Agricultural Applications Research Articles

Climate change scenario analysis in Spree catchment, Germany using statistically downscaled ERA5-Land climate reanalysis data

This study focuses on the statistical downscaling of ERA5-Land reanalysis data using the Statistical DownScaling Model (SDSM) to generate climate change scenarios for the Spree catchment. Linear scaling was used to reduce the biases of the Global Climate Model for precipitation and temperature. The statistical analyses demonstrated that this method is a promising and straightforward way of correcting biases in climate data. SDSM was used to generate climate change scenarios, which considered three emission scenarios: RCP 2.6, RCP 4.5, and RCP 8.5. The results indicated that higher precipitation is expected under higher emission scenarios. Specifically, the summer and autumn seasons were projected to experience up to 50 mm more rainfall in the next 80 years, and the temperature was projected to increase by up to 1∘C by 2100. These projections of climate data for different scenarios are useful for assessing water management studies for agricultural and hydrologic applications considering changing climate conditions. This study highlights the importance of statistical downscaling and scenario generation in understanding the potential impacts of climate change on water resources. The results of this study can provide valuable insights into water resource management, especially on adapting to changing climate conditions.

Experimental investigation on mechanical performance of cotton fiber reinforced epoxy composites for agriculture applications

This research paper investigates the influence of different weight percentages (5%, 10%, 15%, and 20%) of cotton fiber reinforcement in epoxy composites on their mechanical properties. The mechanical characteristics examined include flexural strength, tensile strength, fracture toughness, impact strength, and hardness. The interfacial properties of the composites were analyzed through scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy. Thermal and thermo-mechanical properties were examined using thermogravimetric analysis and dynamic mechanical analysis, respectively. The findings revealed that the addition of 15 wt.% cotton fiber in epoxy composites led to notable improvements in various mechanical properties. As the weight percentage of cotton fiber increased, the fracture and tensile properties of the composites showed a gradual improvement until reaching 15 wt.%. However, additional increments in weight percentage did not lead to significant improvements in the mechanical properties. Flexural strength and hardness showed a decreasing trend with increasing cotton fiber weight percentage, whereas impact strength showed an increasing trend. The SEM micrograph of the fracture surface revealed fiber pull-out, matrix cracking, interfacial debonding, and an uneven surface.

Elucidation of the Biosynthesis of Griseoluteic Acid in Streptomyces griseoluteus P510.

Phenazines are aromatic compounds with antifungal and cytotoxic activities. Phenazines incorporating phenazine 1-carboxylic acid have widespread applications in agriculture, medicine, and industry. Griseoluteic acid is a cytotoxic compound secreted by Streptomyces griseoluteus P510, displaying potential medical applications. However, the biosynthetic pathway of griseoluteic acid has not been elucidated, limiting its development and application. In this study, a conserved phenazine biosynthetic gene cluster of S. griseoluteus P510 was identified through genomic analysis. Subsequently, its was confirmed that the four essential modification enzymes SgpH, SgpI, SgpK, and SgpL convert phenazine-1,6-dicarboxylic acid into griseoluteic acid by heterologous expression in Escherichia coli. Moreover, the biosynthetic pathway of griseoluteic acid was established in Pseudomonas chlororaphis characterized by a high growth rate and synthesis efficiency of phenazines, laying the foundation for the efficient production of griseoluteic acid.

Viperin-like proteins interfere with RNA viruses in plants.

Plant viruses cause substantial losses in crop yield and quality; therefore, devising new, robust strategies to counter viral infections has important implications for agriculture. Virus inhibitory protein endoplasmic reticulum-associated interferon-inducible (Viperin) proteins are conserved antiviral proteins. Here, we identified a set of Viperin and Viperin-like proteins from multiple species and tested whether they could interfere with RNA viruses in planta. Our data from transient and stable overexpression of these proteins in Nicotiana benthamiana reveal varying levels of interference against the RNA viruses tobacco mosaic virus (TMV), turnip mosaic virus (TuMV), and potato virus x (PVX). Harnessing the potential of these proteins represents a novel avenue in plant antiviral approaches, offering a broader and more effective spectrum for application in plant biotechnology and agriculture. Identifying these proteins opens new avenues for engineering a broad range of resistance to protect crop plants against viral pathogens.

