Agricultural Output Research Articles

Understanding human–aquifer interactions using fuzzy cognitive mapping approach at the Palas Basin

Abstract The complex relationships between social and environmental systems create challenges for environmental sustainability. In this study, we used the fuzzy cognitive mapping approach to investigate human–aquifer interactions at the Palas Basin (Kayseri, Türkiye). Cognitive maps are casual maps that reflect the perceptions and understandings of different people/groups. With this approach, we aimed to reveal how goals, perceptions, and understandings of water users/managers affect the groundwater system in the Palas Basin. The cognitive maps were drawn with farmers and state officials and transformed into adjacency matrices for further analyses. Graph theory indices (indegree, outdegree, centrality, density, complexity, hierarchy index) were calculated to characterize maps. The maps of farmers and state officials were compared using the Mann–Whitney U test. Social cognitive maps were prepared by combining individual cognitive maps. Neural network computational method was used to reveal the response of the system to different management scenarios. This study pointed to the importance of irrigation/irrigated agriculture is for the Palas Basin. Increasing agricultural yields and profits by expanding the cultivation of high water consuming crops and irrigation/fertilization was found to be the priority of people. Both farmers and state officials think that groundwater levels go down due to extensive pumping in the basin. However, farmers are still more focused on short-term economic gains rather than the sustainability of the agricultural system. Fuzzy cognitive mapping approach provided an effective tool for revealing the understandings of water users/managers about the groundwater system. The information obtained in this study could be used in the future for groundwater management in the Palas Basin.

Developing a Neural Network Based Microwave Sensing System for Accurate Salinity Prediction in Water

Abstract High and low salinity levels play a crucial role in the vitality of organisms and affect natural ecosystems, agricultural yields and human health. To mitigate the risks associated with high blood pressure and cardiovascular diseases, the World Health Organization (WHO) advocates reducing salt consumption among adults, suggesting an intake of no more than 5 g daily. In this study, a non-invasive microwave (MW) sensing approach, that is augmented by deep neural network (DNN) models is proposed to predict salinity levels. The MW detection measurement system, including a Horn antenna, has been developed to evaluate the salt content in bottled spring waters (BSWs). The system with DNN model provides a novel solution for real-time water quality monitoring. The input and output dataset for DNN model were generated using four different BSWs, each with a salt content ranging from 0 to 32 g and increased by 1 g. The developed DNN model, designed with six fully connected layers, uses reflection coefficients (RCs) as input dataset to predict salt content in grams accurately. The accuracy performance of the DNN model in various bandwidths was evaluated by dividing the 1–13 GHz range into 78 different bands and the lowest error rate was found to be in the 1–8 GHz bandwidth (2.18%). Furthermore, each BSW was measured five times, and the performance of the model was evaluated according to the number of measurements. In three or more measurements, the model demonstrated notable improvement(15.3%) in predicting salt content.

A Novel Agricultural Remote Sensing Drought Index (ARSDI) for high-resolution drought assessment in Africa using Sentinel and Landsat data.

Drought poses a significant threat to agricultural productivity in Africa due to climate variability and water scarcity. To improve drought monitoring using high-resolution remote sensing data, this paper proposes a novel Agricultural Remote Sensing Drought Index (ARSDI). Imagery from Sentinel-1(S-1), Sentinel-2 (S-2), and Landsat 8 was utilized to develop a drought monitoring system for 2017-2023, during which Egypt and Kenya experienced significant drought events. In Kenya, particularly from 2021 to 2023, recurrent droughts severely affected agricultural output, necessitating the evaluation of ARSDI's efficiency in capturing drought severity. Similarly, in Egypt, drought-like conditions linked to reduced Nile River flow and rainfall variability posed significant challenges. ARSDI exhibited strong correlations with traditional drought indicators, such as the Soil Moisture Condition Index (SMCI), ranging from 0.64 to 0.88 in Egypt and 0.74 to 0.85 in Kenya. It also correlated well with the Vegetation Health Index (VHI), with values (0.96 to 0.98) in Egypt and (0.53 to 0.98) in Kenya. Furthermore, ARSDI aligned with Standardized Precipitation Index (SPI), ranging (0.44 to 0.71) in Egypt and (0.47 to 0.60) in Kenya. By integrating Sentinel-1 radar data, ARSDI mitigated the limitations of cloud cover, providing more reliable vegetation monitoring. Compared to other indices, such as the Normalized Difference Vegetation Index (NDVI), ARSDI exhibited superior performance, particularly during the drought events 2019 in Egypt and 2020 in Kenya, demonstrating its robustness in assessing soil moisture and vegetation health.

