Conventional Farming Methods Research Articles

Subtropical region tea tree LAI estimation integrating vegetation indices and texture features derived from UAV multispectral images

Monitoring perennial tea tree leaf area index (LAI) provides essential insights into crop growth, and unmanned aerial vehicle (UAV) -based LAI estimation has recently proven effective across various crops. In subtropical regions, the conventional farming method (CFM), involves intensive irrigation and fertilization, and the agroecological farming method (AFM) emphasizes environmentally sustainable practices. This research employs machine learning (ML) to analyze thirty-four vegetation indices (VIs) and eight texture features (TFs) derived from UAV imagery, alongside in situ LAI measurements, to estimate tea tree LAI under CFM and AFM. Additionally, minimum redundancy-maximum relevance analysis was utilized to assess the mutual information of features for regression analysis using Polynomial Regression (PR), Ridge Regression, Decision Tree Regression, and Random Forest Regression (RFR) models. The results showed that: (1) AFM had higher in situ LAI values (mean=4.323, SD=1.594) compared to CFM (mean=3.901, SD=1.816), with less seasonal variation, mainly attributed to agronomic practices like harvesting and winter pruning. (2) Optimal image features for LAI estimation were identified by extracting pixel-based features from tea tree regions to enhance the correlation between LAI and imagery. (3) Combining VIs and TFs improved LAI estimation accuracy. The RFR model, using two VIs and one TF, achieved R²=0.710 and RMSE=1.357 for CFM, while the PR model, incorporating nine VIs and 10 TFs, had R²=0.540 and RMSE=1.283 for AFM. This study demonstrates that ML techniques are promising for estimating tea tree LAI using UAV-based multispectral imagery, offering practical guidelines for efficient field management.

Concentration and non-carcinogenic risk assessment of heavy metals in leafy vegetables among different farming practices

ABSTRACT Accumulation of heavy metals in vegetables grown using different farming methods, particularly when these metals poses significant health risks. This study investigates the levels of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), lead (Pb), mercury (Hg), nickel (Ni) and antimony (Sb), in leafy vegetables (coriander, fenugreek, and mint) grown using three different farming methods, i.e. conventional, organic, and hydroponic farming methods. Non-carcinogenic health risks were assessed using the estimated metal daily intake (MDI) and target hazard quotient (HQ) for each metal. The cumulative non-carcinogenic hazard posed by exposure to these heavy metals was further evaluated by determining the health risk index (HI). The heavy metal concentrations in conventionally, organically and hydroponically grown vegetables ranged from 0.01 to 4.49 mg per kg, 0.01 to 1.95 mg per kg, and 0.01 to 3.56 mg per kg respectively, which revealed that leafy vegetables grown conventionally and hydroponically contain significantly higher concentrations of these heavy metals compared to those grown organically grown leafy vegetables. The HI value of coriander, fenugreek and mint leaves for all the studied metals range from 0.17 to 0.35 in organic, 0.94 to 1.96 in conventional and 0.73 to 1.49 in hydroponic leafy vegetables respectively, which suggested that the intake of conventionally and hydroponically grown can expose adults and children to adverse non-carcinogenic health effects. The results emphasise the importance of stringent monitoring of agricultural inputs and water quality across all farming methods to mitigate heavy metal contamination and ensure food safety.

Machine learning-based analysis of nutrient and water uptake in hydroponically grown soybeans

Recent advancements in sustainable agriculture have spurred interest in hydroponics as an alternative to conventional farming methods. However, the lack of data-driven approaches in hydroponic growth presents a significant challenge. This study addresses this gap by varying nitrogen, magnesium, and potassium concentrations in hydroponically grown soybeans and conducting essential nutrient profiling across the growth cycle. Statistical techniques like Linear Interpolation are employed to interpolate nutrient data and a feature selection pipeline consisting of chi-squared testing methods, Linear Regression with Recursive Feature Elimination (RFE) and ExtraTreesClassifier have been used to select important nutrients for predicting water uptake using non-parametric regression methods. For different nutrient growth media, i.e. for soybeans grown in Hoagland + Nitrogen and Hoagland + Magnesium media, the Random Forest regressor outperformed other methods in predicting water uptake, achieving testing Mean Squared Error (MSE) scores of 24.55 (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} score 0.63) and 8.23 (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} score 0.81), respectively. Similarly, for soybeans grown in Hoagland + Potassium media, Support Vector Regression demonstrated superior performance with a testing MSE of 4.37 and R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{R}}^{2}$$\\end{document} score of 0.85. SHapley Additive exPlanations (SHAP) values are examined in each case to elucidate the contributions of individual nutrients to water uptake predictions. This research aims to provide data-driven insights to optimize hydroponic practices for sustainable food production.