Enhancing mechanical and rheological properties of HDPE films through annealing for eco-friendly agricultural applications

Abstract A composite of high-density polyethylene/ultra-high molecular weight polyethylene (HDPE/UHMWPE) was synthesized via a high-temperature melting process. We specifically examined the effects of annealing on the morphological and rheological properties of the composite. Using scanning electron microscopy and advanced rotational rheometry, we determined that annealing notably alters the composite’s microstructure, with a reduction in pronounced features post-treatment. Rheologically, UHMWPE content significantly enhanced mechanical properties, particularly in storage modulus (G′), loss modulus (G″), and complex viscosity (|η*|), which aligned with scaling law principles. Our findings highlight a distinct improvement in mechanical properties post-annealing, underscoring the potential of UHMWPE in enhancing polymer composite performance. The scaling laws relating G′, G″, and |η*| with UHMWPE were identified as follows: (1) pre-c e: G′ ∼ c 0.2, g″ ∼ c 0.07, |η*| ～ c0.07 and (2) post-c e: G′ ∼ c 1.20, g″ ∼ c 0.6, |η*| ∼ c 0.64, with a forward shift in c e noted after annealing. The addition of UHMWPE significantly enhanced the system’s properties, particularly after annealing. This study offers crucial insights into optimizing HDPE plastic films for enhanced durability and sustainability, ideally suiting them for eco-friendly agricultural uses.

2-Heptanol inhibits Botrytis cinerea by accelerating amino acid metabolism and retarding membrane transport.

During the postharvest storage of tomatoes, they are susceptible to infection by Botrytis cinerea, leading to significant economic losses. This study evaluated the antifungal potential of 2-heptanol (2-HE), a volatile biogenic compound, against B. cinerea and explored the underlying antifungal mechanism. The results indicated that 2-HE effectively suppressed the growth of B. cinerea mycelia both in vivo and in vitro and stimulated the activities of antioxidative enzymes, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) in tomatoes. Furthermore, 2-HE reduced spore viability, compromised membrane integrity, and resulted in increased levels of extracellular nucleic acids, protein content, and membrane lipid peroxidation. Transcriptomic analysis revealed that 2-HE disrupted the membrane transport system and enhanced amino acid metabolism, which led to intracellular nutrient depletion and subsequent B. cinerea cell death. Additionally, the 2-HE treatment did not negatively impact the appearance or quality of the tomatoes. In conclusion, the findings of this study offer insights into the use of 2-HE as a biocontrol agent in food and agricultural applications.

Evaluation of Data Sufficiency for Interannual Knowledge Transfer of Crop Type Classification Models

We present a study on the effectiveness of using varying data sizes to transfer crop type classification models from one year to the next, emphasizing the balance between data sufficiency and model accuracy. The significance of crop detection through satellite imaging lies in its potential to enhance agricultural productivity and resource management. Machine learning, particularly techniques like long short-term memory (LSTM) models, has become instrumental in interpreting these satellite data due to its predictive accuracy and adaptability. However, the direct application of models trained in one year to subsequent years poses challenges due to variations in environmental conditions and agricultural practices. Fine-tuning pre-existing models is a prevalent strategy to overcome these temporal discrepancies, though it necessitates a careful evaluation of the quantity and relevance of new data. This study explores the cost–benefit of fine-tuning existing models versus developing new ones based on the quantity of new data, utilizing LSTM models for their transferability and practicality in agricultural applications. Experiments conducted using satellite data from farms in southern Alberta reveal that smaller datasets, with fewer than 25 fields per class, can effectively fine-tune models for accurate interannual classification, while larger datasets are more conducive to training new models. This poses a key challenge in optimizing data usage for crop classification, straddling the line between data sufficiency and computational efficiency. The findings offer valuable insights for optimizing data use in crop classification, benefiting both academic research and practical agricultural applications.

