Efficient Agricultural Practices Research Articles

A Review on the Current Trends in Crop Yield Forecasting Using Artificial Intelligence

In the face of escalating global food demands and the increasing unpredictability of climate conditions, the importance of precise crop yield forecasting has never been more critical. This paper provides a comprehensive review of the current trends in leveraging Artificial Intelligence (AI) to enhance crop yield predictions, which is pivotal for strategic agricultural planning and ensuring food security. Our review covers a range of AI methodologies, including machine learning, deep learning, and hybrid models, that have been employed to predict crop yields with increasing accuracy. Recent advancements have demonstrated that machine learning techniques, such as support vector machines and random forests, are effective in modeling complex agricultural data sets with a notable degree of precision. However, deep learning approaches, including convolutional and recurrent neural networks, have started to outperform traditional machine learning models, owing to their ability to process large-scale spatial-temporal data from remote sensing and IoT-based agricultural sensors. We also explore the emergence of hybrid AI models that combine the strengths of both machine learning and deep learning technologies, providing enhanced accuracy and robustness in yield prediction under varying climatic conditions. Additionally, this review discusses the integration of AI with geographic information systems (GIS) and remote sensing technologies, which has significantly improved the spatial resolution of yield predictions. We highlight several key challenges that remain, such as data scarcity, the need for model generalization, and the integration of socioeconomic factors into yield prediction models. In conclusion, AI presents transformative potential for crop yield forecasting. By harnessing cutting-edge AI technologies and addressing existing challenges, significant strides can be made towards more sustainable and efficient agricultural practices. This paper aims to inspire continued research and innovation in this critical field.

Survey on Optimal Crop Prediction using Soil and Weather Analysis

This survey paper provides a comprehensive overview of methodologies, technologies, and approaches for analyzing soil environments to optimize crop production. It covers traditional soil sampling, advanced techniques like remote sensing, and real-time monitoring using sensor networks. The integration of sensor data with IoT and machine learning for decision support is explored. Soil health assessment and site-specific management strategies enabled by precision agriculture are emphasized. Challenges such as data integration and sensor accuracy are addressed, along with future research directions including low-cost sensing solutions and improved data analytics. Overall, the paper serves as a valuable resource for optimizing crop production sustainably, contributing to efficient agricultural practices in a changing climate

Chromatic Wavelengths Luminescence: It’s Impact to Growth Response of Tsitsirika (Catharanthus Roseus)

This study seeks to analyze the complex interactions between color psychology, lighting technology, and the cultivation of Tsitsirika (Catharanthus roseus) in the Philippine agricultural environment. The study is organized around three main goals: first, to investigate the effect of several LED wavelengths, specifically red, blue, and green, on the overall growth parameters of Tsitsirika. Second, it intends to conduct a rigorous comparative analysis, carefully comparing the development patterns shown by various colored LEDs to those detected in natural solar irradiation. This comprehensive analysis seeks to identify significant differences and explain potential benefits or drawbacks related with the use of artificial lighting. Ultimately, the study aims to explain the physiological characteristics induced by red, blue, and green LED wavelengths on Tsitsirika, revealing their subtle effects on the plant's physiological and morphological aspects, including leaf pigmentation and stomatal behavior. By thoroughly addressing these objectives, this study hopes to provide nuanced and academically valuable insights on the prudent application of color psychology and lighting technology, ultimately influencing optimal cultivation practices for Tsitsirika. This scholarly endeavor is positioned to support sustainable and efficient agricultural practices, not just in the Philippines but also with potential for broader application.

Advancements in Agricultural Automation: SVM Classifier with Hu Moments for Vegetable Identification

This study investigates the application of Support Vector Machine (SVM) classifiers in conjunction with Hu Moments for the purpose of classifying segmented images of vegetables, specifically Broccoli, Cabbage, and Cauliflower. Utilizing a dataset comprising segmented vegetable images, this research employs the Canny method for image segmentation and Hu Moments for feature extraction to prepare the data for classification. Through the implementation of a 5-fold cross-validation technique, the performance of the SVM classifier was thoroughly evaluated, revealing moderate accuracy, precision, recall, and F1-scores across all folds. The findings highlight the classifier's potential in distinguishing between different vegetable types, albeit with identified areas for improvement. This research contributes to the growing field of agricultural automation by demonstrating the feasibility of using SVM classifiers and image processing techniques for the task of vegetable identification. The moderate performance metrics emphasize the need for further optimization in feature extraction and classifier tuning to enhance classification accuracy. Future recommendations include exploring alternative machine learning algorithms, advanced feature extraction methods, and expanding the dataset to improve the classifier's robustness and applicability in agricultural settings. This study lays a foundation for future advancements in automated vegetable sorting and quality control, offering insights that could lead to more efficient agricultural practices.