Biofortification and fortification of wheat flour: Qualitative analysis for implementation and acceptance.

This paper comprehensively investigates various aspects of dietary behaviors relating to the usage of wheat flour and sociocultural preferences embedded within rural communities and aims to bridge health gaps resulting from zinc deficiency by introducing zinc bio-fortified and fortified flour in Pakistan. A household and a market study was conducted in Ghotki and Tando Muhammad Khan districts in Sindh, Pakistan. Various stakeholders involved in the wheat-flour industry including farmers, seed suppliers, owners of atta-chakkis and flour mills, grocers and flour merchants, bakers and hoteliers, consumers, and agronomists were interviewed to gauge their knowledge of bio-fortified and fortified wheat-flour. Wheat-flour is a staple food item in Pakistan, however, agricultural output in Pakistan varies across all provinces. Factors that hinder agricultural productivity include a shortfall of essential resources such as irrigation water, superior quality seeds and fertilizers, and machinery. Farmers use primitive methods of farming as they do not have access to modern technologies, information, or training. Wheat flour market vendors and consumers lack awareness of bio-fortified and fortified wheat flour products and believe the only way to create a customer-base is by ensuring that fortified wheat products are available to all and competitively priced compared to traditional options. Additionally, participants misconstrue the process of fermentation and perceive it as unhealthy. The lack of financial resources and awareness restricts adequate promotion of nutrient-rich food products amongst stakeholders involved in the wheat flour industry. Mass awareness campaigns, education and government incentives could bridge the gaps present and encourage wider adoption of bio-fortified and fortified wheat flour. Policy makers and communities can draw on the recommendations made in this paper to introduce and promote zinc bio-fortified and zinc-fortified flour in settings where zinc deficiency is prevalent.

Factors Influencing the Productivity of Direct Energy Inputs in EU Agriculture

Agriculture is a major energy consumer and a significant contributor to global greenhouse gas emissions. As the world’s population grows, increasing food production while reducing energy use presents a critical challenge. This study examined the trends in direct energy input productivity in agriculture across European Union (EU) countries from 2010 to 2021, focusing on the impact of structural factors, including production scale, mechanization, intensity, and output composition. The results showed a gradual decline in energy productivity, averaging a 1.04% annual decrease, reaching EUR 344,000 per terajoule (TJ) in 2021. Higher mechanization and production intensity improved energy productivity, while larger production scales and a greater share of animal farming had negative effects. Given the current trends of production expansion and extensification, further progress in energy productivity in agriculture appears limited. Policy measures should prioritize optimizing animal production’s share and adopting a sustainable use of renewable energy to lower the dependency on non-renewable fossil fuel sources. Future strategies must balance high agricultural output with sustainable energy consumption per food unit.

Comprehensive Analysis of a YOLO-based Deep Learning Model for Cotton Plant Leaf Disease Detection

Diagnosis of cotton plant diseases is essential to maintain agricultural sustainability and output. This study proposes a YOLO-based deep learning model for leaf disease detection to maximize cotton plant leaf disease detection accuracy. This method ensures a comprehensive evaluation of cotton plant health by combining various image processing techniques, improving the accuracy of disease identification. This study provides a viable path to improve crop health monitoring and management in cotton farming systems and emphasizes the importance of utilizing cutting-edge image processing techniques in agricultural activities. ROC curve performance and classification metrics were better for YOLOv5 than for VGG16 and ResNet50, as it had the highest F1 score (99.21%), recall, and precision. Consistent performance in classification tests was demonstrated by all models, which showed balanced precision, recall, and F1 scores. ResNet50 marginally outperformed VGG16 in terms of true positive rates, F1 score (98.88% vs. 98.65%), recall, and precision. More sophisticated models, such as YOLOv5 and ResNet50, showed higher efficiency and accuracy than VGG16, which makes them more appropriate for applications demanding low false positive rates and high precision. The proposed YOLO-based method improves the accuracy of disease identification, ensuring a thorough assessment of cotton plant health using image processing techniques. The results show that the proposed approach is quite successful in correctly detecting and classifying a variety of diseases that affect cotton plants.