Improved YOLO v7 for Sustainable Agriculture Significantly Improves Precision Rate for Chinese Cabbage (Brassica pekinensis Rupr.) Seedling Belt (CCSB) Detection

Precise navigation in agricultural applications necessitates accurate guidance from the seedling belt, which the Global Positioning System (GPS) alone cannot provide. The overlapping leaves of Chinese cabbage (Brassica pekinensis Rupr.) present significant challenges for seedling belt fitting due to difficulties in plant identification. This study aims to address these challenges by improving the You Only Look Once (YOLO) v7 model with a novel approach that decouples its network head deriving from the Faster-Regions with Convolutional Neural Network (Faster R-CNN) architecture. Additionally, this study introduced a BiFormer attention mechanism to accurately identify the centers of overlapping Chinese cabbages. Using these identified centers and pixel distance verification, this study achieved precise fitting of the Chinese cabbage seedling belt (CCSB). Our experimental results demonstrated a significant improvement in performance metrics, with our improved model achieving a 2.5% increase in mean average precision compared to the original YOLO v7. Furthermore, our approach attained a 94.2% accuracy in CCSB fitting and a 91.3% Chinese cabbage identification rate. Compared to traditional methods such as the Hough transform and linear regression, our method showed an 18.6% increase in the CCSB identification rate and a 17.6% improvement in angle accuracy. The novelty of this study lies in the innovative combination of the YOLO v7 model with a decoupled head and the BiFormer attention mechanism, which together advance the identification and fitting of overlapping leafy vegetables. This advancement supports intelligent weeding, reduces the reliance on chemical herbicides, and promotes safer, more sustainable agricultural practices. Our research not only improves the accuracy of overlapping vegetable identification, but also provides a robust framework for enhancing precision agriculture.

The Plant-Associated Flavobacterium: A Hidden Helper for Improving Plant Health.

Flavobacterium is a genus within the phylum Bacteroidota that remains relatively unexplored. Recent analyses of plant microbiota have identified the phylum Bacteroidota as a major bacterial group in the plant rhizosphere. While Flavobacterium species within the phylum Bacteroidota have been recognized as pathogens in the aquatic habitats, microbiome analysis and the characterization of novel Flavobacterium species have indicated the great diversity and potential of their presence in various environments. Many Flavobacterium species have positively contribute to plant health and development, including growth promotion, disease control, and tolerance to abiotic stress. Despite the well-described beneficial interactions of the Flavobacterium species with plants, the molecular mechanisms and bacterial determinants underlying these interactions remain unclear. To broaden our understanding of the genus Flavobacterium's role in plant health, we review the recent studies focusing on their ecological niche, functional roles, and determinants in plant-beneficial interactions. Additionally, this review discusses putative mechanisms explaining the interactions between plants and Flavobacterium. We have also introduced the importance of future research on Flavobacterium spp. and its potential applications in agriculture.

Performance evaluation of BLDC motor drive mounted in aerial vehicle (drone) using adaptive neuro-fuzzy

The development of autonomous drones equipped with cameras and various sensors has paved the way for their application in agriculture and perimeter security. These aerial drones require specific power, acceleration, high torque, and efficiency to meet the demands of agricultural tasks, utilizing built-in brushless DC (BLDC) motors. However, a common challenge drone’s face is maintaining the desired speed for extended periods. Enhancing the performance of BLDC motors through advanced controllers is crucial to address this issue. This research paper proposes optimizing the size and speed of brushless DC motors for aerial vehicles using an adaptive fuzzy inference system and supervised learning techniques. When these drones carry loads, the BLDC motors must dynamically adjust the drone's speed. During this phase, the motors must control their speed and torque using artificial intelligence controllers like adaptive neuro-fuzzy inference systems (ANFIS) to enhance the drone's functionality, resilience, and safety. This research has conducted analyses focused on improving the performance of BLDC motors explicitly personalized for unmanned aerial vehicle (UAVs). The proposed method will be implemented using MATLAB/Simulink, expecting to significantly enhance the BLDC motor's performance compared to conventional controllers. Comparative analyses will be conducted between traditional and ANFIS controllers to validate the effectiveness of the proposed approach.

Effect of different sizes of nanocopper particles on rainbow trout (Oncorhynchus mykiss W.) spermatozoa motility kinematics

