Analysis of Development Strategy for Ecological Agriculture Based on a Neural Network in the Environmental Economy

Abstract

Ecological agriculture (E.A.) protects soil, water, and the climate, ensuring nutritious food. It encourages biodiversity and prohibits chemical inputs or hybrids. Agricultural development strategy should prioritize the development of water, land, forests, biodiversity, agricultural infrastructure, research and extension, technology transfer, investment, and unified management to bring about significant changes in agriculture. Agricultural practices have resulted in deforestation, biodiversity loss, ecosystem extinction, genetic engineering, irrigation issues, pollution, degraded soils, and related waste. Food producers increasingly use artificial neural networks (ANN) at most agricultural production and farm management stages. A new EA-ANN method, including agriculture, has been widely employed to solve categorization and prediction tasks. In addition to maintaining natural resources, sustainable agriculture helps preserve soil quality, reduces erosion, and conserves water. Ecological farming uses ecological services, including water filtering, pollination, oxygen generation, and disease and insect management. ANN increases harvest quality and accuracy of evaluating the economy by enhancing productivity. Agriculture’s prediction and economic profitability are focused on the energy optimization afforded by ANN. Ecological knowledge is assessed in light of commercial markets’ inability to provide sufficient environmental goods. Future agriculture can include robotics, sensors, aerial photos, and global positioning systems. The proposed method uses supervised artificial learning to read the data and provide an output based on effectively classifying the natural and constructed environment. The probability distribution implemented in ANN is a function specifying all possible values and probabilities of a random variable within a specific range of values. The mathematical model assumes that EA-ANN utilizes machine learning on an internet of things platform with bio-sensor assistance to achieve ecological agriculture. Microbial biotechnology is activated, and the best option for EA-ANN is calculated for an effective data-driven model. This ensures profitability and limits the impacts of manufacturing, such as pollution and waste, on the environment. Various agricultural strategies can result in environmental concerns. The EA-ANN methodology is used to make accurate predictions using field data. Agricultural workers can use the results to plan for the future of water resources more effectively.