Tree Canopy Architecture and Engineering: Physiological and Molecular Aspects

The fruit industry is a vital part of the agricultural economy, and the quality and quantity of crops depend heavily on the science of tree canopy architecture and engineering. The canopy is the most important part of a fruit tree as it affects photosynthesis, fruit quality, and yield. The study of canopy architecture and engineering increased in importance due to the need for efficient agricultural practices that optimize fruit production while reducing the environmental impact. Factors that influence canopy architecture, such as light, water, and nutrient availability, genetic makeup of the plants, hormones, management practices and innovative engineering techniques can optimize orchard management. Canopy can managed by the using different types of pruning (Proper pruning can enhance light penetration, airflow, and disease management, ultimately leading to higher yields and healthier fruits), thinning, by using dwarfing rootstocks and training (such as central leader system, modified central leader system and open Centre system etc.) and also by using the growth retardant & regulators and through gene regulation for higher yield as well as quality improvement.

A Comprehensive Review on Aminochelates: Advances and Applications in Plant Nutrition

This comprehensive review explores the advances and applications of aminochelates in plant nutrition, shedding light on their significance and impact in modern agricultural practices. Aminochelates, which consist of metal ions chelated with amino acids, have gained substantial attention for their role in enhancing nutrient uptake and addressing deficiencies in various plant species. The review begins by delving into the fundamental principles of aminochelation and the unique properties that make these compounds highly effective in facilitating nutrient absorption by plants. The interaction between aminochelates and different nutrient elements is examined, providing insights into the mechanisms that contribute to improved nutrient availability in the soil and subsequent uptake by plant roots. Furthermore, the paper discusses the role of aminochelates in mitigating nutrient stress and enhancing plant growth and development. The review incorporates a comprehensive analysis of research findings, including studies on diverse crops, soil types, and environmental conditions. Special attention is given to the safety of aminochelates for plants and the environment, ensuring a balanced evaluation of their overall impact on sustainable agriculture. Additionally, the review explores recent technological advancements and innovations related to the synthesis and application of aminochelates, highlighting emerging trends and potential future developments in the field of plant nutrition.The synthesis methods, application strategies, and their efficacy in different agricultural settings are critically assessed. In conclusion, this review provides a thorough understanding of aminochelates, emphasizing their significance as a promising tool for improving plant nutrition. The insights presented in this paper contribute to the existing knowledge base, guiding future research directions and applications in the realm of sustainable and efficient agricultural practices.

Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning

In the era of advancing artificial intelligence (AI), its application in agriculture has become increasingly pivotal. This study explores the integration of AI for the discriminative classification of corn diseases, addressing the need for efficient agricultural practices. Leveraging a comprehensive dataset, the study encompasses 21,662 images categorized into four classes: Broken, Discolored, Silk cut, and Pure. The proposed model, an enhanced iteration of MobileNetV2, strategically incorporates additional layers—Average Pooling, Flatten, Dense, Dropout, and softmax—augmenting its feature extraction capabilities. Model tuning techniques, including data augmentation, adaptive learning rate, model checkpointing, dropout, and transfer learning, fortify the model's efficiency. Results showcase the proposed model's exceptional performance, achieving an accuracy of ~96% across the four classes. Precision, recall, and F1-score metrics underscore the model's proficiency, with precision values ranging from 0.949 to 0.975 and recall values from 0.957 to 0.963. In a comparative analysis with state-of-the-art (SOTA) models, the proposed model outshines counterparts in terms of precision, recall, F1-score, and accuracy. Notably, MobileNetV2, the base model for the proposed architecture, achieves the highest values, affirming its superiority in accurately classifying instances within the corn disease dataset. This study not only contributes to the growing body of AI applications in agriculture but also presents a novel and effective model for corn disease classification. The proposed model's robust performance, combined with its competitive edge against SOTA models, positions it as a promising solution for advancing precision agriculture and crop management.

