Plant Health Status Research Articles

Unraveling the Rhubarb (Rheum officinale Baill.) Root and Rhizosphere Microbial Communities in Response to Pathogen Exposure.

This study investigated the microbial community composition and structure in healthy and diseased rhubarb (Rheum rhabarbarum) root systems, examining both root tissue and rhizosphere environments. Alpha diversity analysis revealed significantly higher microbial abundance in the rhizosphere compared to root tissues, with notable differences between healthy and diseased plants. Principal coordinate analysis demonstrated that bacterial community composition was primarily influenced by ecological niches (47.5% variation explained), whereas fungal communities segregated based on plant health status. Network analysis revealed increased bacterial community complexity in diseased plants rhizosphere (579 nodes, 13,016 edges) compared to healthy plants (542 nodes, 8700 edges), while fungal networks showed opposite trends with significant reduction in diseased conditions (147 nodes, 30 edges vs. 205 nodes, 418 edges). Correlation analysis identified significant associations between specific microbial taxa and soil properties, with notable positive correlations between certain bacteria (Oscillospirales) and fungi (Barnettozyma, Mortierella) with soil organic matter and nutrient availability. Pathogenic taxa, including Fusarium and members of Burkholderiales, showed negative correlations with beneficial microorganisms, suggesting potential antagonistic relationships. These findings provide crucial insights into the complex interactions within the rhubarb root microbiome and their implications for plant health, contributing to our understanding of root rot disease dynamics and potential management strategies.

A multi-spectral and hyperspectral image dataset for evaluating chemical traits and the water status of avocado, olive and grape through leaf dehydration under laboratory conditions

Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of vegetation in laboratory settings, and also hold the potential of assessing vegetation of large portions of land. However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and grape trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.

Exploring Microelement Fertilization and Visible-Near-Infrared Spectroscopy for Enhanced Productivity in Capsicum annuum and Cyprinus carpio Aquaponic Systems.

This study explores the effects of varying exposure times of microelement fertilization on hydrochemical parameters, plant growth, and nutrient content in an aquaponic system cultivating Capsicum annuum L. (pepper) with Cyprinus carpio (Common carp L.). It also investigates the potential of visible-near-infrared (VIS-NIR) spectroscopy to differentiate between treated plants based on their spectral characteristics. The findings aim to enhance the understanding of microelement dynamics in aquaponics and optimize the use of VIS-NIR spectroscopy for nutrient and stress detection in crops. The effects of microelement exposure on the growth and health of Cyprinus carpio (Common carp L.) in an aquaponic system are investigated, demonstrating a 100% survival rate and optimal growth performance. The findings suggest that microelement treatments, when applied within safe limits, can enhance system productivity without compromising fish health. Concerning hydrochemical parameters, conductivity remained stable, with values ranging from 271.66 to 297.66 μS/cm, while pH and dissolved oxygen levels were within optimal ranges for aquaponic systems. Ammonia nitrogen levels decreased significantly in treated variants, suggesting improved water quality, while nitrate and orthophosphate reductions indicated an enhanced plant nutrient uptake. The findings underscore the importance of managing water chemistry to maintain a balanced and productive aquaponic system. The increase in root length observed in treatments 2 and 6 suggests that certain microelement exposure times may enhance root development, with treatment 6 showing the longest roots (58.33 cm). Despite this, treatment 2 had a lower biomass (61.2 g), indicating that root growth did not necessarily translate into increased plant weight, possibly due to energy being directed towards root development over fruit production. In contrast, treatment 6 showed both the greatest root length and the highest weight (133.4 g), suggesting a positive correlation between root development and fruit biomass. Yield data revealed that treatment 4 produced the highest yield (0.144 g), suggesting an optimal exposure time before nutrient imbalances negatively impact growth. These results highlight the complexity of microelement exposure in aquaponic systems, emphasizing the importance of fine-tuning exposure times to balance root growth, biomass, and yield for optimal plant development. The spectral characteristics of the visible-near-infrared region of pepper plants treated with microelements revealed subtle differences, particularly in the green (534-555 nm) and red edge (680-750 nm) regions. SIMCA models successfully classified control and treated plants with a misclassification rate of only 1.6%, highlighting the effectiveness of the spectral data for plant differentiation. Key wavelengths for distinguishing plant classes were 468 nm, 537 nm, 687 nm, 728 nm, and 969 nm, which were closely related to plant pigment content and nutrient status. These findings suggest that spectral analysis can be a valuable tool for the non-destructive assessment of plant health and nutrient status.