Estimation of soil erosion and risk assessment in Somalia

Soil erosion is a pressing issue that threatens environmental stability and agricultural productivity across the globe. In Somalia, where the population already contends with severe socioeconomic and environmental challenges, the effects of erosion compound problems like food insecurity and land degradation. This study investigates erosion patterns and identifies areas prone to soil loss, aiming to provide actionable insights for improving land use practices and resource management. The research employs advanced analytical tools to study how erosion factors are distributed geographically. Data such as terrain characteristics, rainfall intensity, vegetation cover, soil quality, and land usage are systematically analyzed. Cutting-edge modeling techniques, supported by historical erosion trends, help to develop accurate predictions about future soil loss, offering a robust basis for creating effective intervention strategies. The findings show that many regions in Somalia are highly susceptible to erosion, particularly where unsustainable human activities such as overgrazing, deforestation, and improper farming methods are prevalent. These insights underscore the urgent need for targeted policies to mitigate erosion and protect valuable soil resources. By forecasting potential erosion scenarios, this research equips decision-makers with the tools needed to prioritize interventions that enhance agricultural output, preserve ecosystems, and ensure long-term sustainability. This research aids policymakers, land managers, and stakeholders in formulating focused, evidence-based plans for soil conservation and sustainable land use, ultimately facilitating the preservation and enhancement of Somalia's natural resources and agricultural resilience. Int. J. Agril. Res. Innov. Tech. 14(2): 111-121, December 2024

Advanced Soil Moisture Predictive Methodology in the Maize Cultivation Region

Soil moisture is a critical determinant of the maize crop health and productivity. With over 60% of India's maize cultivation concentrated in South Indian states, accurately forecasting soil moisture is essential for optimizing irrigation and enhancing agricultural output. This study introduces an Improved Hybrid Machine Learning (IHML) model that integrates and optimizes Machine Learning (ML) models to deliver superior predictive performance. By leveraging data from key maize-growing districts in South India, the IHML model demonstrates enhanced convergence rates and accuracy compared to traditional ML approaches. The research framework is grounded in comprehensive correlation evaluations, which inform parameter selection and model architecture. Extensive comparisons reveal that the IHML model significantly outperforms individual ML models in forecasting soil moisture with higher precision. These findings highlight the potential of IHML models to advance smart farming practices and enable precise irrigation management, paving the way for improved crop yield and sustainable agriculture.

Participatory development of mobile agricultural advisory driven by behavioural determinants of adoption.

Mobile applications have the potential to revolutionise agricultural advisories, providing farmers with real-time information and insights for improved decision-making. However, the adoption of such apps is influenced by various behavioural factors, necessitating a participatory approach of development with the stakeholders. This study proposes a framework that begins with a prototype app informed by a literature review and the identification of behavioral determinants of app adoption. Iterative participatory feedback, grounded in these determinants, is employed to refine the app. The framework is demonstrated through the case study of Makara, an app providing risk advisories for farm yield, income, and risk mitigating practices in Maharashtra, India. A user-focused Theory of Change (ToC) was used to design a survey to identify socio-economic and behavioral drivers of agricultural app adoption. Data collected from 1354 farmers across four districts of Maharashtra during April-May 2023 informed a linear regression model that identified significant explanatory factors. Building on these findings, multiple feedback sessions with farmers were conducted over a year to iteratively co-develop the app's features. Key behavioral determinants, including norms, trust, abilities, and attitudes towards adopting mobile-based agricultural advisories, significantly influenced adoption. The participatory design process addressed these factors, incorporating features such as multi-lingual support, intercropping and multi-cropping options, and multi-component budgeting to enhance trust and perceived ease in using the app. User-friendliness was further improved through redundant communication of risks, combining textual and audio-visual formats. This paper presents a mixed-methods approach to integrating behavioral drivers of agricultural (advisory app) technology adoption into a participatory co-design framework (of such an app), enabling considerations for inclusivity and scaling in the design process of the app itself.

