Agricultural Applications Research Articles

Molecular cloning and characterization of a brassinosteriod biosynthesis-related gene PtoCYP90D1 from Populus tomentosa

Brassinosteroids (BRs), one of the major classes of phytohormones are essential for various processes of plant growth, development, and adaptations to biotic and abiotic stresses. In Arabidopsis, AtCYP90D1 acts as a bifunctional cytochrome P450 monooxygenase, catalyzing C-23 hydroxylation in the brassinolide biosynthetic pathway. The present study reports the functional characterizations of PtoCYP90D1, one of the AtCYP90D1 homologous genes from Populus tomentosa. The qRT-PCR analysis showed that PtoCYP90D1 was highly expressed in roots and old leaves. Overexpression of PtoCYP90D1 (PtoCYP90D1-OE) in poplar promoted growth and biomass yield, as well as increased xylem area and cell layers. Transgenic plants exhibited a significant increase in plant height and stem diameter as compared to the wild type. In contrast, the CRISPR/Cas9-generated mutation of PtoCYP90D1 (PtoCYP90D1-KO) resulted in significantly decreased biomass production in transgenic plants. Further studies revealed that cell wall components increased significantly in PtoCYP90D1-OE lines but not in PtoCYP90D1-KO lines, as compared to wild-type plants. Overall, the findings indicate a positive role of PtoCYP90D1 in improving growth rate and elevating biomass production in poplar, which will have positive implications for its versatile industrial or agricultural applications.

Monitoring of Ammonium and Nitrate Ions in Soil Using Ion-Sensitive Potentiometric Microsensors

Focusing on the ChemFET (chemical field-effect transistor) technology, the development of a multi-microsensor platform for soil analysis is described in this work. Thus, different FET-based microdevices (i.e., pH-ChemFET pNH4-ISFET and pNO3-ISFET sensors) were realized with the aim of monitoring nitrogen-based ionic species in soil, evidencing quasi-Nernstian detection properties (>50 mV/decade) in appropriate concentration ranges for agricultural applications. Using a specific test bench adapted to important earth samples (mass: ~50 kg), first experiments were done in a lab, mimicking rainy periods as well as nitrogen-based fertilizer inputs. By monitoring pH, pNH4, and pNO3 in an acidic (pH ≈ 4.7) clay-silt soil matrix, different processes associated to the nitrogen cycle were characterized over a fortnight, demonstrating comprehensive results for ammonium nitrate NH4NO3 inputs at different concentrations, water additions, nitrification phenomena, and ammonium NH4+ ion trapping. Even if the ChemFET-based measurement system should be improved according to the soil(electrolyte)/sensor contact, such realizations and results show the ChemFET technology potentials for long-term analysis in soil, paving the way for future “in situ” approaches in the frame of modern farming.

Ionically crosslinked Sodium alginate – Galactoxyloglucan hydrogel beads as a controlled release system for sustainable urea delivery

The prolonged usage of chemical fertilizers often causes deleterious effects on the environment, and it remains an alarming issue in agricultural practices. Smart delivery of fertilizers achieved by hydrogels proved their efficiency in reducing risk in action. In this study, we investigated the optimization of urea-encapsulated hydrogel formulations and evaluated their physical and chemical attributes for agricultural applications. The proposed hydrogel formulation consists of ionically crosslinked two promising plant biopolymers (galactoxyloglucan and sodium alginate) which are specifically developed for the controlled release of urea fertilizer. The FT-IR analysis of the hydrogel beads confirmed the presence of biopolymer functional groups and urea. XRD analysis indicated the amorphous nature of the biopolymers. TGA revealed a thermal stability of up to 350°C. SEM images showed the porous structure of the hydrogel beads. To investigate the controlled release of urea, two kinetic models were employed: Korsmeyer-Peppas and Peppas-Sahlin. The results indicated a controlled release mechanism. The hydrogel beads demonstrated excellent water retention properties, maintaining soil moisture for up to 16 days. Additionally, degradability results demonstrated that in the soil environment, the hydrogel beads were degraded completely in 21 days. Therefore, we are confident that the formulated hydrogel bead exhibits eco-friendly and bio-compatible characteristics, which potentially mitigate the risks associated with urea fertilizers.