1. Introduction

An ANN is a self-learning and self-adapting algorithmic mathematical model comparable to the structure of the brain’s synaptic connections for distributed and parallel information processing. The greatest advantage of ANN is its ability to describe complicated nonlinear interactions; it performs effectively in prediction and simulation [1]. Ecological agriculture improves pollination, which directly leads to increased agricultural yields. Efficient water filtering, pollination, and oxygen generation are several natural ecosystem services that may be used in environmental agriculture [2]. Biotic stress, pests, and viruses threaten agricultural productivity due to rising populations and environmental deterioration worldwide. Solutions are urgently needed. Using biopesticides is an alternative to using synthetic chemicals in pest management [3]. Past and current agricultural practices have caused environmental contamination, negatively impacting food safety, public health, and environmental sustainability. Low- and middle-income nations must end hunger, reduce poverty, improve nutrition, and achieve food security [4]. The effects of climate change jeopardize long-term agricultural viability. Rural areas in underdeveloped nations, where agriculture is the primary source of income, are susceptible to the effects of global warming [5]. Agroecology is becoming increasingly popular to promote more sustainable farming and food systems. While agroecology’s contribution to sustainability has been widely acknowledged, the evidence is still fragmented due to the different methodologies, data sets, and timeframes used [6]. Agriculture’s harmful influence on the environment is hardly unique. One solution to this difficulty is the concept of eco-efficiency, which evaluates agricultural production in terms of standard inputs and environmental impact [7]. Sustainable farming techniques provide several economic, social, and environmental advantages. Farmers’ attitude toward risk is an important initial step in understanding their behavior and coping mechanisms for managing environmental risks [8]. Agricultural production is essential to maintain long fertilizer supply networks. Agricultural stakeholders can consider the trade-offs and long-term consequences of various approaches to agricultural modification [9]. The use of sustainable agriculture to strengthen the resilience of ecosystem services to climate change has been explored extensively for decades. Agriculture has the potential to help vulnerable rural areas, but there is no evidence to support this claim [10]. Economic and agricultural sustainability has been a focus of study for some time. Sustainability in agriculture is again part of the Common Agricultural Policy (CAP) mandate. The processes a producer uses are entirely up to them [11]. The agricultural sector is increasingly concerned about sustainable growth, since growth cannot continue at current rates in light of the current lack of resources and energy use and the pollution created by harmful chemicals. Reducing toxic pesticides, conserving natural resources, and lowering greenhouse gas emissions may contribute to agricultural sustainability [12]. Plant microbiomes are critical to promoting plant development and to sustaining agricultural sustainability. Natural resources, such as plants and soil, are rich in bacteria. Maintaining the global nitrogen balance and ecosystem function depends critically on soil microbiomes [13]. Primary water users include agriculture and electricity producers. They are intertwined and difficult to understand. Uncertainty in the water–food–energy nexus model can reflect the intricate connections between water, agriculture, and power [14]. Food production can be increased by using plant agriculture. A potential solution to one of the most pressing challenges facing humanity in the twenty-first century can be found in these advancements [15]. Historically, individuals and cultures have used collective action to create efficient and effective distributor systems and the possibility of preserving natural capital and valuable flows of ecosystem services [16]. Numerous empirical studies have been conducted on promoting Sustainable Agrarian Practices (SAP). Researchers should consider farmers’ economic and psychological aspects when analyzing the adoption of SAP [17]. According to newly found information, research into plant roots has shown bacteria’s potential role in safeguarding sustainable agriculture methods by promoting plant growth. Research on the soil microbiome and plant growth-promoting concerns are both covered in this section. [18]. Urbanization and depletion of natural resources threaten the environment and food and energy supplies. Throughout evolution, cyanobacteria have been the most successful and long-lasting prokaryotic creatures [19]. Agriculture evolves to maintain food security, social justice, and ecological integrity. Agricultural systems’ cognitive, social, structural, and functional changes transcend alternative technology. An integrated strategy must identify agricultural and associated research and extension needs. The results indicate that this technique can enhance regional agricultural development [20]. Agriculture has many societal effects, such as providing food, habitat, and employment, producing raw materials for food and various products, developing strong economies via industry, and developing healthy communities. The digital agricultural economy in China and the digital entrepreneurial behavior of Chinese farmers have seen extraordinary growth in recent years in terms of scientific, technological, and economic advancements. There is a fast-growing agricultural industry, and agricultural production activities seriously affect the natural world. Significant environmental harm has occurred from these actions, such as soil, water, and air pollution from industrial triple waste emissions, indiscriminate deforestation, and overconsumption of natural resources. A well maintained eco-environment is essential to human life, agricultural enterprise growth, and public health preservation. Studies relevant to the issue have concluded that environmental and ecological issues will lead to a steady decline in agricultural output and put a damper on the development of the agricultural industry. However, the government’s environmental control and preservation goals will be more easily implemented thanks to the agricultural economy’s constant development. Therefore, a sustainable agricultural economy must be developed with a sustainable agricultural eco-environment.

In agreement to be practical, environmental approaches require up-to-date information, techniques, and actions. An environment’s local knowledge develops through time. Innovations are expected to be adapted to individual communities and provide individualized approaches. Agricultural production is hardly a kind of ecological cultivation practice, encompassing various techniques that restore ecosystems, such as reducing land degradation, increasing moisture absorption and preservation, storing emissions in composting, and promoting variety. Apply mathematical models and statistical methods to collect, analyze, and present data to anticipate market trends and explain economic occurrences. Create suggestions, ideas, and strategies to explore markets and address financial issues. Watch and monitor students’ research projects and educational assignments. Environmental economics is the research of the resource base’s most cost-effective allocation, use, and preservation. In essence, economics analyzes how people generate and consume products and services. Conventional climate change, soil erosion, water pollution, and ecological agriculture health are just some of the problems that traditional agriculture exacerbates. Sustainable organic farming practices reduce greenhouse gas emissions, improve soil quality, and restore depleted ecosystems, producing purer air and water. Compile, analyze, and report data to explain economic phenomena and forecast market trends using mathematical models and statistical techniques. Create recommendations, policies, or plans to solve financial problems or interpret markets. Supervise research projects, as well as student research projects.