Automated Mixing Process for Solar Powered Fertigation System Using IoT Application

The rising population globally, along with the need to maintain sustainable food production, has increased demand for efficient agricultural practices. Fertigation is the combination of fertilization with irrigation, has emerged as an effective method for increasing harvests while using less resources. The problem of traditional method are inefficiency and inaccuracy of manual fertilization methods in agriculture. The traditional methods of fertilization rely on manual application of fertilizers, which can be time- consuming and labor-intensive. Additionally, manual methods can lead to over- or under-fertilization, which can negatively impact plant growth and yield. As a solution, an automated mixing process for fertigation system powered by solar using IoT application was developed to solve the problem. This system can be controlled using a smartphone and all the process from flowing the fertilizer to a mixture tank and watering the plant can be automatically implemented. The nutrient for each type of plant can be established using Blynk application such that the plant could grow properly. This prototype can also help the user monitor the level of fertilizer in the tank in which the user gets a message from the smartphone if the fertilizer level is decreased or low. The data collected is the time taken by the system to mix the setup value of the solution using Electrical Conductivity (EC) sensor. It can be concluded that if the setup EC value in Blynk is large, the time taken to mix the solution increases. Another result is monitoring the fertilizer level of fertilizer in two tanks. In this case, the ultrasonic sensor detects the distance of fertilizer in both tanks and if the level of fertilizer in the tank is low, a notification will appear in Blynk application, stating that the tank level is low. All the output from this system is proven to be successful in this work.

Boron Fertilization Improves the Agronomic Performance of Soybean Genotypes in the Brazilian Cerrado

Currently, Brazil is the largest producer and exporter of soybeans in the world. Most of this cultivation is concentrated in the Cerrado region, which has soils with low boron levels. Boron performs functions that are directly linked to plant performance. The objective of this study was to evaluate the agronomic performance of soybean cultivars with and without boron fertilization. Two field experiments were carried out in the agricultural years 2018/2019 and 2019/2020. Each experiment was carried out in a randomized block design with four replications and 10 soybean cultivars (Desafio, Foco, Bonus, Maracaí, 7067, 7110, 7739, 8372, 7100, and Population). Boron fertilization was carried out at the V3 stage of the crop using ulexite (10% of boron) at a rate of 3194 kg ha−1 and 0.0 kg ha−1 of B. The application of boron to the soil increased plant height, pod insertion height, number of branches, main stem diameter, and number of pods per plant, in addition to increasing the cycle of these cultivars. The cultivars 7110, 7739 and Desafio did not statistically differ in terms of grain yield in response to boron fertilization. The cultivars Foco, Bonus, Maracaí, 7067, 8372, 7100, and Population responded favorably to this fertilization. Furthermore, genetic breeding programs incorporate advanced strategies, such as the use of boron fertilization, in order to improve the performance of the selected genotypes. Implementing boron fertilization as an integral part of breeding programs helps not only to achieve high-yielding cultivars but also to optimize key agronomic traits. This integrated approach not only boosts breeding research but also provides a solid basis for sustainable and efficient agricultural practices.

Efficient agricultural practices in Africa reduce crop water footprint despite climate change, but rely on blue water resources

Alarming projections of climate change, decline in crop yields, and increased food demand constitute daunting threats to African food production and sustainable water management. Here, we map this complex water-food nexus by combining gridded climate data and process-based crop modelling to quantify scenarios of crop water footprint under Representative Concentration Pathway 2.6 and Representative Concentration Pathway 6.0 for time horizons 2040, 2070 and 2100. We show that high-input agricultural management coupled with the expansion of irrigation infrastructure could generate an average reduction of water use intensity up to 64% for staple crops, but only 5% for cash crops, by 2040. Notwithstanding the positive effect of intensification, between 82 Km3 (2040) and 102 Km3 (2100) of additional blue water will be required to sustain the increased yields. Our scenarios are suited for identifying locations where crops are subject to high climate impacts and where crop production shows trade-offs between high-input management and irrigation demand.