Synthesis and mechanism of Mg/Al layered double oxides-silica nanocomposites for sustainable multi-nutrient delivery in agricultural applications

Potassium (K), urea (N), phosphate (P), and selenite (Se) are widely used in modern agriculture for improvement of crop yield and quality. However, traditional fertilizer suffers from poor fertilizer utilization efficiency and noncontrollable slow-release behavior. To improve nutrient utilization, we developed a layered double oxides-silica (LDO@Si) based on calcined Mg/Al layered double hydroxide-silica nanocomposites (LDH@Si), for slow release of K, N, P, and Se to crops. In this study, SEM, XRD, FT-IR, XPS, BET, and TGA were employed to analyze the structure, morphology, and microstructures of the samples. Results show that LDH@Si successfully transforms into LDO@Si after calcination, where LDO is mainly connected to silica via M-O-Si bonds. Furthermore, after K, P, and Se loading, the LDH@Si structure was successfully restored, indicating that phosphate and selenite ions have been effectively embedded in the inner layer of LDH. K ions are firmly fixed to the material surface via M-O-K bonds. After urea introducing, the pore structure of the material was completely filled, and excess urea formed a urea layer on the surface of the material. LDO@Si can continuously release nutrients into water through diffusion and LDO@Si dissolution for up to 240 h. Compared with chemical fertilizers, LDO@Si based slow-release fertilizer (CRSF2) significantly improves plant fresh weight, dry weight and chlorophyll content, increasing them by 13.91 %–23.13 %, 18.20 %–34.40 % and 2.24 %–14.81 %, respectively. Furthermore, a modest increase in the levels of nutrients N, P, and K is observed, while the Se concentration in plants treated with CRSF2 demonstrates significant enhancements of 9.57 %, 72.49 %, and 50.97 % compared to control treatments. These results illustrate the potential agricultural application of LDO@Si as a slow-release fertilizer for improving crop yields while minimizing the required amount of fertilizer.

Green synthesis of selenium nanoparticles fromCassia javanica flowers extract and their medical and agricultural applications.

Nanostructured materials are advantageous within numerous fields of medicine owing to their intriguing qualities, which include their size, reactive surface, bioactivity, potential for modification, and optical characteristics. Cassia javanica flower extract was used as a chelating agent in an environmentally friendly process to create SeNPs FTIR, XRD, and TEM, SAED were utilized to analyze and characterize the synthesized. The findings showed that the MIC of Se NPs against B. subtilis and S. aureus was 500µg/ml. Conversely, the MIC for P. aeruginosa, E. coli, and C. albicans were 125, 250, and 62.5µg/ml, respectively. Hence, SeNPs considerably reduced the activity; the inhibition peaked at 77.6% at 250µg/ml to reach 49.04% at 7.8µg/ml. Which showed the greatest suppression of MRSA biofilm formation without affecting bacterial growth. SeNPs showed an intriguing antioxidant capacity, achieving an IC50 of 53.34µg/ml. This study looked how soaking seeds before sowing them with Se NPs at 50, 100, and 200 ppm affected the plants' development in different parameters, as well as their yield of Vicia faba L. The growth conditions were effectively increased by soaking application of various quantities of Se NPs. The highest values of dry weight/pod (g), number of seeds/plant, weight of 100 seeds (g), and number of pods/plant were caused by high concentrations of Se NPs, by 28.43, 89.60, 18.20, and 94.11%, respectively.

Smart agriculture as source of sustainability in intensive greenhouse production

Abstract The article addresses the interaction between smart agriculture and sustainability within the specific context of intensive greenhouse agriculture in southeast Spain. Combining a literature review and expert interviews, the methodology is based on a qualitative data analysis, whose main goal is to gain a comprehensive understanding of the phenomenon under study. The results identify three key areas of technology that strongly influence this type of agriculture. The first relates to soil, water savings, sensor utilization, and environmental improvement, while the second encompasses robot use, labor reduction, resulting cost improvement, and economic sustainability. Finally, the third group, albeit of lesser importance, links artificial intelligence with ethical issues such as data control, usage, and technological dependence. In summary, the application of smart agriculture is projected to have positive impacts on economic and environmental sustainability, relegating the social dimension to a more distant level. This imbalance suggests that social and ethical aspects could be subordinated to more immediate benefits. The results also suggest that the need for future investments could create polarization in the sector. Not all farmers and businesses can afford these investments, leading to progressive deterioration and even abandonment of agricultural activities in some cases.

