning about plants, further supporting their feasibility as emerging educational tools.

Due to the constraints of agricultural computing resources and the diversity of plant diseases, it is challenging to achieve the desired accuracy rate while keeping the network lightweight. In this paper, we proposed a computationally efficient deep learning architecture based on the mobile vision transformer (MobileViT) for real-time detection of plant diseases, which we called plant-based MobileViT (PMVT). Our proposed model was designed to be highly accurate and low-cost, making it suitable for deployment on mobile devices with limited resources. Specifically, we replaced the convolution block in MobileViT with an inverted residual structure that employs a 7×7 convolution kernel to effectively model long-distance dependencies between different leaves in plant disease images. Furthermore, inspired by the concept of multi-level attention in computer vision tasks, we integrated a convolutional block attention module (CBAM) into the standard ViT encoder. This integration allows the network to effectively avoid irrelevant information and focus on essential features. The PMVT network achieves reduced parameter counts compared to alternative networks on various mobile devices while maintaining high accuracy across different vision tasks. Extensive experiments on multiple agricultural datasets, including wheat, coffee, and rice, demonstrate that the proposed method outperforms the current best lightweight and heavyweight models. On the wheat dataset, PMVT achieves the highest accuracy of 93.6% using approximately 0.98 million (M) parameters. This accuracy is 1.6% higher than that of MobileNetV3. Under the same parameters, PMVT achieved an accuracy of 85.4% on the coffee dataset, surpassing SqueezeNet by 2.3%. Furthermore, out method achieved an accuracy of 93.1% on the rice dataset, surpassing MobileNetV3 by 3.4%. Additionally, we developed a plant disease diagnosis app and successfully used the trained PMVT model to identify plant disease in different scenarios.

Background Digital plant images are becoming increasingly important. First, given a large number of images deep learning algorithms can be trained to automatically identify plants. Second, structured image-based observations provide information about plant morphological characteristics. Finally in the course of digitalization, digital plant collections receive more and more interest in schools and universities. Results We developed a freely available mobile application called Flora Capture allowing users to collect series of plant images from predefined perspectives. These images, together with accompanying metadata, are transferred to a central project server where each observation is reviewed and validated by a team of botanical experts. Currently, more than 4800 plant species, naturally occurring in the Central European region, are covered by the application. More than 200,000 images, depicting more than 1700 plant species, have been collected by thousands of users since the initial app release in 2016. Conclusion Flora Capture allows experts, laymen and citizen scientists to collect a digital herbarium and share structured multi-modal observations of plants. Collected images contribute, e.g., to the training of plant identification algorithms, but also suit educational purposes. Additionally, presence records collected with each observation allow contribute to verifiable records of plant occurrences across the world.

Abstract There has been a recent explosion in development of image recognition technology and its application to automated plant identification, so it is timely to consider its potential for field botany. Nine free apps or websites for automated plant identification and suitable for use on mobile phones or tablet computers in the field were tested on a disparate set of 38 images of plants or parts of plants chosen from the higher plant flora of Britain and Ireland. There were large differences in performance with the best apps identifying >50 % of samples tested to genus or better. Although the accuracy is good for some of the top-rated apps, for any quantitative biodiversity study or for ecological surveys, there remains a need for validation by experts or against conventional floras. Nevertheless, the better-performing apps should be of great value to beginners and amateurs and may usefully stimulate interest in plant identification and nature. Potential uses of automated image recognition plant identification apps are discussed and recommendations made for their future use. There is much interest in potential uses of artificial intelligence approaches to the automated identification of unknown plants. This paper reviews and compares a number of free smartphone apps that attempt automatically to identify unknown plants from images taken in natural environments, using images of plants growing wild in Britain. These apps are continually improving but the best ones already show an outstanding success rate identifying up to three quarters of samples to family and half to species. Criteria for the selection of an app for any situation are discussed and highlight the importance of minimizing erroneous identifications.

In the Covid-19 pandemic situation, the world is looking for immunity-boosting techniques for fighting against coronavirus. Every plant is medicine in one or another way, but Ayurveda explains the uses of plant-based medicines and immunity boosters for specific requirements of the human body. To help Ayurveda, botanists are trying to identify more species of medicinal immunity-boosting plants by evaluating the characteristics of the leaf. For a normal person, detecting immunity-boosting plants is a difficult task. Deep learning networks provide highly accurate results in image processing. In the medicinal plant analysis, many leaves are like each other. So, the direct analysis of leaf images using the deep learning network causes many issues for medicinal plant identification. Hence, keeping the requirement of a method at large to help all human beings, the proposed leaf shape descriptor with the deep learning-based mobile application is developed for the identification of immunity-boosting medicinal plants using a smartphone. SDAMPI algorithm explained numerical descriptor generation for closed shapes. This mobile application achieved 96%accuracy for the 64 × 64 sized images.