There are three main objectives:

• Conduct agriculture research into the production, treatment, and management of an agricultural product, including research needed for a better knowledge of the processes or the environment essential for developing an economy.
• Enhanced harvest success can largely be attributed to ecological farming’s many benefits, including, most notably, increased pollination. Environmental farming is a technique that employs natural ecosystem services, such as pollination, water purification, oxygen creation, and insect damage control.
• The ecological economic idea is developed from assessing commercial markets’ inability to provide sufficient environmental goods. As a result, the research emphasizes how these people can increase wellbeing besides impeding in industry sectors.
• Smart farming uses ANN systems to increase harvest quality and accuracy. Assist EA-ANN in detecting plant diseases and insufficient pest nutrition using ANN technology. In addition, it enables farmers to keep an eye on the health of their crops and the surrounding soil.

The rest of the paper follows Section 2 for a survey of the literature of the existing method. Section 3 proposed a method for EA-ANN to be discussed. Section 4 is concerned with experimental analysis, and Section 5 is the conclusion.

2. The Literature Survey

Nawab Khan et al. (2021) discussed that the agricultural economy is becoming more important, albeit widely understood, and farming operations are becoming increasingly dependent on accurate, advanced data, and technology [21]. Farming benefits from new technologies, such as the internet of things (IoT). Due to this seismic shift, existing agricultural methods have been thrown off balance, which has given rise to new opportunities and threats. For the first time, an in-depth examination of IoT potential in agriculture and the difficulties that arise when integrating these sophisticated technologies with conventional agricultural systems is presented in this thorough research report. Finally, by performing a thorough investigation, they discovered that the IoT has the potential to transform growing food.

Qurban Ali et al. (2021) detailed that plant pathogens, including insects, fungi, bacteria, viruses, and viroids, may cause most infectious diseases that significantly impact the world economy [22]. Therefore, plant disease resistance is essential to ensure crop survival and production. A bio-sensor (B.S.) technology of disease detection that has been successfully employed in other industries is reviewed in this article, and these technologies might potentially change agricultural production systems. Emerging BS technologies, such as isothermal amplification, detection of non-materials, paper-based techniques, robots, and lab-on-a-chip analytical devices were likewise featured in the list of innovations.

Sachin S. Kamble et al. (2020) explained that agriculture food supply networks have several challenges, including a lack of industrialization, inadequate management, inaccurate data, and inefficient supply chains [23]. Several factors must be considered while developing solutions to these problems. First, data-driven digital (D.D.) technologies are changing the agricultural supply chain. This framework emphasizes the importance of supply chain visibility and resources. Practitioners may use this paradigm to plan their investments for a robust D.D. food supply chain. To wrap up our work, we discuss future research options and constraints.

Rohit Sharma et al. (2020) introduced the idea that agriculture is vital to all human endeavors. Food safety, security, and environmental sustainability depend on data analytics in the coming years [24]. Machine learning (ML) applications in agricultural supply chains have been thoroughly examined in the current work. Ninety-three publications were analyzed for their use of different machine learning methods in various stages of the farm supply chain. The study indicates that ML approaches may help agricultural supply networks and contribute to the sustainability of agricultural supply chains. Based on the findings of this study, a ML framework for sustainable agricultural supply chains is provided.

Ajar Nath Yadav et al. (2020) proposed that abiotic pressures, such as nutrient restriction, high temperature, salt, drought, infections, and other climatic calamities, significantly impact the agriculture industry [25]. Several crops have suffered dramatically due to the combined effects of these factors. Microbial biotechnology (MBT) is one of the best ways to solve the problem and boost agricultural output. It provides an overview of MBT for sustainable farming and biomedical systems: functional and diversity aspects in this chapter. Furthermore, microorganisms can be employed as a bio-control agent to protect crops from plant infections that significantly impact yields.