Utilising brewer’s spent grain as a cost-effective feed formula for sustainable house cricket (Acheta domesticus) rearing

Abstract This study investigates the potential of brewer’s spent grain (BSG) as a cost-effective alternative to soybean meal in the diets of house crickets (Acheta domesticus) to enhance sustainable cricket rearing. BSG, a by-product of the brewing industry, was chosen due to its local availability and high protein content (38%). Here, the impacts of varying levels of BSG substitution (0%, 10%, 20% and 30%) on the growth performance, nutrient utilisation, chemical composition, and volatile compound profiles of crickets over a 45-day period was assessed. Crickets were reared under controlled conditions and divided into five groups, each receiving one of the experimental diets. Growth performance parameters, including body weight, average daily gain, and feed conversion ratio, were measured. Chemical analysis of the crickets and their diets was conducted, focusing on protein efficiency ratio, apparent dry matter digestibility (ADMD), and nitrogen retention. Volatile compounds were identified using gas chromatography-mass spectrometry. Crickets fed BSG diets exhibited higher protein deposition and lower fat content compared to those on a commercial diet, suggesting BSG’s potential to improve protein quality while reducing fat accumulation. ADMD was also higher in BSG-fed crickets, likely due to the prebiotic effects of components such as β-glucans and arabinoxylans. Additionally, the inclusion of BSG up to 30% in cricket diets reduced production costs by up to 29% compared to a commercial diet. Volatile compound analysis revealed that crickets fed BSG diets produced different odour profiles, with 2-heptanone, a compound with a fruity aroma, detected only in BSG-fed crickets. These findings suggest that BSG is a viable and sustainable ingredient for cricket feed, offering both economic and nutritional benefits while also influencing the sensory characteristics of crickets. Further research is recommended to optimise BSG inclusion levels and explore its broader applications in insect and animal farming.

CRISPR Technology Acts as a Dual-Purpose Tool in Pig Breeding: Enhancing Both Agricultural Productivity and Biomedical Applications

Pigs have long been integral to human society for their roles in agriculture and medicine. Consequently, there is an urgent need for genetic improvement of pigs to meet human dual needs for medicine and food. In agriculture, gene editing can improve productivity traits, such as growth rate and disease resistance, which could lower farming costs and benefit consumers through enhanced meat quality. In biomedical research, gene-edited pigs offer invaluable resources as disease models and in xenotransplantation, providing organs compatible with human physiology. Currently, with CRISPR technology, especially the CRISPR/Cas9 system emerging as a transformative force in modern genetics, pigs are not only sources of sustenance but also cornerstones of biomedical innovation. This review aims to summarize the applications of CRISPR/Cas9 technology in developing pigs that serve dual roles in agriculture and biomedical applications. Compared to ZFNs and TALENs, the CRISPR/Cas9 system offers several advantages, including higher efficiency, greater specificity, ease of design and implementation, and the capability to target multiple genes simultaneously, significantly streamlining the process of genetic modifications in complex genomes. Therefore, CRISPR technology supports the enhancement of traits beneficial for agricultural productivity and facilitates applications in medicine. Furthermore, we must acknowledge the inherent deficiencies and technical challenges of the CRISPR/Cas9 technology while also anticipating emerging technologies poised to surpass CRISPR/Cas9 as the next milestones in gene editing. We hypothesize that with the continuous advancements in gene editing technologies and successful integration of traits beneficial to both agricultural productivity and medical applications, the goal of developing dual-purpose pigs for both agricultural and medical use can ultimately be achieved.