Optimizing Palm Oil Plantation Productivity Using Offline Blockchain and Drone Rover Solutions

The increasing demand for sustainable palm oil production challenges plantations to maintain efficient management and data transparency, particularly in remote areas with limited internet access. This study aims to develop and implement an offline blockchain system integrated with drone rover devices to support data collection and decision-making without internet connectivity. Drone rovers equipped with sensors and cameras are deployed to collect comprehensive data on plant health, pest detection, and environmental conditions across the plantation. The offline blockchain securely stores this data, ensuring integrity and traceability. Additionally, an AI system is utilized to process this data in real-time, enhancing the precision of monitoring plant health and fruit ripeness. Results indicate that this approach optimizes resource management, improves operational transparency, and enables accurate decision-making in palm oil plantation management. By combining offline blockchain technology with AI-driven analysis, this study provides a scalable and effective solution for sustainable agriculture in connectivity-challenged environments.

A Multifunctional Hydrogel with Multimodal Self-Powered Sensing Capability and Stable Direct Current Output for Outdoor Plant Monitoring Systems

HighlightsA simple and scalable fabricated hydrogel with multiple functionalities to realize self-sustainable outdoor monitoring solely by it for large-scale applications in precision agriculture.Stable direct-current output without requirement on stochastic or temporal environmental energies, achieving an energy density of 1.36 × 107 J m–3 with continuous operation of 56.25 days in normal outdoor environment.Self-powered noninvasive leaf relative water content monitoring and environmental sensing to evaluate plant health status with high durability and self-recoverability in severe environments.

Deciphering the dynamics and trophic mode distribution of the leaf spot-associated fungal community of eggplant (Solanum melongena L.)

The invasion of phytopathogens impacts the composition and associations of the internal microbial inhabitants. Leaf spot is one of the most devastating diseases in eggplant var. Mattu Gulla which is unique in terms of geographic indication (GI) status. Leaf spot samples (asymptomatic and symptomatic) were collected to characterize the fungal community associated with them using culture-based and next-generation ITS rRNA-based metabarcoding approaches. Both methods showed that Ascomycota and Basidiomycota were the predominant phyla in both groups. In the asymptomatic group, Didymosphaeriaceae, Pleosporaceae, Trichomeriaceae, and Capnodiaceae were the most differentially abundant families. In contrast, Phaeosphaeriaceae, Pleosporaceae, Didymellaceae, Rhynchogastremataceae, and Bulleribasidiaceae were the most differentially abundant families in the symptomatic group. At the genus level, Cladosporium was the most differentially abundant genus in the asymptomatic group. In the symptomatic group Alternaria, Remotididymella, Vishniacozyma, Bulleribasidium, Occultifur, Epicoccum, and Loratospora were the abundant genera. The pathotroph-saprotrophic mode was the most common mode identified in both groups, with an increased abundance in the symptomatic group. Seven fungal families and two genera were identified as common according to the culture-based method and NGS analysis based on ITS rRNA metabarcoding. Our study indicated that the composition of the core microbial community varies with plant health status, and a combination of culturable and next-generation ITS rRNA-based metabarcoding approaches could be a reliable option for obtaining a detailed understanding of plant-associated fungal communities.

Unveiling Potassium and Sodium Ion Dynamics in Living Plants with an In-Planta Potentiometric Microneedle Sensor.

Potassium and sodium ions (K+ and Na+) play crucial roles in influencing plant growth and health status. Unfortunately, current strategies to determine the concentrations of such ions are destructive for the plants because it is necessary to collect/extract the sap for further analysis and produce either scattered or delayed results. Here, we introduce a new potentiometric dual microneedle sensor for nondestructive, real-time, and continuous monitoring of K+ and Na+ concentrations in living plants. The developed sensors show a response time <5 s, close-to-Nernstian slope (∼55 mV dec-1), resiliency to five insertions on the stem, good repeatability (max. %RSD = 0.3%) and reversibility (max. %RSD = 3%), appropriate continuous operation for 24 h, and linear range of responses that cover expected plant physiological levels (5-50 mM for Na+ and 50-120 mM for K+). Moreover, the accuracy was successfully investigated by comparing the results provided by the microneedle sensors to those obtained by a standard reference method (e.g., ion chromatography). Finally, we demonstrate that the developed analytical device is capable of tracking K+ and Na+ transportation from the hydroponic solution to the stem within 5-10 min. This research will contribute to establishing a new generation of analytical platforms for smart agriculture offering real-time information.