Inoculation effects of growthpromoting bacteria on corn root architecture: influence of nitrogen levels, bacterial populations, and plant genotypes

ABSTRACT Inoculating corn with diazotrophic bacteria as growth promoters has been demonstrated to be an efficient agricultural practice in Brazil, mainly due to the root stimulation they provide to plants. This study investigates the corn (Zea mays L.) root architecture in a greenhouse assay where A. baldaniorum Sp245 and H. seropedicae ZAE94 strains were inoculated and evaluated for 22 days under two N levels: 0.6 and 6 mmol L -1 of N. Short-term bioassays were conducted to assess the plant’s response to the addition of indole-acetic acid, two bacterial populations, and two corn genotypes, utilizing image capture software WinRhizo Pro. The growth and distribution of tips, crossing, and length of fine roots were determined to be the most sensitive aspects to inoculation and indole-acetic acid induction. These responses were influenced by the genotype and the number of bacterial cells present. Biomass accumulation analyses quantified these modifications after a 22-day period. Additionally, the growth response was found to be more significant when applying the Hs-ZAE94 strain to plants fertilized with a higher dose of nitrogen (6.0 mmol L -1 ), and this response was positively correlated with bioassay data. Selected strains used as an inoculant can improve root architecture and, consequently, the N use efficiency.

Exploring the Benefits of Advanced Technologies in Fertilizer, Pesticide, and Water Management for Improved Efficiency and Yield Enhancement

The advantages of new technology in fertilizer, pesticide, and water management for enhanced productivity and yield improvement in precision agriculture are investigated in this thesis. Research of both a quantitative and qualitative nature was conducted as part of the approach in order to offer a thorough knowledge of the influence that improved technology have had on the practices of precision agriculture. The quantitative findings of the research indicate that the use of modern technological practices leads to a large increase in both productiveness and profitability. The qualitative findings emphasize the positive effects on the environment that would follow from a reduction in the use of pesticides and fertilizers, as well as an increase in the sustainability of agricultural operations. Nevertheless, the research also reveals several challenges to adoption, such as the expense involved and a resistance to changing behaviors that have been done traditionally. Getting rid of these obstacles is going to be essential if we want to realize the full potential of precision agricultural methods. In general, this thesis provides important insights into the potential of advanced technologies in fertilizer, pesticide, and water management to promote sustainable and efficient agricultural practices. Additionally, it identifies key areas for future research in this field, which is extremely helpful.

Enhancing Crop Protection and Yield through Precision Agriculture and Integrated Pest Management: A Comprehensive Review

The increasing global population has intensified the necessity for sustainable and efficient agricultural practices. One promising avenue for meeting this demand is the synergy between Precision Agriculture (PA) and Integrated Pest Management (IPM). This review paper aims to scrutinize the multifaceted relationship between PA and IPM in augmenting crop protection and yield. Utilizing a comprehensive analysis of existing literature, the study elucidates how cutting-edge technologies in PA, such as drone imaging and soil sensor networks, can be harmoniously integrated with IPM strategies. These encompass biological, chemical, and cultural tactics to manage pest populations and mitigate damage, thereby fostering an environment conducive to optimal crop growth. The review identifies that the confluence of PA and IPM not only enhances the efficiency of resource use but also mitigates the environmental footprint of agricultural activities. Moreover, we delve into case studies that demonstrate significant yield improvements and cost reductions, underscoring the economic viability of integrating PA and IPM. The findings highlight the transformative potential of marrying these two domains, suggesting that such integration could be a cornerstone in the future of sustainable agriculture. The paper concludes by outlining research gaps and proposing avenues for future studies, emphasizing the need for multi-disciplinary approaches to fully unlock the potential of this integration.

Automated Sprinkle Irrigation Advantages and Disadvantages

In light of continuous progress in agricultural technology, the rise of autonomous systems has led to the development of advanced solutions like the Sprinkler Robot. This comprehensive paper reviews the design, functionality, and applications of Sprinkler Robots in modern agriculture. It emphasizes their vital role in optimizing water distribution and crop management to tackle challenges related to water scarcity and labour-intensive irrigation practices. By examining the integration of Sprinkler Robots into current agricultural frameworks, the study delves into underlying technologies such as artificial intelligence, sensing mechanisms, and precise control. Moreover, it explores the potential environmental and economic benefits linked with the widespread adoption of Sprinkler Robots, underlining their contribution to sustainable and efficient agricultural practices. The paper also evaluates existing limitations and future research prospects, providing a comprehensive perspective on the impact of Sprinkler Robots on the future of precision agriculture.