Shuang Chen et al. (2018) examined estimating agricultural climate conditions using deep learning and short-term memory (LSTM). Agriculture and human populations are interested in protecting the environment [26]. Analytical training that uses environmental data to predict future trends may improve agricultural product safety. Based on the experimental findings, the LSTM model is adjusted to replace LSTM, which is more effective. The model’s prediction results deviate very little from the actual values.

Nga Nguyen et al. (2021) introduced the idea that adopting climate change adaptation will be impossible without sustainable agriculture practices (SAP). A better understanding of farmers’ decisions to employ SAP might benefit policymakers [27]. The theoretical framework for our investigation was the deconstructed theory of planned conduct, which was expanded to include climate change perception and farmers’ historical behavior. An important conclusion was that social trust and financial control are related. We emphasize the significance of improving climate perception and comprehension to assist the adoption of sustainable agricultural methods while developing confidence in the scientific information received by local farmers and their support network.

Sudhir Shendeet al. (2021) discussed the idea that scientific interest in using metal-based nanoparticles in agriculture and food security has risen significantly in recent years [28]. For reducing and stabilizing metal salts and MNPs, various biological resources have been used instead of toxic chemicals; environmentally friendly synthesis techniques have paid attention to nano biotechnological advancements. Green synthesized MNPs can be employed in agriculture and food security; however, this study concentrated on their agricultural uses. In agriculture, green-produced MNPs might help promote plant growth, control pests and diseases, prolong shelf life, prevent spoiling, and more. Researchers are now studying how to use green synthesized MNPs in sustainable farming and food security.

Jorge Poveda et al. (2021) detailed that a more efficient and effective alternative to conventional cattle for animal protein production is in full industrial development due to the current human population boom [29]. An option for developing sustainable agriculture and the circular economy is thus being considered: using insect manure as an organic fertilizer instead of agricultural inputs. After evaluating the most recent studies, this article concludes that employing insects as organic fertilizer in sustainable agriculture has several advantages. First, when insects are raised in industrial quantities for food and nutrition, they serve as a valuable organic fertilizer source.

Anderson do Espirito Santo Pereira et al. (2021) examined the idea that global warming and resource and biodiversity degradation threaten agricultural production. New agricultural advances are needed to increase crop yields and ensure food safety in the long run [30]. Altering seed metabolism and signaling pathways can influence all stages of plant development, even germination and establishment. Reactive oxygen species and plant growth hormones are in equilibrium and are altered by nano-priming to boost stress and disease resistance, reducing pesticides and fertilizers. Nanotechnology in seed nano-priming can have advantages and disadvantages in promoting sustainable agricultural practices.

Yevhen Mishenin et al. (2021) detailed the idea that climate change, shocks, the degradation of land and soils, and the loss of ecosystems all impact agriculture’s ability to produce enough food of a given quality. Agriculture is mostly to blame for these environmental issues [31]. As a result, agricultural greening must address socio-ecological and economic factors to achieve global food security. As a final note, this chapter explores the significance of agricultural production in rural development and the need to find a farming organization and policy that aligns with long-term rural development goals that can be achieved sustainably. An effective way to implement the local food paradigm is to design an environmental management system involving industry and the community.

Oludare Isaac Abiodun et al. (2018) introduced how neural networks are used in the actuality of things. Artificial neural networks (ANNs) are categorized in this book, and the reader is given an overview of current and future developments in ANN application research and researchers’ focus. [32]. The analysis evaluates ANN contributions, compares performances, and criticizes techniques. An ANN is a computer program that simulates a natural neural network. The broader definition of agriculture was employed to solve different categorization and prediction problems. Decision support systems and systems for precision farming can both benefit from them.