Protection of Oats against Puccinia and Drechslera Fungi in Various Meteorological Conditions

Due to their multi-purpose use and, in many cases, lower requirements and financial outlays for cultivation, oats are an interesting crop. However, fungal diseases may contribute to significant declines in grain yields and quality. The aspects that may potentially influence this matter of fact include weather conditions. The aim of the study was to determine the severity of diseases caused by fungi in oat cultivation during the vegetation season. The next goal was to assess the efficacy of the selected active ingredients (a.i.) of fungicides from the chemical groups of triazoles and strobilurins in selected diseases’ control under various meteorological conditions. All of the fungicides were applied in the form of a spray treatment to reduce the severity of the diseases in the cultivation of different oat varieties. Husked and naked oat varieties were used. The health status of the oat plants was determined on the basis of a macroscopic evaluation of plants performed in accordance with the proper methodology. Field experiments were carried out under different weather conditions, which varied over the years during which the trials were conducted. Statistically significant differences were found in the reduction in infection for F and F1 leaves with D. avenae and P. coronata in comparison to the control treatment, regardless of the a.i. used. The use of a.i. tebuconazole (250 g/L), a.i. epoxiconazole (125 g/L), a.i. azoxystrobin (250 g/L) and a.i. picoxystrobin (250 g/L) enabled a reduction in the severity of oat helmintosporiosis in all years of the study for all the varieties analyzed. The efficacy was 72.4%, 74.2%, 71.5%, and 73.1%, respectively. Higher efficacy in reducing P. coronata was found in comparison with D. avenae. The obtained research results confirm the satisfactory efficacy of the above-mentioned active substances in reducing the fungi D. avenae and P. coronata.

(Invited) Carbon Nanostructures for Targeted Delivery and Health Monitoring in Plants

To meet the demand for food and bioenergy of a rapidly growing global population requires novel and convergent approaches to enhance photosynthetic organism function. Nanotechnology is enabling the development of targeted and species independent tools for advancing plant bioengineering and precision agriculture.My research group has developed carbon nanotube-based sensors capable of real-time sensing of plant signaling molecules. These nanosensors enabled the creation of plant sentinels that communicate the health status of plants to electronic devices. Near-infrared (nIR) fluorescent single-walled carbon nanotubes (SWCNTs) were designed and interfaced with plant leaves to report hydrogen peroxide (H2O2), a key signaling molecule associated with the onset of plant stress. The sensor nIR fluorescence response (>900 nm) is quenched by H2O2 with selectivity against other stress-associated signaling molecules and within the plant physiological range (10–100 H2O2 μM). In vivo remote nIR imaging of H2O2 sensors enabled optical monitoring of plant health in response to stresses including UV-B light (−11%), high light (−6%), and a pathogen-related peptide (flg22) (−10%), but not mechanical leaf wounding (<3%). The sensor’s high biocompatibility was reflected on similar leaf cell death (<5%) and photosynthetic rates to controls without SWCNT. These optical nanosensors report early signs of stress and will improve our understanding of plant stress communication, provide novel tools for precision agriculture, and optimize the use of agrochemicals in the environment.We also developed targeted carbon-based nanomaterials as tools for precise chemical delivery (carbon dots, CDs) and gene delivery platforms (SWCNTs) to plant organelles (chloroplasts) and vasculature (phloem). A biorecognition approach of coating the nanomaterials with a rationally designed chloroplast targeting peptide improved the delivery of CDs with molecular baskets (TP-β-CD) for delivery of agrochemicals and of plasmid DNA coated SWCNT (TP-pATV1-SWCNT) from 47% to 70% and from 39% to 57% of chloroplasts in leaves, respectively. Plants treated with TP-β-CD (20 mg/L) and TP-pATV1-SWCNT (2 mg/L) had a low percentage of dead cells, 6% and 8%, respectively, similar to controls without nanoparticles, and no permanent cell and chloroplast membrane damage after 5 days of exposure. Orthogonally, the surfaces of carbon dot nanocarriers were functionalized with sucrose, enabling rapid and efficient foliar delivery into the plant phloem, a vascular tissue that transports sugars, signaling molecules, and agrochemicals through the whole plant. The chemical affinity of sucrose molecules to sugar membrane transporters on the phloem cells enhances the uptake of biocompatible carbon dots with β-cyclodextrin molecular baskets (suc-β-CD) that can carry a wide range of agrochemicals. The nanoparticle fluorescence emission properties allowed detection and monitoring of rapid translocation (<40 min) in the vasculature of wheat leaves by confocal and epifluorescence microscopy. The suc-β-CDs more than doubled the delivery of chemical cargoes into the leaf vascular tissue. The sucrose coating of nanoparticles approach enables unprecedented targeted delivery to roots with ≈70% of phloem-loaded nanoparticles delivered to roots.The use of plant biorecognition molecules mediated delivery provides an approach for guiding nanocarriers with their cargoes to plant organelles, cells and tissues and whole plants and engineering safer and efficient agrochemical and biomolecule delivery tools. Communication and actuation of plants mediated by nanosensors and targeted nanostructures can turn plants into technology and lead to a more precise and sustainable agriculture with reduced environmental impact.