Combining simulations and field experiments: Effects of subsoiling angle and tillage depth on soil structure and energy requirements

The study investigates the impact of subsoiling on soil structure and energy requirements, focusing on the angle of the subsoiler and the depth of tillage. Combines discrete element analysis and field experiments, various parameters related to soil structure and machinery performance were analysed. These parameters include simulation traction, energy and velocity of soil particles, soil cone index, soil surface flatness, fuel consumption, traction force, and slip rate during the field experiment. The result showed that the soil cone index reduction was influenced by both the subsoiling angle and tillage depth. Traction resistance increased with greater tillage depth and varies based on soil texture and subsoiler angle. Furthermore, fuel consumption during the subsoiling is significantly affected by the chosen tillage depth and subsoiling angle. The analysis also reveals that slip rate was influenced by soil texture, tillage depth, and subsoiler angle. However, no significant difference in soil surface flatness was observed across most tillage depths and working angles after the tillage operation. Significant correlations were observed between simulation traction with field experiment traction, simulation soil particle energy with simulation traction, and fuel consumption with field experiment traction, at a significance level of 0.05. By considering the angle of the subsoiler and the depth of tillage, practitioners can optimise soil structure, energy consumption, and machinery performance, thereby promoting sustainable and efficient agricultural practices.

A Review on Estimation of soil Macronutrients using Satellite Image Processing

Major portion of Indian economy depends on agriculture. More than 40 % of the Indian land is used for agriculture which contributes to Gross Domestic Product (GDP) of the country and also provides employ to the population of the country. Increment in the population day by day increases the requirement for the increment in the yield of the agricultural products. There are several factors which is responsible to control the agricultural product yield. Among them Nitrogen, Phosphorus and Potassium are the macronutrients are the vital elements of the soil . Due to the instability in the climatic condition of India and also due to other biotic and abiotic factors macronutrients get varies. To increase the crop yield, the perfect handling of macronutrients i.e., nitrogen, phosphorus and potassium are required. As the technology is developing day by day, the application of remote sensing in agriculture is becoming promising. The satellite images of agricultural land can be processed and analysed to provide vital information for efficient agricultural practices. This paper is the review of the present state of art in the field of satellite image processing techniques for the estimation of the macronutrients of the agricultural land. This review will promote the acceptance of hyperspectral image technology for the investigation of soil NPK.

Long term conservation agriculture impact on soil nitrogen fractions and wheat (Triticum aestivum) yield in subtropical Inceptisol

The present study was carried out at ICAR-Indian Agricultural Research Institute (IARI), New Delhi over two consecutive years (2020 and 2021) to evaluate the influence of conservation agriculture (CA) and nitrogen management strategies on wheat yield in an intensified maize (Zea mays L.)-wheat (Triticum aestivum L.)-mungbean (Vigna radiata L.) cropping system. Results indicated that grain and straw yields of wheat in CA plots were higher, reaching 4829 and 7231 kg/ha, respectively. However, these yields were statistically comparable to those obtained from conventionally tilled plots (4502 and 6611 kg/ha, respectively). Significantly higher wheat grain and straw yields were observed in plots receiving nitrogen fertilizer. Interestingly, the harvest index remained unaffected by both tillage practices and nitrogen management strategies. After two seasons of wheat cultivation, CA plots had significantly higher soil nitrogen fractions, including mineral, alkali-permanganate, potentially mineralizable, microbial biomass N and total nitrogen, compared to conventionally tilled plots. Moreover, all nitrogen-receiving treatments demonstrated significantly higher nitrogen fractions at both soil depths (0–5 and 5–15 cm) compared to the control. Notably, plots treated with urea super granules exhibited superior results in enhancing plant-available nitrogen. In conclusion, this study underscores the importance of CA and various nitrogen management strategies for increasing wheat crop yield and promoting plant-available nitrogen fractions in the soil. These findings providevaluable insights for farmers and researchers looking for sustainable and efficient agricultural practices to optimize wheat production and soil health.