Nano Management Techniques for Soil Reclamation

The application of nano-scale management strategies for soil remediation presents a promising avenue for tackling soil salinity issues and bolstering agricultural sustainability. Nano-sized materials like gypsum, biochar, sulphur, and zinc offer novel solutions by capitalizing on their distinct properties to alleviate soil stressors and bolster soil vitality. These nano materials demonstrate heightened reactivity, mobility, and efficacy compared to traditional alternatives, facilitating precise delivery and enduring effects on soil characteristics and crop performance. Through targeted ion displacement, soil structure enhancement, and improved nutrient accessibility, nano management approaches play a pivotal role in rehabilitating saline and sodic soils, thereby fostering agricultural resilience in adverse environmental conditions. Moreover, the integration of nano technologies in farming practices holds considerable promise for advancing precision agriculture, optimizing resource utilization, and mitigating ecological concerns associated with conventional farming methods. As research in this domain advances, ongoing exploration and refinement of nano management techniques are imperative to ensure their effectiveness, safety, and long-term sustainability, thereby contributing to the establishment of robust and productive agricultural systems.

The role of conservation agriculture in enhancing biodiversity and common beans productivity

Conservation of biodiversity in agricultural landscapes is vital for maintaining ecosystem stability and resilience. Conservation agriculture, a sustainable farming approach that involves minimum tillage and reduced use of synthetic pesticides, herbicides, and growth promoters, has gained attention as a potential strategy to enhance biodiversity and reduce production costs. This study investigated the role of conservation agriculture in enhancing biodiversity and common bean productivity in Tanzania. The study compared plots practicing conservation agriculture (minimum tillage and reduced agrochemical use) with those using conventional farming methods. Biodiversity metrics, that is, earthworm populations, bird species richness, bird nest abundance, bird species diversity, bee visitation rates, and bean yield were assessed. The results showed significant positive effects of conservation agriculture on biodiversity, emphasizing its potential role in promoting sustainable agriculture, biodiversity conservation, and household improvement due to reduced production costs and increased yield per unit area. Earthworms per meter square observed to increase from 12 in the non-conservation plots to 34 in the conservation plots, which is likely to improve nutrient cycling and productivity. Furthermore, conservation plots had a higher number of bird species flying around (36−76) compared with 14–32 in non-conservation plots (4000 m2) at 50 % bean flowering. The number of bird nests observed in the two plots during bean harvesting varied significantly (P<0.05), where the number of bird nests observed in 4000 m2 of non-conservation plots ranged from 14 to 36, compared to 12–62 in conservation plots. High bird nest numbers in conservation plots indicated higher bird habitat quality in conservation plots than in non-conservation plots. Bees as chief pollinators, bees visiting conservation plots during 50 % flowering per square meter ranged from 5 to 11, compared to 2–6 in non-conservation plots. Bee visitation to bean flowers has a significant influence on productivity, as it influences pollen transfer and fertilization. Common beans yield among plots ranged 0.9 tons/ha to 1.4 tons/ha in conservation plots compared to 0.35–1.01 tons/ha, in non-conservation plots. Land use conservation contributes considerably to economic growth through increased productivity and reduced production costs, where the production cost is reduced by 50 % in conservation plots compared with non-conservation plots. However, the present study was conducted in a single location with smallholder farmers; hence, further experiments over larger areas and longer time periods are needed for a more robust conclusion.

Varying Light Intensities Affect Lettuce Growth and Physiology in Controlled Indoor Environments