The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review

The potato is a key crop in addressing global hunger, and deep learning is at the core of smart agriculture. Applying deep learning (e.g., YOLO series, ResNet, CNN, LSTM, etc.) in potato production can enhance both yield and economic efficiency. Therefore, researching efficient deep learning models for potato production is of great importance. Common application areas for deep learning in the potato production chain, aimed at improving yield, include pest and disease detection and diagnosis, plant health status monitoring, yield prediction and product quality detection, irrigation strategies, fertilization management, and price forecasting. The main objective of this review is to compile the research progress of deep learning in various processes of potato production and to provide direction for future research. Specifically, this paper categorizes the applications of deep learning in potato production into four types, thereby discussing and introducing the advantages and disadvantages of deep learning in the aforementioned fields, and it discusses future research directions. This paper provides an overview of deep learning and describes its current applications in various stages of the potato production chain.

Job demands, control, social support and mental health in workers of thermal power plants and underground mines: A comparative study

This descriptive study was conducted to compare the job demands, job control, social support and mental health status of thermal power plant and underground coal mine workers. 158 workers in thermal power plant and 162 workers in underground coal mine participated in the study. The results unearthed that thermal power plant workers had 2.3 times better mental health (p < 0.001 OR = 2.3 CI = 1.50-3.74) and 3.0 times lower job demands (p < 0.001 OR = 3.0 CI = 1.91-4.92) than coal mine workers. In the study, it was determined that mental health was positively affected as job control and social support increased in both thermal power plant and underground mine workers (p < 0.05); there was no significant relationship between job demands and mental health (p > 0.05). These results indicate that underground mine workers are at higher risk in terms of mental health and job demands than thermal power plant workers.

Potential of Castanea sativa for biomonitoring As, Hg, Pb, and Tl: A focus on their distribution in plant tissues from a former mining district

Bioavailability of potentially toxic elements (PTEs) from the Earth's crust in the soil, e.g., As, Hg, Tl, and Pb, can pose a potential environmental and health risk because of human activities, especially related to mining extraction. The biomonitoring allows to detect PTE contamination through their measurement in living organisms as trees. However, the choice of which plant species and tissue to analyse is a key point to be evaluated in relation to PTE absorption and translocation. The aim of this work was to assess the As, Hg, Tl, and Pb distribution in Castanea sativa Mill. plant tissues, given its importance for both biomass and food production. The study identified two sites in the Alpi Apuane (Italy), with similar environmental conditions (e.g., elevation, exposure, forest type, and tree species) but different soil PTE levels. The topsoil was characterized, and the PTE fractions with different bioavailability were measured. The PTE concentrations were also analysed in chestnut plant tissues (leaves, bark, wood, nuts, and shells) in parallel with and evaluation of plant health status through the determination of micro and macronutrient concentrations and the leaf C and N isotope composition (δ13C or δ15N). Chestnut trees showed a good health status highlighting its suitability for Tl, As, Hg, and Pb biomonitoring, displaying a tissue-specific PTE allocation. Thallium and Hg were detected in all plant tissues at similar concentrations, As was found in leaves, wood, and nuts while Pb only in the bark. The δ15N negatively correlated with leaf Mn and Tl concentrations, suggesting possible changes in N source and/or plant metabolism due to the high contamination level and acid soil pH. Thallium in La Culla site trees was associated with its presence in the carbonate rocks but not in the topsoil, highlighting the potentiality of chestnut in providing valuable information for geochemical surveying.