In recent years, nanocopper (Cu NPs) has gained attention due to its antimicrobial properties and potential for industrial, agricultural, and consumer applications. But it also has several effects on the aquatic environment. Widespread use of various nanoproducts has raised concerns about impacts of different nanoparticle size on environment and biological objects. Spermatozoa is a model for studying the ecotoxic effects of pollutants on cells and organisms. This study aimed to investigate the effects of different sizes of copper nanoparticles on rainbow trout spermatozoa motility, and to compare their effects with copper ionic solution. Computer assisted sperm analysis (CASA) was used to detect movement parameters at activation of gametes (direct effect) with milieu containing nanocopper of primary particle size of 40–60, 60–80 and 100 nm. The effect of the elements ions was also tested using copper sulfate solution. All products was prepared in concentration of 0, 1, 5, 50, 125, 250, 350, 500, 750, and 1000 mg Cu L−1. Six motility parameters were selected for analysis.The harmful effect of Cu NPS nanoparticle was lower than ionic form of copper but the effect depends on the motility parameters. Ionic form caused complete immobilization (MOT = 0 %, IC100) at 350 mg Cu L−1 whilst Cu NPs solution only decreased the percentage of motile sperm (MOT) up to 76.4 % at highest concentration tested of 1000 mg Cu L−1 of 40–60 nm NPs. Cu NPs of smaller particles size had more deleterious effect than the bigger one particularly in percentage of MOT and for curvilinear velocity (VCL). Moreover, nanoparticles decrease motility duration (MD). This may influence fertility because the first two parameters positively correlate with fertilization rate. However, the ionic form of copper has deleterious effect on the percentage of MOT and linearity (LIN), but in some concentrations it slightly increases VCL and MD.

HybridTransferNet: soil image classification through comprehensive evaluation for crop suggestion

<p>Soil image classification is a critical task within the realms of agriculture and environmental applications. In recent years, the integration of deep learning has sparked significant interest in image-based soil classification. Transfer learning, a well-established technique in image classification, involves finetuning a pre-trained model on a specific dataset. However, conventional transfer learning methods typically focus solely on fine-tuning the final layer of the pre-trained model, which may not suffice to attain high performance on a new task. HybridTransferNet, a unique hybrid transfer learning approach designed for soil classification based on images is proposed in this paper. HybridTransferNet goes beyond the conventional approach by finetuning not only the final layer but also a select number of earlier layers in a pre-trained ResNet50 model. This extension results in substantially enhanced ability to classify when compared to standard transfer learning methods. Our evaluation of HybridTransferNet, conducted on a soil classification dataset, encompasses the reporting of various performance indicators, such as the F1 score, recall, accuracy, and precision. Our findings from experiments highlight HybridTransferNet's advantages over conventional transfer learning strategies, establishing it as a state-of-the-art solution in the domain of soil classification.</p>

Capacitive deionization technology with multi-stage treatment as an efficient desalination process for agricultural irrigation

Novel capacitive deionization (CDI) technology is recognized as a cheap desalination method and holds a promise to be deployed in low-profit fields such as agricultural and municipal works. In this study, the application feasibility of CDI technology in agricultural irrigation is thoroughly investigated. The desalination performance of three operation methods (“low flow rate”, “high applied voltage”, and “two-stage treatment”) are compared for treating artificial brackish water of 2000 mg L−1 to a set desalination target (600 mg L−1). Among these three methods, “two-stage treatment” overwhelms the other two methods regarding several desalination indicators, achieving a decent productivity (1.5 L h−1 m−2) with a small energy input of 1.4 kWh m−3. The modified Donnan model indicates that energy dissipation is mitigated in a high flow rate adopted in two-stage treatment, and meantime a high desalination capacity and a good charge efficiency are achieved. In addition, desalinated water with a salt concentration gradient is produced by the CDI module for tomato seedlings irrigation. Compared with the tomato seedlings irrigated by feedwater (2000 mg L−1), the total length and the biomass weight of those irrigated by desalinated water of 300 mg L−1 increase 51.1 % and 119.8 %, respectively. The research accomplishes a proof-of-concept of the agricultural application of CDI technology, and provides reliable data as the foundation for its future practical operation.

Synthesis of Nano Sulfur/Chitosan-Copper Complex and Its Nematicidal Effect against Meloidogyne incognita In Vitro and on Coffee Pots.