Transformative Approaches to Agricultural Sustainability: Automation, Smart Greenhouses, and AI

Agricultural sustainability is continually undermined by climate change, resource depletion, and the increasing worldwide need for food. Fundamental technologies, including automation, smart greenhouses, and artificial intelligence (AI), are changing modern agricultural methods by providing novel ways to improve sustainability in farming. This review study examines the significance of these technologies in advancing sustainable agricultural systems, particularly their effects on resource optimization, environmental conservation, and economic efficiency. Automation technologies, such as robots, drones, and autonomous vehicles, enhance farm management by enhancing efficiency and minimizing resource waste. Intelligent greenhouses, fitted with IoT sensors and temperature regulation systems, provide precise management of environmental conditions, therefore improving agricultural output while minimizing water and energy use. AI-driven technologies, including machine learning and predictive analytics, enhance crop health monitoring, pest management, and yield prediction, enabling data-informed decision-making. The research analyses the combination of various technologies, focusing their synergies in developing comprehensive smart agricultural systems that promote enduring sustainability. Despite the apparent promise, challenges like substantial initial investment, technological intricacy, and scalability persist. This review continues by addressing future directions, policy implications, and research requirements for promoting the use of these technologies to enhance global agricultural sustainability.

МОДЕЛИРОВАНИЕ МНОГОУРОВНЕВОЙ ДИНАМИКИ УРОЖАЙНОСТИ СЕЛЬСКОХОЗЯЙСТВЕННЫХ КУЛЬТУР

The objective of the study is to develop an algorithm for modeling crop yields based on the dynamic-stochastic and cyclical properties of long-term characteristic series. Tasks: identifying the properties of time series of agricultural crop bioproductivity based on their consideration as multi-level structures with cyclical fluctuations; using the properties of variability of long-term characteristic series to build forecasting or stochastic assessment models; implementing the modeling algorithm using the example of grain and leguminous crop yields in Russia and wheat bioproductivity in the USA. The object of the study is long-term variability of agricultural crop yields. An algorithm for constructing a dynamic-stochastic multilevel model for forecasting the variability of agricultural crop yields under different conditions of a combination of the state of the natural environment and technological processes, taking into account cyclical fluctuations of the characteristic, has been created. The algorithm has been implemented for modeling empirical series. When describing the long-term variability of the characteristic and assessing favorable and unfavorable events, trends, histograms of the distribution of relative frequencies of local minimum and maximum cycles, and probability distributions of yield losses and gains have been used. Multilevel modeling allows assessing the dynamics of agricultural crop bioproductivity under different conditions of producers' activities: determining losses in unfavorable conditions of the natural and technological environment, identifying yield gains in favorable situations. Using stochastic methods, it is proposed to estimate rare events corresponding to very high and very low crop yields, as well as transitions of series levels into events. Based on the identified multi-level trends and the frequency of local extreme cycles, a retrospective forecast of the characteristic with a lead time of 3 years is obtained.

Empowering Communities: A Low‐Cost Approach to Land and Water Management in Developing Countries

ABSTRACTAccess to water is fundamental for life, yet many citizens in developing countries face severe water shortages, exacerbated by recurring droughts, particularly in water‐stressed regions. Street‐level bureaucrats in these areas encounter the dual challenge of addressing local residents' expectations for relief while operating with constrained resources. This study examines the innovative efforts of local bureaucrats in Banda, a drought‐prone and underdeveloped district in the Indian state of Uttar Pradesh, to promote rainwater harvesting and water conservation. By leveraging limited discretionary funds and engaging local stakeholders, these initiatives have demonstrated significant positive impacts. Using on‐ground visits and official government data, the study evaluates the effects of these efforts on groundwater levels, agricultural productivity, and related indicators. Findings reveal substantial improvements, including higher groundwater levels, increased agricultural yields, and enhanced water availability for sanitation needs. This research highlights the pivotal role of water conservation initiatives driven by street‐level bureaucrats in fostering economic growth and environmental sustainability. The insights from Banda offer valuable lessons for policymakers and bureaucrats in similarly resource‐constrained settings in developing countries striving to ensure water access and resilience against drought.

NANOTECHNOLOGY IN AGRICULTURAL YIELD EXPANSION: A COMPEHENSIVE REVIEW

This comprehensive review explores the burgeoning field of nanotechnology and its transformative impact on agricultural yield expansion. Nanotechnology offers unprecedented opportunities to address key challenges facing global agriculture, including soil degradation, resource inefficiency, pest and disease management, and climate change adaptation. By manipulating materials at the nanoscale, researchers have developed innovative solutions to enhance soil fertility, improve crop protection, optimize resource utilization, and promote sustainable farming practices. This paper examines recent advancements in nanofertilisers, nanopesticides, nanosensors, and precision agriculture technologies, providing insights into their mechanisms of action and potential benefits for crop productivity. Nanotechnology enables the development of nanorobots and nanoscale devices for automated farming operations and precision agriculture applications. Furthermore, environmental and safety considerations, as well as regulatory frameworks governing nanotechnology in agriculture, are discussed to ensure responsible and sustainable deployment of these technologies. Through case studies and practical examples, the review underscores the transformative potential of nanotechnology in revolutionizing agricultural practices and contributing to global food security. Finally, challenges, future perspectives, and recommendations for further research and implementation are outlined to guide the continued development and adoption of nanotechnology for agricultural yield expansion.