Agriculture in controlled environments has gained popularity over time. Compared to traditional agriculture, controlled environments emerge as an alternative to mitigate the negative impacts of conventional farming methods. However, controlled environment agriculture, particularly plant factories with artificial lighting, incurs higher electricity costs, primarily for supplemental lighting and dehumidification of the cultivation area. Given these high costs, it is crucial to understand how efficiently plants utilize available light to convert it into biomass. This understanding can be used to design lighting strategies to reduce electricity usage. In this study, we cultivated ‘Rex’ lettuce (Lactuca sativa) plants on a soilless substrate and used an ebb-and-flow system for irrigation and fertilization. Plants grew in varying photosynthetic photon flux density (PPFD) levels ranging from 125 to 375 µmol·m−2·s−1 and were assessed for various physiological responses. Our findings revealed that plants exposed to higher light levels exhibited greater final dry weight, increased photosynthetic activity, higher water use efficiency, and accelerated growth compared to those under lower light conditions. Notably, plants subjected to higher light intensities did not show a significant increase in transpiration, suggesting a potential trade-off between energy expenditure on supplemental lighting and dehumidification. This finding opens the possibility of reducing energy consumption for dehumidification and achieving economic savings by subjecting plants to optimal growing conditions for shorter durations. This depends on whether higher savings on dehumidification are achieved compared to the energy required to maintain high PPFD levels.

Crop Nutrition and Soil Fertility Management in Organic Potato Production Systems

Organic farming is a comprehensive production management system that fosters and improves the health of agroecosystems, encompassing biodiversity, biological cycles, and soil biological activity. The potato (Solanum tuberosum L.) is a crucial crop in organic farming systems, standing out as one of the most highly demanded organic products on the market. Among all crops, with potatoes, there is a very large yield gap between organic and conventional systems, attributable mainly to its intensive nutrient demands. The present review, considering the most relevant scientific literature worldwide, discusses the contemporary state of knowledge on crop nutrition and soil fertility management in organic potato crop production, analyzing the effects of animal manures, green manures, organic amendments, and biostimulants on organic potato tuber yield and quality. Overall, the main findings show a particular combination is needed to effectively maintain good soil fertility, satisfy the nutritional needs of the crop, and overcome the difference in potato yield between organic and conventional farming methods while meeting consumer demand. This combination entails using an animal manure or leguminous green manure with an organic soil amendment, and even better with a biofertilizer, such as a mycorrhizae-fungus-based one. It also emerged that more targeted studies are needed to select appropriate cultivars for organic potato farming systems to optimize this environmentally friendly production method.

Drone Technology for Precision Agriculture: Advancements and Optimization Strategies

This paper delves into the development and optimization of an intelligent agricultural monitoring system that makes use of drone technology to enhance agricultural productivity and sustainability. With the world's population on the rise and the scarcity of arable land, precision agriculture becomes essential in guaranteeing food security. By harnessing state-of-the-art sensors and imaging technologies, drone technology offers a new and innovative approach to agricultural monitoring. Efficiently collecting vital data on vegetation indices, soil moisture, and crop health. This study seeks to create a state-of-the-art drone-based monitoring system that uses advanced machine learning algorithms and data processing technologies to analyze agricultural data with exceptional precision. It explores the most efficient methods for operating drones, including flight planning and data collection protocols, with the aim of creating a comprehensive agricultural monitoring system. This platform provides the convenience of automating monitoring processes, leading to lower labor costs, improved resource allocation, and a beneficial impact on agricultural modernization. The article delves into the effects of the latest developments in drone technology and the subsequent decrease in costs on the agriculture industry worldwide. It highlights the ways in which these advancements are transforming conventional farming methods.

Implementasi Sistem Hidroponik Ramah Lingkungan Menggunakan Galon dan Botol Bekas untuk Peningkatan Produksi Tanaman Sayur di Desa Balongwono

In 2023, Indonesia generated 32.035 million tons of waste per year, with 49.6% coming from household waste (SIPSN, 2023). Balongwono Village in Mojokerto Regency, East Java, also faces waste problems and is an agricultural area with the majority of the population working as farmers. Vegetable production in this village is often suboptimal due to limited land and conventional farming methods. To overcome this problem, a socialization on the benefits and basic techniques of hydroponics was conducted, training on making hydroponic installations from used items, and mentoring to ensure the success of the system. The hydroponic system that utilizes used gallons and bottles as growing media aims to increase vegetable production in Balongwono Village. The results show that plants such as kale and pakcoy grow faster and fresher than conventional methods. In addition to increasing yields, this system also reduces plastic waste and lowers production costs. The implementation of this hydroponic method provides double benefits: increasing vegetable production and reducing waste. This method can be a reference for other villages with similar problems, contributing to environmental sustainability at large.