Bibliometric Evaluation of Energy Efficiency in Agriculture

In recent years, the importance of energy efficiency in the agricultural sector has become increasingly apparent. As the world faces challenges such as climate change, resource scarcity, and population growth, the need for sustainable and efficient agricultural practices has intensified. Energy plays a crucial role in agricultural production, from powering machinery and irrigation systems to processing and transportation. Therefore, understanding the scientific advancements and collaborative efforts in the field of energy efficiency in agriculture is essential for devising effective strategies, promoting innovation, and achieving global sustainability goals. The aim of this study is to provide insight into and a comprehensive overview of global publications on energy efficiency in agriculture and examine its scientific productivity. The paper explores the research areas and trending topics within the field, as well as the extent of collaboration among authors, institutions, and countries involved in this scientific domain. This investigation is crucial in order to address the current energy shocks and the numerous problems they generate, highlighting the importance of a holistic approach and the need for multiple regions to work together. Only by offering rapid and viable solutions can we effectively overcome this situation.

Development of Smart Farming Technology on Ginger Plants in Padamulya Ciamis Village, West Java, Indonesia

In this paper, we present a comprehensive study aimed at enhancing the cultivation of ginger plants through the integration of smart farming technology. Ginger (Zingiber officinale) is an essential crop in the agriculture-based economy of Indonesia, providing numerous health benefits and culinary applications. However, traditional farming methods often face challenges such as inefficiencies, resource wastage, and unpredictable yields. The research conducted in Padamulya Ciamis Village seeks to address these issues by harnessing the potential of smart farming technology. The study involves the implementation of cutting-edge agricultural tools, including Internet of Things (IoT) devices, sensor networks, and data analytics. By utilizing these advancements, the project aims to optimize the cultivation process, ensure sustainable resource management, and enhance overall productivity. The methodology of the research encompasses a mix of experimental trials, data collection, and analysis. Smart sensors are deployed to monitor critical variables such as soil moisture, temperature, humidity, and light intensity, enabling farmers to gain real-time insights into their ginger fields. The collected data is processed using machine learning algorithms, providing predictive models and personalized recommendations for cultivation practices. The results of this study demonstrate promising advancements in ginger farming practices. By implementing smart farming technology, farmers in Padamulya Ciamis Village experience optimized irrigation schedules, precise nutrient delivery, and timely pest control measures, leading to increased crop yields and improved quality. Furthermore, resource utilization efficiency is enhanced, minimizing water and fertilizer wastage, contributing to the sustainable and eco-friendly management of ginger plantations. Beyond its local implications, this research showcases the potential of smart farming technology as a transformative force in agriculture. The findings serve as a foundation for scaling up similar projects in other regions of Indonesia and beyond, contributing to the nation's agricultural modernization and food security. Finally, the development of smart farming technology on ginger plants in Padamulya Ciamis Village presents a promising pathway towards sustainable and efficient agricultural practices. By combining traditional farming knowledge with cutting-edge technology, this study exemplifies how smart farming can elevate crop cultivation, empower farmers, and foster rural development in Indonesia.

Glass-based LED system for indoor horticulture: enhanced plant growth through Sm3+ and Tm3+ co-doped luminescent glasses.

This study addresses the challenges of sustainable and efficient agricultural practices in the face of climate change and the destruction of agricultural lands by presenting the development of a novel plant growth LED based on Sm3+ and Tm3+ co-doped luminescent glasses with color-converting properties that emit blue and red light, resulting in an increased rate of photosynthesis and density of photosynthetically active radiation reaching the harvesting pigments. The developed LED exhibits photoluminescence (PL) peak positions ranging from 454 to 648 nm, with a spectral coverage of 50% and 39% of the absorption regions of chlorophyll a and chlorophyll b, respectively, resulting in an impressive 56% photoluminescence quantum yield (PLQY). Furthermore, the developed plant growth LED demonstrates robust performance, remaining unaffected by temperature cycles and extended operation periods. Using Romaine lettuce cultivated under identical conditions, a comparative study between the developed LED and commercially available plant growth LED is conducted, with the designed LED showing significant improvements in plant growth characteristics, including increased plant height, weight, number of leaves, and enhanced levels of chlorophyll a, chlorophyll b, and carotenoid content, while the root diameter is reduced, and the shoot-to-root ratio is diminished in comparison to the commercially available plant growth LED. The paper also compares the performance of Sm3+ and Tm3+ co-doped luminescent glass-based plant growth LED with other reported plant growth LED designs using different luminescent materials, exploring the impact of PLQY, PL position, and plant growing conditions. The results suggest that the developed LED system offers a more efficient and sustainable way of lighting for indoor horticulture and has significant implications for meeting the increasing food demands of the growing world population.