Simulation of sugar beet yield

Mathematical simulation of agricultural crop yields is based on the physical principle of causal interactions balance in a closed physical system. The conditions for model verification are determined, allowing to obtain objective and reliable findings. It is noted that empirical equations representing the dependence of crop yields on crop-forming factors in the form of polynomials of any degree, obtained using the multiple nonlinear regression method, are valid only for the conditions of a specific field experiment. Using them, it is impossible to carry out theoretical generalizations that would allow developing these particular solutions into a generalized mathematical model. It is shown that mathematical model of yield, presented in multiplicative form, can include an unlimited number of yield-forming factors. Atmospheric precipitation was used as a factor characterizing the moisture supply for plants in case of no irrigation. The validity of the developed solution is confirmed by 13 years of data on the yield of sugar beet (NZ-type hybrid) cultivated in Belarus at the State Agricultural Institution “Molodechno Variety Testing Station”. The mathematical model of yield is presented in dimensionless form; all blocks of factors of this model are similarity criteria. This allows to compare the results of mathematical simulation of yield of any agricultural crop on soils with any agrochemical properties. Such an analysis cannot be carried out with the results of calculating yields using the formulas of private mathematical models of agricultural crop yields in the form of algebraic polynomials.

Advances in Integrated Nutrient Management Practices for Cereal Crops: A Comprehensive Review

Integrated Fertilizer Management (INM) is an agricultural practice that maximizes the use of all available fertilizer sources to maintain and improve soil fertility and crop yield. The major goal of INM is to create sustainable agricultural output through a balance of organic, inorganic, and biological nutrition sources. INM promotes soil health by recycling nutrients, using less chemical fertilizer, and combining organic manures, crop waste, biofertilizers, and mineral fertilizers. INM entails meeting crop nutrient requirements with a variety of nutrient sources, including organic manures (e.g., farmyard manure, compost), crop residues, biofertilizers (e.g., Rhizobium, Azotobacter), and chemical fertilizers. INM requires soil fertility management, crop rotation, green manuring, and balanced fertilizer application. INM focuses on increasing nutrient efficiency, maintaining soil organic matter, stimulating biological activity, and limiting environmental effect. INM helps farmers by improving soil health, increasing nutrient efficiency and crop yields, lowering environmental pollution, and maintaining economic sustainability. INM has aided many crops, including wheat, maize, barley, and rice. For wheat, INM improves grain yield, quality, and protein content. It improves maize biomass yield, nitrogen uptake, and soil fertility. It allows barley to grow quicker, produce larger grains, and withstand abiotic stress better. In rice, INM enhances tillering, grain quality, and yield. Finally, INM is a holistic method that integrates a variety of fertilizer sources to promote long-term crop production and soil health management, and it offers a feasible solution to the difficulties produced by traditional agricultural practices.

Desalination for Food Security in Tunisia: Harnessing Renewable Energy to Address Water Scarcity and Climate Change by Using ARDL Approach and VECM Technical

This study employs the Autoregressive Distributed Lag (ARDL) model to investigate the short-term and long-term effects of independent variables, including Agricultural output (A), Renewable energy consumption (REC), Non-renewable energy consumption (NREC), CO2E emissions, Climate change (CC) and Financial (FD), on food security (FS) in Tunisia during the 1990–2023 period. After confirming the stationarity of the variables and the existence of long-run cointegration, the ARDL model was employed. The short-term ARDL estimates revealed mixed results. While some variables had positive effects, others exhibited negative influences on FS. For instance, A positively impacted FS, while REC, NREC, CO2E, CC, and FD had negative effects. The long-term ARDL analysis indicates that A, NREC, CC, and FD have significant effects on FS. A and NREC positively influence FS, while CC and FD have negative impacts. REC’s effect on FS is uncertain due to its marginal significance, and CO2E shows no significant relationship with FS in the long run. This study provides valuable insights into the short-term and long-term relationships between FS and its influencing factors. The findings can inform policy decisions and future research in this area.