Societal Impact Statement Society depends on experts able to correctly identify plants. This skill set is taught at university, classically using tools such as identification keys. The advent of artificial intelligence apps for identification, while benefiting society in many ways, poses a challenge for university education: Students may not see the need for learning skills beyond using an app. This may lead to a generation unable to verify and maintain artificial intelligence tools for plant identification. We suggest university teachers carefully adapt their courses so students are equipped with the skills to become experts proficient in the use of all tools. Summary Plant identification skills are essential for humanity. Universities train the next generation of experts who identify plants and maintain and develop the underlying taxonomic infrastructure. Apps using artificial intelligence can now identify plants with high accuracy and speed, record data and integrate it with additional information. These features make them highly attractive for a general public but also for the students we train at university. Classically used identification tools such as text‐based keys by comparison appear unnecessarily complex. We outline a risk in this development: the emergence of a generation unable to provide the very infrastructure on which artificial intelligence tools depend, and to verify their output independently. We suggest three guiding principles for critically engaging with identification apps at university: (1) Treat students as future experts. They are the ones who will build future infrastructure and need to be able to test one method with another. (2) Design exercises that cover all skill levels. Simple remembering and understanding, sufficient for the casual use of ID apps, falls short of the critical mindset we strive for in academia. (3) Emphasise primary data. Understanding that all botanical information, however much aggregated, is derived from physical specimens, is essential. Including ID apps thoughtfully in plant identification courses based on these principles can enable students to critically assess both their strengths and weaknesses. This will help ensure we continue to train experts proficient in the use of all tools for plant identification. Society depends on experts able to correctly identify plants. This skill set is taught at university, classically using tools such as identification keys. The advent of artificial intelligence apps for identification, while benefiting society in many ways, poses a challenge for university education: Students may not see the need for learning skills beyond using an app. This may lead to a generation unable to verify and maintain artificial intelligence tools for plant identification. We suggest university teachers carefully adapt their courses so students are equipped with the skills to become experts proficient in the use of all tools.

Mollugo oppositifolia Linn. is traditionally used in neurological complications. The study aimed to investigate in-vitro neuroprotective effect of the plant extracts through testing against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and β-secretase linked to Alzheimer’s disease (AD). To understand the safety aspects, the extracts were tested for CYP450 isozymes and human hepatocellular carcinoma cell (HepG2) inhibitory potential. The heavy metal contents were estimated using atomic absorption spectroscopy (AAS). Further, the antioxidant capacities as well as total phenolic content and total flavonoid content (TFC) were measured spectrophotometrically. UPLC-QTOF-MS/MS analysis was employed to identify phytometabolites present in the extract. The interactions of the ligands with the target proteins (AChE, BChE, and BACE-1) were studied using AutoDockTools 1.5.6. The results showed that M. oppositifolia extract has more selectivity towards BChE (IC 50 = 278.23 ± 1.89 μg/ml) as compared to AChE (IC 50 = 322.87 ± 2.05 μg/ml). The IC 50 value against β-secretase was 173.93 μg/ml. The extract showed a CC 50 value of 965.45 ± 3.07 μg/ml against HepG2 cells and the AAS analysis showed traces of lead 0.02 ± 0.001 which was found to be within the WHO prescribed limits. Moreover, the IC 50 values against CYP3A4 (477.03 ± 2.01 μg/ml) and CYP2D6 (249.65 ± 2.46 μg/ml) isozymes justify the safety aspects of the extract. The in silico molecular docking analysis of the target enzymes showed that the compound menthoside was found to be the most stable and showed a good docking score among all the identified metabolites. Keeping in mind the multi-targeted drug approach, the present findings suggested that M. oppositifolia extract have anti-Alzheimer’s potential.

Livestock management is challenging for resource-poor (R-P) farmers due to unavailability of quality feed, limited professional advice, and rumor-spreading about animal health condition in a herd. This research seeks to improve animal health in southern Africa by promoting sericea lespedeza (Lespedeza cuneata), a nutraceutical forage legume. An automated geospatial model for precision agriculture (PA) can identify suitable locations for its cultivation. Additionally, a novel approach of radio-frequency identifier (RFID) supported telemetry technology can track animal movement, and the analyses of data using artificial intelligence can determine sickness of small ruminants. This RFID-based system is being connected to a smartphone app (under construction) to alert farmers of potential livestock health issues in real time so they can take immediate corrective measures. An accompanying Decision Support System (DSS) site is being developed for R-P farmers to obtain all possible support on livestock production, including the designed PA and RFID-based DSS.