Sulfur is one of the inorganic elements used by plants to develop and produce phytoalexin to resist certain diseases. This study reported a method for preparing a material for plant disease resistance. Sulfur nanoparticles (SNPs) stabilized in the chitosan-Cu2+ (CS-Cu2+) complex were synthesized by hydrolysis of Na2S2O3 in an acidic medium. The obtained SNPs/CS-Cu2+ complex consisting of 0.32% S, 4% CS, and 0.7% Cu (w/v), contained SNPs with an average size of ~28 nm as measured by transmission electron microscopy images. The X-ray diffraction pattern of the SNPs/CSCu2+ complex showed that SNPs had orthorhombic crystal structures. Interaction between SNPs and the CS-Cu2+ complex was also investigated by ultraviolet-visible. Results in vitro nematicidal effect of materials against Meloidogyne incognita showed that SNPs/CS-Cu2+ complex was more effective in killing second-stage juveniles (J2) nematodes and inhibiting egg hatching than that of CS and CS-Cu2+ complex. The values of LC50 in killing J2 nematodes and EC50 in inhibiting egg hatching of SNPs/CS-Cu2+ complex were 75 and 51 mg/l, respectively. These values were lower than those of CS and the CS-Cu2+ complex. The test results on the nematicidal effect against M. incognita on coffee pots showed that the SNPs/CS-Cu2+ complex was 100% effective at a concentration of 150 mg/l. Therefore, the SNPs/CS-Cu2+ complex could be considered as a biochemical material with potential for agricultural applications to control root-knot nematodes.

Effects of biogas slurry on hydrothermal carbonization of digestate: Synergistic valorization of hydrochars and aqueous phase

In this study, livestock manure digestate (LMD) was used as feedstock for hydrothermal carbonization (HTC) at different temperature (180–260 °C) and residence time (0–4 h). Nutrient flow and distribution during the HTC process were evaluated by comparing the effects of livestock manure biogas slurry (LBS) and ultrapure water (UW) to determine the optimal reaction conditions for the synergistic production and application of hydrochars (HC) and aqueous phases (AP). Compared with UW, the HC yields derived from LBS as solvent were increased by 27.05–38.24% under the same conditions. The C content, high heating value (HHV), and energy densification of HC obtained from LMD and UW were higher than those obtained from LMD and LBS, and the ash content was lower. While, LBS circumstance improved the porosity, N content and some trace elements e.g. Ca, Fe and Mg in HC that showed excellent fertility potential. In addition, the recovery rate of K, TOC, NH4+-N, and TN concentrations in AP were significantly higher in the LBS circumstance than in UW. The results show that the addition of UW is more favorable for fuel generation, and the HC obtained from LMD and UW at 220 °C has the potential to be used as a fuel. Whereas, the addition of LBS enhanced the potential of HC and AP for agricultural applications simultaneously. It is recommended to use HC and AP obtained from LMD and LBS at 240 °C for using as fertilizer.

Internet of Things - Novel Application in Agriculture

Abstract: In the Present Era, the world is experiencing a strong rush towards modern technology, while specialized companies are living a terrible rush in the information and Communication technology towards the so-called Internet of things IoT or Internet of objects, which is the integration of things with the world of Internet, by adding hardware or/and software to be smart and so is able to communicate with each other and participate effectively in all aspects of daily life. By enabling new forms of communication between people and things, and between things themselves, that will change the traditional life into a high style of living. But it won’t be easy, because there are still many challenges and issues that need to be addressed and have to be viewed from various aspects to realize their full potential. The main objective of this paper is to provide the reader with a detailed discussion from a technological and social perspective. The various IoT aspects, definition and architecture, challenges and issues were discussed. Furthermore, a description of several sensors and actuators and their smart communication. Also, the most important application areas of IoT were presented with a case study. This work will help readers and researchers understand the IoT and its potential application in the real world

Agricultural object detection with You Only Look Once (YOLO) Algorithm: A bibliometric and systematic literature review

Vision is a major component in several digital technologies and tools used in agriculture. Object detection plays a pivotal role in digital farming by automating the task of detecting, identifying, and localization of various objects in large-scale agrarian landscapes. The single-stage detection algorithm, You Only Look Once (YOLO), has gained popularity in agriculture in a relatively short span due to its state-of-the-art performance in terms of accuracy, speed, and network size. YOLO offers real-time detection performance with good accuracy and is implemented in various agricultural tasks, including monitoring, surveillance, sensing, automation, and robotics operations. The research and application of YOLO in agriculture are accelerating at a tremendous speed but are fragmented and multidisciplinary in nature. Moreover, the performance characteristics (i.e., accuracy, speed, computation) of the object detector influence the rate of technology implementation and adoption in agriculture. Therefore, this study aimed to collect extensive literature to document and critically evaluate the advances and application of YOLO for agricultural object recognition tasks. First, we conducted a bibliometric review of 257 selected articles to understand the scholarly landscape (i.e., research trends, evolution, global hotspots, and gaps) of YOLO in the broad agricultural domain. Secondly, we conducted a systematic literature review on 30 selected articles to identify current knowledge, critical gaps, and modifications in YOLO for specific agricultural tasks. The study critically assessed and summarized the information on YOLO’s end-to-end learning approach, including data acquisition, processing, network modification, integration, and deployment. We also discussed task-specific YOLO algorithm modification and integration to meet the agricultural object or environment-specific challenges. In general, YOLO-integrated digital tools and technologies showed the potential for real-time, automated monitoring, surveillance, and object handling to reduce labor, production cost, and environmental impact while maximizing resource efficiency. The study provides detailed documentation and significantly advances the existing knowledge on applying YOLO in agriculture, which can greatly benefit the scientific community. The results of this study open the door for implementing YOLO-based solutions in practical agricultural scenarios and add to the expanding corpus of information on computer vision applications in agriculture.