Digital Revolution in Agriculture: Using Predictive Models to Enhance Agricultural Performance Through Digital Technology

Digital innovation in agriculture has become a powerful force in the modern world as it revolutionizes the agricultural sector and improves the sustainability and efficacy of farming practices. In this context, the study examines the effects of digital technology, as reflected by the digital economy and society index (DESI), on key agricultural performance metrics, including agricultural output and real labor productivity per person. The paper develops a strong analytical method for quantifying these associations using predictive models, such as exponential smoothing, ARIMA, and artificial neural networks. The method fully illustrates how economic and technological components interact, including labor productivity, agricultural output, and GDP per capita. The results demonstrate that digital technologies significantly impact agricultural output and labor productivity. These findings illustrate the importance of digital transformation in modernizing and improving agriculture’s overall efficacy. The study’s conclusion highlights the necessity of integrating digital technology into agricultural policy to address productivity problems and nurture sustainable growth in the sector.

The Impact of Credit Policy Environment on Agricultural Output in Nigeria

The growth of the agriculture sector is a key factor in the survival of the Nigerian economy. However, the production and productivity of this sector have been lower than expected recently. The country is now a net importer of some crops that were previously produced in large quantities. Hence, with the aim to identify factors that influence the sector, the study specifically establishes the relationship between the agricultural sector growth proxy by the agricultural production index and the credit policy variables in Nigeria. The research utilized the flexible autoregressive distributed lag (ARDL) approach to determine the presence of co-integration. The estimated long-run and short-run models demonstrated the best, most efficient, and unbiasedness qualities. The findings revealed that loans from commercial banks and the Agricultural Credit Guarantee Scheme Fund (ACGSF) have a significant positive impact on the agricultural sector's growth in the long run. Conversely, the long-run agricultural growth exhibited a negative relationship with the domestic credit to the private sector and lending interest rate. This finding underscores the importance of boosting commercial bank loans to the agricultural sector and increasing credit volume from the Agricultural Credit Guarantee Scheme Fund. Furthermore, it highlights the necessity of introducing more financial incentives to attract domestic private investments and lowering the lending interest rate on agricultural credit within the country.

Sectoral Interdependencies and Multipliers in Nigeria: An Input-Output Approach

This study examines the sectoral contributions to Nigeria's Gross Domestic Product (GDP) and intersectoral interdependencies using Input-Output (I-O) analysis. Results as obtained from the 2022 Eurostat database revealed significant value added/sectoral contributions, agriculture ($6.512B, 15.1%), fishing ($4.529B, 1.1%), food/beverages ($8.446B, 19.6%), and mining/quarrying ($14.659B, 34.1%). Intersectoral interdependencies highlighted key intersectoral linkages between agriculture and food/beverages, mining/quarrying and other industries, and fishing and food/beverages. Results showed that agriculture's output (74.58%) is primarily consumed by households and used by the food and beverages sector; output from mining/quarrying (85.73%) is consumed mainly by industries (manufacturing/construction), and output from the fishing (81.68%) sector is mainly consumed by households and used by the food and beverages sector. The Type I and Type II multipliers assessed the economic ripple effects of changes in industry demand. The findings indicate substantial indirect effects, with output multipliers ranging from 1.726% (food/beverages), 2.118% (fishing), 1.803% (mining/quarrying), and 1.758% (agriculture). Income multipliers range from income multipliers: to $2.053B (food/beverages), $3.290B (fishing), $2.540B (agriculture), and $3.01B (mining/quarrying). Fishing and mining/quarrying sectors exhibited the highest total multiplier effects, emphasizing their potential for driving economic growth. The study confirms the robustness of the results using I-O analysis, validating the initial findings. Policy implications include targeted investments in strategic sectors such as agriculture, fishing, food/beverages, and mining/quarrying, which would stimulate economic growth and diversification. Also, resource allocation and diversification strategies to enhance intersectoral linkages would maximize economic benefits and reduce dependence on individual sectors.

Plant Leaf Disease Detection Utilizing Machine Learning Techniques

The rapid development of machine learning and artificial intelligence is dramatically changing plant disease. The most important among them include the detection of plant diseases, where crop diseases are identified, categorized, and analysed with the help of AI and ML systems. It examines the application of AI and ML techniques in detecting plant disease, with regards to how increased farm output and diminished harvest losses could ensue in sustainable farming. We carry out this study to illuminate how AI-based solutions are transforming current farming practices in relation to methods, challenges, and emerging trends in this burgeoning field.