Impact of Selected Plant Extracts on Winter Wheat (Triticum aestivum L.) Seedlings: Growth, Plant Health Status and Soil Activity

Impact of Selected Plant Extracts on Winter Wheat (Triticum aestivum L.) Seedlings: Growth, Plant Health Status and Soil Activity

Fusaric acid mediates the assembly of disease-suppressive rhizosphere microbiota via induced shifts in plant root exudates

The plant health status is determined by the interplay of plant-pathogen-microbiota in the rhizosphere. Here, we investigate this tripartite system focusing on the pathogen Fusarium oxysporum f. sp. lycopersici (FOL) and tomato plants as a model system. First, we explore differences in tomato genotype resistance to FOL potentially associated with the differential recruitment of plant-protective rhizosphere taxa. Second, we show the production of fusaric acid by FOL to trigger systemic changes in the rhizosphere microbiota. Specifically, we show this molecule to have opposite effects on the recruitment of rhizosphere disease-suppressive taxa in the resistant and susceptible genotypes. Last, we elucidate that FOL and fusaric acid induce changes in the tomato root exudation with direct effects on the recruitment of specific disease-suppressive taxa. Our study unravels a mechanism mediating plant rhizosphere assembly and disease suppression by integrating plant physiological responses to microbial-mediated mechanisms in the rhizosphere.

Intelligent Aeroponics System Using Machine Learning

The Intelligent Aeroponics System Using Machine Learning is a cutting-edge agricultural technology that leverages the power of artificial intelligence to enhance the cultivation of plants in a soilless environment. This system integrates an array of sensors to collect data on crucial environmental parameters, plant health, and growth conditions. A machine learning model, trained on historical data, analyzes this information to make informed decisions in real-time. The system employs data- driven algorithms to optimize the delivery of nutrients, misting intervals, lighting conditions, and environmental control parameters. It includes a user-friendly interface for remote monitoring and control, enabling users to receive timely alerts and notifications about the system's status. Additionally, computer vision-based models are used to detect early signs of pests and diseases, allowing for swift intervention. With an emphasis on energy efficiency, security, and scalability, this intelligent aeroponics system offers a sustainable approach to crop cultivation. Its continuous feedback loop and data analysis ensure crop optimization and resource conservation. The system can be adapted to various plant types and growth stages, making it a valuable tool for modern agriculture. Overall, the Intelligent Aeroponics System Using Machine Learning not only maximizes crop yields but also minimizes resource consumption, enhancing the efficiency and sustainability of agricultural practices. Keywords: aeroponic, monitoring system, automated system, Arduino, IoT, machine learning, lettuce.

Attempts to Use Hemp (Cannabis sativa L. var. sativa) Inflorescence Extract to Limit the Growth of Fungi Occurring in Agricultural Crops

The primary objective of this investigation was to assess the potential applicability of hemp (Cannabis sativa L. var. sativa) lateral inflorescence extract in mitigating the growth of fungi, including phytopathogens, on agricultural plants. The extract, comprising a blend of biologically active compounds, holds promise for integration into contemporary plant protection methodologies. The research involved a comprehensive analysis of the extract’s chemical composition, encompassing the determination of total polyphenol and flavonoid content (utilizing spectrophotometric methods), antioxidant activity (evaluated through the DPPH method employing synthetic 2,2-diphenyl-1-picrylhydrazyl radical), and cannabinoid content (analyzed using HPLC techniques). Additionally, this study employed the poisoned substrate method to gauge the impact of 5, 10, and 20% extract concentrations on the growth of various microfungi, including Alternaria alternata, Botrytis cinerea, Colletotrichum coccodes, Fusarium avenaceum, F. culmorum, F. graminearum, F. oxysporum, F. sporotrichioides, and Trichoderma koningii. The hemp extract demonstrated a substantial presence of total polyphenolic compounds, with polyphenol and flavonoid concentrations measuring 149.65 mg/mL and 1.55 mg/mL, respectively. Furthermore, the extract contained cannabinoids at a concentration of 0.12%. The most pronounced antifungal activity was observed with the 20% extract, particularly against T. koningii (62.22–84.79%), C. coccodes (61.65–81.82%), and B. cinerea (45.00–75.42%). However, the efficacy of hemp extracts exhibited large differences against Fusarium spp. (3.10–72.95%), dependent on the specific extract and fungus strain. Introduction of hemp extracts to the substrate induced a reduction in substrate pigment and a discernible color alteration in the mycelium to a lighter shade compared to the control. These findings mark the initial phase in the exploration of practical applications for plant extracts, setting the groundwork for subsequent field trials to ascertain the extract’s impact on phytotoxicity and the health status of agricultural plants.