Rapid Classification of Sugarcane Nodes and Internodes Using Near-Infrared Spectroscopy and Machine Learning Techniques

Accurate and rapid discrimination between nodes and internodes in sugarcane is vital for automating planting processes, particularly for minimizing bud damage and optimizing planting material quality. This study investigates the potential of visible-shortwave near-infrared (Vis–SWNIR) spectroscopy (400–1000 nm) combined with machine learning for this classification task. Spectral data were acquired from the sugarcane cultivar Khon Kaen 3 at multiple orientations, and various preprocessing techniques were employed to enhance spectral features. Three machine learning algorithms, linear discriminant analysis (LDA), K-Nearest Neighbors (KNNs), and artificial neural networks (ANNs), were evaluated for their classification performance. The results demonstrated high accuracy across all models, with ANN coupled with derivative preprocessing achieving an F1-score of 0.93 on both calibration and validation datasets, and 0.92 on an independent test set. This study underscores the feasibility of Vis–SWNIR spectroscopy and machine learning for rapid and precise node/internode classification, paving the way for automation in sugarcane billet preparation and other precision agriculture applications.

Human-Centered Robotic System for Agricultural Applications: Design, Development, and Field Evaluation

This paper introduce advancements in agricultural robotics in response to the increasing demand for automation in agriculture. Our research aims to develop humancentered agricultural robotic systems designed to enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications where human labor and experience significantly impact performance, addressing four primary robotic systems, i.e., harvesting robots, intelligent spraying robots, autonomous driving robots for greenhouse operations, and multirobot systems, as a method to expand functionality and improve performance. Each system is designed to operate in unstructured agricultural environments, adapting to specific needs. The harvesting robots address the laborintensive demands of crop collection, while intelligent spraying robots improve precision in pesticide application. Autonomous driving robots ensure reliable navigation within controlled environments, and multirobot systems enhance operational efficiency through optimized collaboration. Through these contributions, this study offers insights into the future of agricultural robotics, emphasizing the transformative potential of integrated, experience-driven intelligent solutions that complement and support human labor in digital agriculture.

Polycaprolactone-zinc oxide biocomposite membranes for wastewater treatment by ultrafiltration process: Synthesis, characterization and removal of inorganic pollutants

The severe droughts impacting North Africa have intensified the pressure on potable water supplies and agricultural irrigation, highlighting the urgent need for sustainable water management solutions. This study explores the potential of poly(ε-caprolactone)/zinc oxide (PCL/ZnO) biocomposites as ultrafiltration membranes for treating urban wastewater, with the goal of enabling water reuse in agriculture. PCL/ZnO membranes, fabricated using the solvent-casting method, were thoroughly characterized using FTIR, XRD, EDAX, SEM, AFM, DSC, TGA, and water contact angle (WCA) measurements. Key findings revealed that increasing ZnO content (1–4 wt%) significantly enhanced membrane thickness, crystallinity, mechanical robustness, and surface roughness, while lowering WCA values to improve hydrophilicity. These enhanced properties are critical for ultrafiltration, as they contribute to higher contaminant rejection and improved water permeability. Performance tests demonstrated ion retention rates of 32–61 % post-filtration, including for toxic metals, effectively reducing pollutant levels. Notably, the treated water met the Food and Agriculture Organization (FAO) standards for irrigation quality, indicating its suitability for agricultural applications. This work highlights the efficacy of PCL/ZnO membranes in facilitating sustainable wastewater recycling under drought conditions, presenting a promising, adaptable solution for water reclamation in water-scarce regions. The results underscore the potential of these biocomposite membranes to advance sustainable water treatment practices, supporting both environmental resilience and agricultural productivity.

Redevelopment of natural eugenol as potential pesticidal resource: New insights into synthesis and antifungal evaluation of eugenol-derived Schiff bases with reactive oxygen species behavior

Natural products have ancient history as fascinating sources of pharmaceuticals, agrochemicals as well as NPs-derived drug/pesticide innovation. Its diversiform biological performance and skeleton diversity enable to fill the drug/pesticide discovery pipeline in response to the growing innovation demand. Eugenol, a naturally occurring phenolic compound from plant Eugenia caryophyllata, have been attracting attention in agricultural application. To further extend its value-added utilizations in agricultural application and elucidate the underlying molecule mechanism, the 35 of novel eugenol-derived Schiff bases were herein skillfully prepared by means of natural-products modification strategy. Interestingly, the antifungal activity results indicated that the optimal antifungal molecule was A10, affording an EC50 of 0.850 μg/mL toward Botryosphaeria dothidea (B. d.), which was superior to those of parent eugenol (EC50 > 100 μg/mL) and ferimzone (EC50 > 100 μg/mL). It was illustrated that introduction of Schiff base scaffold into natural eugenol contributed to enhance the antifungal performance. Furthermore, a series of bioassays showed the antifungal behavior of compound A10, which had a powerful disruptive effect on the membrane by triggering reactive oxygen species production in the mycelium. Taken together, this study redeveloped natural eugenol as potential antifungal candidates to control fungal infections by triggering reactive oxygen species production.