Smart agriculture technology: An integrated framework of renewable energy resources, IoT-based energy management, and precision robotics

Modern agricultural practices encounter challenges related to operational efficiency and environmental effects. This prompts a demand for innovative solutions to foster sustainability in farming while emphasizing the limitations of conventional farming methods. To address these challenges in modern agriculture systems, this research proposes a comprehensive framework for smart farming. The proposed framework comprises of three technology integrations: 1) an efficient integration of renewable energy resources (RERs) with solar panels and battery energy storage systems (BESS), 2) an IoT-based environmental monitoring for precision irrigation, and 3) an android application-controlled precision robotic system for targeted chemical application. The proposed framework investigates a case study on Sharjah, United Arab Emirates (UAE) to explore and analyze optimal scenarios of multiple energy resources. Results demonstrate successful cross-prototype integration through the Blynk IoT platform providing users with a unified interface. Furthermore, the results provide a comprehensive analysis and investigation into the interactions between RERs and the grid across various combinations. The findings indicate the potential of this framework to revolutionize agriculture and thus offer a sustainable, efficient, and technologically advanced approach. It also represents the contribution of a complete solution to modern agricultural challenges presenting tangible results for a promising future in smart and sustainable farming practices.

Performance and Sustainability of Organic and Conventional Cotton Farming Systems in Egypt: An Environmental and Energy Assessment

Cotton cultivation is resource-intensive, posing significant environmental challenges, especially with conventional farming methods. Growing interest in sustainable agriculture drives the exploration of organic farming as a potential alternative with lower environmental impacts. Despite its benefits, organic farming often faces criticism for lower crop yields, sparking debates on the trade-offs between productivity and environmental impact. This study hypothesizes that organic cotton farming will have a smaller environmental footprint and higher energy efficiency compared to conventional methods. To test this hypothesis, a cradle-to-farm gate energy analysis and life cycle assessment (LCA) were conducted on both organic and conventional seed cotton production systems in the Beheira governorate of Egypt. The ReCiPe 2016 midpoint and endpoint characterization model was used for an environmental impact assessment. The impacts were evaluated using two functional units: one ton of seed cotton and one hectare of cultivated cotton. The findings revealed that organic cotton outperforms conventional cotton in net energy gain, efficiency, and profitability, with higher productivity and lower energy intensity. Regardless of the functional unit used (mass- or land-based), the assessed organic systems generally show a better environmental performance than the conventional systems in the local context, even when accounting for data uncertainty. This is due to lower input intensity and the use of less energy-intensive organic fertilizers and bio-fertilizers. Fertilization and irrigation are key factors influencing environmental impacts, with fertilization affecting midpoint impacts and irrigation affecting endpoint impacts. Therefore, precision fertilization, efficient irrigation practices, and effective nutrient and soil moisture management are recommended to minimize environmental impacts. Subsequent studies could explore whether similar patterns are observed in different geographic regions and evaluate additional social and economic aspects of cotton sustainability beyond environmental impacts. Future agricultural LCAs should use both mass-based and area-based functional units to capture a broader range of environmental effects and evaluate the co-benefits and trade-offs between organic and conventional practices.

Utility of AI Based Soil Testing in Agriculture

ABSTRACT The soil plays crucial role in any kind of agricultural crops. Depending upon the different ingredients of the soil the nature of crops to be farmed is decided.Optimizing agricultural practices is crucial for countries like India to address economic growth and escalating food demands. Historical challenges stemming from diverse weather conditions, geographical variations, conventional farming methods, and economic uncertainties have hindered improved crop yields. The dearth of information on crop yield productivity has also resulted in significant economic losses. In response, the adoption of advanced agricultural technologies such as smart farming, digital agriculture, and Data Analytics has become imperative. These innovations provide valuable insights into factors influencing crop yields, best-fit crops for a certain area, and enabling accurate predictions. With precise crop yield forecasts, farmers can develop tailored cultivation plans, implement efficient crop health monitoring systems, and manage yields effectively, thereby transforming agriculture into a highly profitable venture. This paper delves into the applications of technological advancements in agriculture, focusing on areas such as Digital Agriculture, Crop Management, and Crop Protection, and Data Analytics, with the aim of contributing to the optimization of agricultural practices and bolstering the economic impact of the agricultural sector. KEYWORDS: Agriculture, Artificial Intelligence, Machine learning, crop prediction

Sustainable rural economy and food security: An integrated approach to the circular agricultural model