Developmental toxicity of the neonicotinoid pesticide clothianidin to the larvae of the crustacean Decapoda, Penaeus vannamei

The developmental toxicity effects of neonicotinoid pesticides such as clothianidin have not been fully explored in agricultural applications. This is particularly noteworthy because such pesticides significantly impact the survival rates of invertebrates, with arthropod larvae being particularly vulnerable. This study aimed to address this research gap by specifically investigating the toxicological effects of clothianidin on the developmental stages of the larvae of the economically important aquaculture species Penaeus vannamei. In these experiments, shrimp eggs were exposed to seawater containing different concentrations of clothianidin beginning at N1, and each phase was observed and analyzed to determine its toxic impact on larval development. These results revealed that clothianidin induces an increase in deformity rates and triggers abnormal cell apoptosis. It also significantly reduced survival rates and markedly decreased body length and heart rate in the later stages of larval development (P3). Transcriptomic analysis revealed disruptions in larval DNA integrity, protein synthesis, and signal transduction caused by clothianidin. To survive prolonged exposure, larvae may attempt to maintain their viability by repairing cell structures and enhancing signal transduction mechanisms. This study offers the first empirical evidence of the toxicity of clothianidin to arthropod larvae, underscoring the impact of environmental pollution on aquatic health.

The vast landscape of carbohydrate fermentation in prokaryotes.

Fermentation is a type of metabolism carried out by organisms in environments without oxygen. Despite being studied for over 185 years, the diversity and complexity of this metabolism are just now becoming clear. Our review starts with the definition of fermentation, which has evolved over the years and which we help further refine. We then examine the range of organisms that carry out fermentation and their traits. Over one-fourth of all prokaryotes are fermentative, use more than 40 substrates, and release more than 50 metabolic end products. These insights come from studies analyzing records of thousands of organisms. Next, our review examines the complexity of fermentation at the biochemical level. We map out pathways of glucose fermentation in unprecedented detail, covering over 120 biochemical reactions. We also review recent studies coupling genomics and enzymology to reveal new pathways and enzymes. Our review concludes with practical applications for agriculture, human health, and industry. All these areas depend on fermentation and could be improved through manipulating fermentative microbes and enzymes. We discuss potential approaches for manipulation, including genetic engineering, electrofermentation, probiotics, and enzyme inhibitors. We hope our review underscores the importance of fermentation research and stimulates the next 185 years of study.

REMOVAL OF HEAVY METALS FROM INDUSTRIAL SEWAGE SLUDGE BY APPLICATION OF VERMICOMPOSTING.

India faces a significant challenge with the escalating production of industrial sewage sludge, which contains hazardous heavy metals and toxins, posing a serious environmental threat. Managing the disposal of such waste is increasingly difficult due to its resistance to degradation. This research explores the potential of vermicomposting as a sustainable solution for mitigating heavy metal contamination in sewage sludge. Vermicomposting harnesses earthworms to accelerate organic matter decomposition and immobilize heavy metals. The study investigates various vermicomposting parameters, such as worm species, moisture levels, pH, and NPK to optimize heavy metal removal. Furthermore, it assesses the quality of the resulting vermicompost, focusing on heavy metal concentrations and potential agricultural applications. The findings indicate that vermicomposting holds promise as an eco-friendly method for addressing heavy metal pollution in sewage sludge, offering opportunities for sustainable waste management and soil enhancement. Key Words: Industrial waste, Heavy Metals, Land applications, Vermicomposting Method, Chemical speciation, Characterization and speciations.