Dry mass grassland estimation using UAV ultra-wide RGB images

Abstract. Dry mass is an important parameter to optimise grassland management. Traditionally, dry mass values are estimated manually by cutting, drying, and weighing vegetation samples. In large areas of cultivation, this becomes a time-consuming and costly activity. In recent years, many researchers have studied different sensors embedded in Unmanned Aerial Vehicles (UAV) to collect spatial data and estimate biomass using machine learning algorithms for forest and agricultural applications. However, there needs to be more research dealing with estimating production indices for pasture, especially in Brazil, as stated. This study evaluates the feasibility of using the GoPro wide-angle RGB camera on UAVs (Unmanned Aerial Vehicles) to estimate the dry mass of pastures. Different data analysis methods were compared, including the combination of vegetation indices (VIs) values and three-dimensional metrics (3D) extracted from the Canopy Height Model (CHM): all metrics (ALL), three VIs plus four 3D metrics (VI3 + CHM4) and only 3D metrics. Random Forest (RF) machine learning algorithm was used to estimate dry mass. The best results were obtained when merging all the variables from the two flight campaigns, with a coefficient of determination (R2) of 0.80 for the model and a Pearson Correlation Coefficient (PCC) of 0.85 for validation, with a Root Mean Square Error (RMSE%) of 20.5%. In summary, using RGB sensors embedded in UAVs is a promising technique for estimating farm grazing parameters.

Neuromorphic Computing for Smart Agriculture

Neuromorphic computing has received more and more attention recently since it can process information and interact with the world like the human brain. Agriculture is a complex system that includes many processes of planting, breeding, harvesting, processing, storage, logistics, and consumption. Smart devices in association with artificial intelligence (AI) robots and Internet of Things (IoT) systems have been used and also need to be improved to accommodate the growth of computing. Neuromorphic computing has a great potential to promote the development of smart agriculture. The aim of this paper is to describe the current principles and development of the neuromorphic computing technology, explore the potential examples of neuromorphic computing applications in smart agriculture, and consider the future development route of the neuromorphic computing in smart agriculture. Neuromorphic computing includes artificial synapses, artificial neurons, and artificial neural networks (ANNs). A neuromorphic computing system is expected to improve the agricultural production efficiency and ensure the food quality and safety for human nutrition and health in smart agriculture in the future.

Leaf Identification in High-Density LiDAR-RGB Data

Abstract. Leaf detection through automated segmentation of 3D data is becoming a crucial technique in many applications of digital agriculture. Some 3D segmentation techniques that can be mentioned are based on normal differences and median normalised vector growth. However, applying these approaches to high canopy density data remains challenging. In this study, we propose a processing flow for leaf detection in high canopy density LiDAR-RGB point clouds. First, a noise removal technique inspired by Moving Least Squares (MLS) was applied to the LiDAR point cloud, and a RGB colour was assigned to each point by combining computer vision and photogrammetric methods. Moreover, once the data were suitable for leaf detection, the branches were filtered using the Statistical Outlier Removal (SOR) filter based on an analysis of the statistical behaviour of the neighbourhood. Afterwards, an unsupervised DBSCAN (Density-Based Clustering Non-Parametric Algorithm) method was used to segment similar points. Finally, the points within each cluster were identified as leaf or non-leaf using the RGB values implemented by our method; ground points were filtered out using a maximum height threshold. As a result, the leaf, non-leaf, and ground point identifiers were correct in 98.9% of cases, with the branch filtering technique SOR proving effectiveness in removing branches with low information loss and without additional complex point densification steps in reconstruction. This SOR-based solution overcomes major challenges in semantic segmentation (leaves and branches) in high-density data and potentially contributes to precision agriculture.