The lack of resources and increasing damage are major obstacles to sustainable economic progress. Conventional farming methods, known for their energy consumption and resulting pollution, need changes. This study focuses on agriculture, an approach inspired by natural ecological cycles that aim to promote sustainable development in rural areas through efficient resource reuse. The connection between agriculture and rural economies, along with strategies for their combined advancement, is an area that requires more exploration and thorough empirical analysis. The research involves an energy analysis methodology tailored for use in regions to assess the benefits and environmental impacts of implementing circular agriculture for sustainable rural economic growth. A multi-objective decision-making approach using TOPSIS is employed to enhance agricultural policy formulation and decision-making processes. The study concludes by developing a model that considers production, processing, recycling, and socioeconomic aspects to delve into the development of circular agriculture within rural communities and propose relevant strategies. The uniqueness of this research lies in its combination of agriculture and the coordinated advancement of rural economies, which offers a fresh viewpoint and approach to sustainable farming methods and robust rural economic development. The results contribute to the circular agriculture literature body and provide practical guidance for policymakers and farmers.

Comparison of Chemical Constituents of Some Plant Species of Lamiaceae Family Grown Conventionally and Organically

In this study, the chemical constituents of Origanum onites, Salvia officinalis and Lavandula officinalis plants belonging to Lamiaceae family grown by conventional and organic methods were determined by soxhlet extraction. Gas Chromatography-Mass Spectrometry (GC-MS) method was used to determine the chemical constituents of conventionally and organically grown plant samples. When the results of the analyses were examined, out of 97 chemical components of Salvia officinalis plant, 50 components were determined from plants grown with conventional farming methods and 78 components were determined from plants grown with organic farming methods. Of the 38 components detected in Origanum onites, 36 components were determined from plants grown with conventional farming methods and 38 components were determined from plants grown with organic farming methods. Out of 60 components of Lavandula officinalis, 40 components were determined from plants grown with conventional farming methods and 60 components were determined from plants grown with organic farming methods. In terms of chemical structure, the components are divided into different classes as hydrocarbons, monoterpenes, monoterpenoids, sesquiterpenes, sesquiterpenoids, diterpenes, diterpenoids, fatty acids and derivatives, and phenolic compounds.

Untargeted foodomics for authenticating the organic farming of water spinach (Ipomoea aquatica)

This study aimed to conduct a comprehensive analysis of the primary and secondary metabolites of water spinach (Ipomoea aquatica) using hydrophilic interaction liquid chromatography coupled with Orbitrap high-resolution mass spectrometry (HILIC-Orbitrap-HRMS). Certified samples from two cultivars, Green stem water spinach (G) and White stem water spinach (W) cultivated using organic and conventional farming methods, were collected from the Hong Kong market. Multivariate analysis was used to differentiate water spinach of different cultivars and farming methods. We identified 12 metabolites to distinguish between G and W, 26 metabolites to identify G from organic farming and 8 metabolites to identify W from organic farming. Then, two metabolites, isorhamnetin and jasmonic acid, have been proposed to serve as biomarkers for organic farming (in both G and W). Our foodomics findings provide useful tools for improving the crop performance of water spinach under abiotic/biotic stressesand authentication of organic produce.

Agricultural Decision Support System

Abstract: In India, agriculture stands as the backbone of the economy, yet farmers encounter persistent challenges rooted in conventional farming methods that result in suboptimal yields and environmental degradation. To address these issues, a transformative shift towards precision agriculture is imperative. Our Agricultural Decision Support System (ADSS) represents a pioneering initiative harnessing advanced machine learning algorithms to confront these challenges head-on. By scrutinizing soil characteristics alongside critical weather parameters encompassing temperature, humidity, pH levels, and precipitation, our system furnishes tailored recommendations for crop selection and optimal fertilizer application. Through the implementation of our innovative solution, farmers stand poised to diversify their crop cultivation, augment yields, and pave the way for sustainable agricultural practices. The integration of our system offers farmers the opportunity to explore new crop varieties, potentially leading to heightened profitability and the mitigation of soil pollution concerns.