Precision agricultural technology for advanced monitoring of maize yield under different fertilization and irrigation regimes: A case study in Eastern Hungary (Debrecen)

Precision agricultural technology for advanced monitoring of maize yield under different fertilization and irrigation regimes: A case study in Eastern Hungary (Debrecen)

Wearable sensor supports in-situ and continuous monitoring of plant health in precision agriculture era.

Plant health is intricately linked to crop quality, food security and agricultural productivity. Obtaining accurate plant health information is of paramount importance in the realm of precision agriculture. Wearable sensors offer an exceptional avenue for investigating plant health status and fundamental plant science, as they enable real-time and continuous in-situ monitoring of physiological biomarkers. However, a comprehensive overview that integrates and critically assesses wearable plant sensors across various facets, including their fundamental elements, classification, design, sensing mechanism, fabrication, characterization and application, remains elusive. In this study, we provide a meticulous description and systematic synthesis of recent research progress in wearable sensor properties, technology and their application in monitoring plant health information. This work endeavours to serve as a guiding resource for the utilization of wearable plant sensors, empowering the advancement of plant health within the precision agriculture paradigm.

A sensitive coumarin fluorescence sensor designed for isoprene detection and imaging research in plants

The release of isoprene by plants is considered to be an adaptation to the environment. Herein, a highly selective coumarin fluorescent probe (DMIC) was designed for detecting isoprene. When isoprene came into contact with the maleimide of DMIC, an electrophilic addition process took place. The powerful push-pull effect of DMIC was disrupted. Simultaneously, intramolecular charge transfer was initiated. This enabled DMIC to achieve rapid detection of isoprene within 5min. Furthermore, excellent linearity was observed in the concentration range of 1-560ppm (R2=0.996). A limit of detection is 1.6ppm. DMIC was applied to in vitro studies of plant release of liberated isoprene. By monitoring the release of isoprene from different tree species throughout the day, the dynamics of isoprene release from plants throughout the day have been successfully revealed. In addition, the release of isoprene varied considerably among different tree species. In particular, the biocompatibility of DMIC allowed for the in vivo detection of isoprene using fluorescence imaging. The results successfully revealed the dynamics of isoprene release in plants under stress. The amount of isoprene that a plant produced increased with the severity of the stress it experienced. This suggested that the level of isoprene content in plants could be used as a preliminary indicator of the physiological health status of plants. This research demonstrates great potential for clarifying signal transduction in biological systems. It provided ideas for further understanding the biology of isoprene.

The Deep Learning-Crop Platform (DL-CRoP): For Species-Level Identification and Nutrient Status of Agricultural Crops.

Precise and timely detection of a crop's nutrient requirement will play a crucial role in assuring optimum plant growth and crop yield. The present study introduces a reliable deep learning platform called "Deep Learning-Crop Platform" (DL-CRoP) for the identification of some commercially grown plants and their nutrient requirements using leaf, stem, and root images using a convolutional neural network (CNN). It extracts intrinsic feature patterns through hierarchical mapping and provides remarkable outcomes in identification tasks. The DL-CRoP platform is trained on the plant image dataset, namely, Jammu University-Botany Image Database (JU-BID), available at https://github.com/urfanbutt. The findings demonstrate implementation of DL-CRoP-cases A (uses shoot images) and B (uses leaf images) for species identification for Solanum lycopersicum (tomato), Vigna radiata (Vigna), and Zea mays (maize), and cases C (uses leaf images) and D (uses root images) for diagnosis of nitrogen deficiency in maize. The platform achieved a higher rate of accuracy at 80-20, 70-30, and 60-40 splits for all the case studies, compared with established algorithms such as random forest, K-nearest neighbor, support vector machine, AdaBoost, and naïve Bayes. It provides a higher accuracy rate in classification parameters like recall, precision, and F1 score for cases A (90.45%), B (100%), and C (93.21), while a medium-level accuracy of 68.54% for case D. To further improve the accuracy of the platform in case study C, the CNN was modified including a multi-head attention (MHA) block. It resulted in the enhancement of the accuracy of classifying the nitrogen deficiency above 95%. The platform could play an important role in evaluating the health status of crop plants along with a role in precise identification of species. It may be used as a better module for precision crop cultivation under limited nutrient conditions.