High-throughput screening and identification of lignin peroxidase based on spore surface display of Bacillus subtilis.

Lignin peroxidase is closely related to agriculture and food as it improves the quality of feedstuffs, facilitates the degradation of lignin in agricultural wastes, and degrades azo dyes that have similar complex structures to lignin. However, the current status of homologous or heterologous expression of lignin peroxidase is unsatisfactory and needs to be modified with the help of immobilization and directed evolution to maximize its potential. Directed evolution technology is an effective strategy for designing and improving enzyme characteristics, and Bacillus subtilis spore surface display technology is an efficient method for preparing immobilized enzymes. A colorimetric dye decolorization assay using Congo red as a substrate was developed and optimized for high-throughput screening of spore surface display in a 96-well plate. After two rounds of screening, a superior mutant strain was selected from 2700 mutants. Its highest catalytic activity was 196.36%. The amino acid substitution sites were identified as N120D and I242T. The mechanism for the enhanced catalytic activity was explained using protein modeling and functional analysis software. This study provides insights into the rational design of lignin peroxidase and its application in food and agriculture. © 2024 Society of Chemical Industry.

Factors that affecting firm performance mediated by digital transformation in Indonesia’s state-owned enterprises plantation and forestry industries

Digital transformation refers to the integration of digital technology into all areas of a business, fundamentally changing the way it operates and adding value to a firm's performance. This can involve adopting new technologies, reorganizing business processes, and creating a digital culture within the organization. One of the State-Owned Enterprises (SOE) in Indonesia, PT Perkebunan Nusantara III (Persero) with a focus on agriculture and main commodities, namely Palm Oil and Rubber, is carrying out a digital transformation initiative with the aim of improving firm performance. Along the way, the company faced many obstacles related to implementing digital transformation and the large investment costs incurred. This research aims to test the influence of Digital Transformation Leadership, IT Capability, Digital Mindset, Work Culture Change, Organizational Agility, Human Capital and IT Alignment with Business and test the influence of these variables on Firm Performance with Digital Transformation as a mediating variable. This research uses quantitative research methods by distributing online questionnaires via Google Form to 234 company employees who use Digital Farming, GIS and SCADA applications. Data was analyzed using the SEM-PLS analysis method with Smart-PLS Software version 3.3.9. The results of the research show that IT Capability, Work Culture Change, IT Alignment With Business have a positive effect on Digital Transformation. Apart from that, this research also shows the positive influence of Digital Transformation Leadership on Digital Transformation which is mediated by IT Capability and Digital Mindset on Digital Transformation which is mediated by Work Culture Change. Then, research also shows that Digital Transformation has a significant effect on Firm Performance. the research outcomes can serve as valuable insights for what factors influence firm performance mediated by digital transformation in the SOE plantation and forestry industry business cluster in Indonesia.

Perceptual Pigeon Galvanized Optimization of Multi-objective CNN on the Identification and Classification of Mango Leaves Disease

Aims The aim of this study is to develop a strong Multi-objective Convolutional Neural Network (MOCNN) optimized using Perceptual Pigeon Galvanized Optimization (PPGO) for accurate identification and classification of mango leaf diseases. This approach aims to increase classification accuracy, computational efficiency, and generalization ability. The ultimate goal is to improve disease management in mango crops through advanced image-based diagnostic techniques. Background Demands for the consumption of mango (Mangifera indica) fruit and its leaves were growing exponentially in all parts of the world due to its large health benefits for various organs in the human body. However, these plants were largely exposed to various kinds of microbial diseases during cultivation despite the application of pesticides. Hence, it is becoming a significant threat to the farmers and to the food industry. Objective The objectives of this study are to develop reliable methods for the early identification of mango leaf diseases, enabling prompt intervention and reducing crop damage. Additionally, the study aims to provide effective disease management applications that will help farmers minimize crop losses and maintain their economic stability. Methods PPGO is an advanced optimization algorithm inspired by the natural foraging behavior of pigeons. It integrates perceptual hashing and galvanic responses to adaptively adjust the search process, allowing for efficient exploration and exploitation of the solution space. The multi-objective convolutional neural network is trained to minimize a composite loss function that considers classification accuracy, computational efficiency, and generalization error. Results The Perceptual Pigeon Galvanized Optimization (PPGO) with a Multi-objective Convolutional Neural Network (MOCNN) demonstrates superior performance compared to traditional CNN optimization techniques. The results show an accuracy of 96%, recall of 94%, precision of 92%, and an F1 score of 92%. These metrics surpass those of existing methods such as Efficient Supervised Learning based on Deep Neural Network (ESDNN), Hierarchical Deep Learning Support Vector Machine (HDLSVM), Ordinal Regression Neural Network (ORNN), Deep Convolutional Generative Adversarial Network (DCGAN), Convolutional Neural Network (CNN), Local Contrast Normalization Convolutional Neural Network (LCNN), and Visual Geometry Group Network (VGGNET 19). Conclusion The integration of Perceptual Pigeon Galvanized Optimization with a Multi-objective Convolutional Neural Network offers a powerful approach for identifying and classifying mango leaf diseases. The proposed method effectively balances multiple performance metrics, leading to a robust and efficient model suitable for real-world agricultural applications.