An assessment of Organic and Conventional Farming Practices for Yield, Pest Management and Soil Health

This study contrasts and compares conventional and organic farming. The purpose of the study is to assess the effectiveness and impact of each practice before deciding on the best crop-growing approach. Despite their agricultural systems can be categorized as conventional or sustainable depending on the techniques employed. Organic farming which cultivates a range of crops without the use of synthetic manures, the agricultural method that is most sustainable. While producing enough food, organic farming improves soil composition and supports biodiversity by relying on ecosystem services. The review compares the long-term effects of conventional and alternative organic farming practices on yield in which in short term organic yields were found to be 19.2% lower than conventional yield while in long term yields were lower in conventional systems by 31% and 50%, respectively, due to the long-term improvement in soil health. In soil health point of view long term practices of organic farming improves physical, chemical and biological properties of soil by increase soil carbon stock by 12 %, more nutrient release and microbial activity. Pest management is effectively controlled by crop rotation, increase beneficial population of pest in organic farming which reduce the dependency on chemical pesticides and maintain the sustainability of soil health and ecology. As a result, organic farming methods are being evaluated in a large number of research worldwide as an alternative due to their better physical, chemical and biological qualities than conventional farming methods.

Biotechnological Breakthroughs: Paving the Path to Cleaner and More Productive Agriculture through Bioremediation

In recent years, biotechnological advancements have played a pivotal role in transforming traditional agricultural practices into sustainable and environmentally conscious systems. This article explores the revolutionary concept of bioremediation and its potential to address soil pollution, enhance crop productivity, and mitigate the environmental impact of conventional farming methods. Bioremediation involves harnessing the power of microorganisms, plants, or enzymes to detoxify or remove pollutants from soil, presenting a promising solution to the challenges posed by agrochemicals and industrial wastes. The first section of this abstract delves into the current state of agriculture, highlighting the widespread use of chemical fertilizers, pesticides, and herbicides, which has led to soil degradation, water contamination, and loss of biodiversity. These issues necessitate a paradigm shift towards sustainable agricultural practices that prioritize soil health and ecosystem balance. The second section explores the fundamental principles of bioremediation, emphasizing its versatility in addressing a wide range of pollutants, including heavy metals, pesticides, and organic contaminants. Through the use of genetically engineered microorganisms or naturally occurring plant-microbe partnerships, bioremediation offers a targeted and eco-friendly approach to soil restoration. Moving forward, the paper examines specific biotechnological breakthroughs that have propelled the field of bioremediation. From the development of genetically modified crops with enhanced phytoremediation capabilities to the use of engineered microbes for efficient pollutant degradation, these innovations showcase the potential of biotechnology in revolutionizing agriculture. Furthermore, the paper explores the economic and environmental benefits associated with the adoption of bioremediation techniques in agriculture. By reducing the reliance on synthetic chemicals, farmers can lower production costs, while simultaneously preserving soil fertility and safeguarding water resources. The potential for increased crop yields and improved food quality further underscores the positive impact of bioremediation on agricultural productivity.

A Survey Paper On: Uplifting Farmers with Mobile Applications: An Overview of Modern Agricultural Utilities

Indian agriculture although it forms the foundation of the nation’s economy, the sector nevertheless faces a number of challenges. One of the major problems is that farmers lack access to markets and knowledge. Therefore, a mobile app may be essential in resolving this problem. The design and development of this software are intended to address the challenges faced by Indian farmers while purchasing seeds and fertilizer. An Android-based application powers it. In this paper, we look at the agricultural technology options that are already available, think about the challenges that Indian farmers face, and highlight some potential benefits from this Android app. An androidbased approach that could fundamentally alter how Indian farmers get seeds, fertilizer, and essential agricultural knowledge is provided in this paper. The ”App- Based Implementation of Modern Agriculture Utilities in Android” intends to close the technological and agricultural gap between conventional farming methods. by giving farmers simple access to essential resources and equipment via their smartphones.

A STUDY ON HYDROPHONIC FARMING

Hydroponics technology is possibly the most intensive and a versatile method of growing crops production at present as it allows optimum utilization of nutrient solution, water and space, as well as a better control of climate and plant protection factors. Hydroponic technology can be an efficient mean for food production from extreme environmental ecosystems such as deserts, mountainous regions, or arctic communities. Furthermore, hydroponics production increases the quality of crops and its productivity, which results in higher competitiveness and economic income. Several types of hydroponics systems can be used to grow the crops. Commercially Nutrient Film Technique (NFT) has been used across a globe for successful production of the leafy as well as other exotic vegetables cultivation. Hydroponic systems use mineral nutrient solutions to feed the plants in water of using several non-soil growing media. Despite of having few disadvantages, Hydroponics technology provides variety of benefits when compared with conventional farming methods Hydroponic farming is an innovative soil-less cultivation method that has gained significant attention in recent years due to its potential for sustainable and efficient food production..