Two Novel Bacterial Species, Rhodanobacter lycopersici sp. nov. and Rhodanobacter geophilus sp. nov., Isolated from the Rhizosphere of Solanum lycopersicum with Plant Growth-Promoting Traits

Two novel bacterial species were isolated from the rhizosphere of Solanum lycopersicum (tomato plant), both exhibiting plant growth-promoting properties. Two isolated strains, Rhodanobacter lycopersici sp. nov. Si-cT and Rhodanobacter geophilus sp. nov. S2-gT, were classified through a polyphasic approach, confirming their novel status within the Rhodanobacter genus. The strains demonstrated a remarkable tolerance to extreme pH conditions, with R. lycopersici Si-cT surviving in pH 3.0–13.0 and R. geophilus S2-gT tolerating pH 2.0–13.0. Additionally, both strains exhibited multiple plant growth-promoting traits, including indole-3-acetic acid and ammonia production, phosphate solubilization, and siderophore formation. These characteristics suggest that the two strains may play an important role in promoting plant growth, especially in soils with variable pH levels. However, since the direct impact on plant growth was not experimentally tested, the potential of these bacteria for agricultural applications remains to be confirmed through further research. This study expands our understanding of the diversity within the Rhodanobacter genus and provides insights into the potential use of these novel species in sustainable agriculture.

Research Update on the Therapeutic Potential of Garden Cress (Lepidium sativum Linn.) with Threatened Status.

Garden cress (Lepidium sativum) has been used in India for medicinal purposes since the Vedic era. Garden cress, a native of Egypt and southwest Asia, is a small perennial edible herb that has been used to treat many diseases for centuries. The seeds, leaves as well as roots have medicinal properties. The seeds are rich in protein, fat, calcium, and iron and have high nutritional value. They are considered to be galactagogue, anticarcinogenic, antidiabetic, antiasthmatic and antidiarrheal. Leaves, seeds, and aerial parts extracts are found to have alkaloids, flavonoids, glycosides, polypeptides, vitamins, minerals, proteins, fats, and carbohydrates. Lepidium sativum is known for its pungent odor due to the several volatile oils and has been used to treat various conditions, including respiratory disorders, muscle pain, inflammation, and bone fractures in the past. Lepidium sativum is a fast-growing annual herb; in India, it is commonly known as Chandrasoor. Whole fruits or seeds are used, fresh or dried, as a seasoning with a peppery flavor. Boiled seeds are consumed in drinks by Arabs, either ground in honey or as an infusion in hot milk. The seed oil can be used for illumination and soap making. Additionally, limited awareness and conservation efforts have further contributed to its threatened status. Recognizing the importance of preserving this valuable plant species is crucial for maintaining biodiversity and ensuring its availability for future generations. Furthermore, this review explores the potential benefits of Lepidium sativum in different domains. Its nutritional value and health benefits make it a promising candidate for addressing malnutrition and improving overall well-being. The presence of bioactive compounds suggests its potential use in functional foods, pharmaceuticals, and natural medicines for various ailments. Moreover, Lepidium sativum exhibits antimicrobial and insecticidal properties, offering potential applications in agriculture and pest control. The current review discussed the nutritional, potential benefits and pharmacological effects of Lepidium sativum.