Tawseef Ayoub Shaikh et al. (2022) discussed the idea that digital agricultural management applications had advanced significantly, influencing information and communication technology (ICT) to provide advantages for farmers and customers and pushing technological solutions into rural areas [33]. When appropriate, global and cutting-edge IoT-based agricultural platforms and technologies are also featured. We thoroughly evaluate the most current literature in each area of expertise. As a result of this thorough analysis, we conclude current and forthcoming trends in artificial intelligence and highlight ongoing and new research issues in artificial intelligence in agriculture.

Wang, L et al. (2019) discussed the method to forecast the future of solar agriculture’s ecological niche. The findings demonstrate that China’s photovoltaic agricultural sector is flourishing and positively contributing [34]. There will be three stages of evolution for the ecological niche of China’s solar agriculture: placement, integration, and symbiosis. This research shows that China has moved past the positioning phase, focusing on integrating its newfound strengths. As they have become increasingly intertwined, the two sectors have continually recreated their resources, technology, and environmental norms, expanded their reach beyond the original industries’ niches, absorbed more potential resources, and created a functioning ecological niche system. This article provides commentary on the ecological niche of solar agriculture by combining the interval entropy weight approach with the interval cloud model, which is based on the shape of the interval data table.

Zhang, M et al. (2023) explored the connection between urban sprawl and carbon emissions from various angles [35]. However, most of these studies only focus on discussing and analyzing one of these directions, and very fewer examine how urbanization affects the local thermal environment or carbon emissions. Furthermore, the authors employ artificial neural networks to anticipate urban land use or surface temperature changes. Finally, they also explore the influence of changes in urban sprawl on the thermal environment around single-period or multi-period remote sensing picture data.

One drawback of neural networks’ ecological agriculture environment economy is a lack of a problem. Global change, erosion, species extinction, dead zones, and genetic modification are many environmental issues caused by agriculture. Land and water deterioration and eutrophication are expected outcomes of agricultural expansion worldwide. Emissions of gas emissions pollutants are a result of farming activities, such as harvesting. IoT can overcome the disadvantage of B.S., D.D., ML, and MBT compared with the proposed method EA-ANN.

3. Ecological Agriculture on Environment Economy Based on Neural Network

In the literature section, the lack of a problem is one of the drawbacks of the ecological agricultural environment economics based on neural networks. Agriculture significantly contributes to several environmental issues, including climate change, erosion, species extinction, dead zones, and genetic manipulation. Worldwide agricultural expansion is predicted to lead to the degradation of land and water resources, as well as eutrophication, which is discussed in the literature section. Based on the literature section, the proposed section highlights how these individuals might improve their wellbeing and impede business sectors.

Soil quality is maintained, water is saved, and the earth’s natural resources are conserved through sustainable agriculture. Agriculture has a significant impact on economic development and growth. It is a fundamental part of human life, since it provides us with the food that we need to survive. It dramatically impacts other parts of the economy, since it allows for raw materials for production. It implies substantial prospects for a green economy transition, leading to increased societal wellbeing, including better health, excellent employment opportunities, and economic advancement. Real-time insights from the fields can be provided by ANN to agriculture, allowing them to identify areas that require irrigation, fertilizer, or pesticide application. Increasing food production, while using fewer resources, is possible with the help of new technologies, such as vertical agriculture.

3.1. Ecological Agriculture in Artificial Neural Network

Figure 1 shows an ANN output of a predicted value for a variable being searched. ANN’s weight is a crucial consideration. A weight is applied to increase the amount of information a neuron receives. The weights of every neuron in a network are independent of each successive one. Plants and other greenery are housed in greenhouses, labeled greenhouses. Greenhouses have gone by many different identities over the generations, depending on the country, the purpose, and the materials used in the construction. Energy efficiency can improve the quality of life while positively impacting health in many circumstances. For example, it is essential to implement energy-efficient measures in residences to reduce the danger of disease and mold growth. It is possible to describe plant growth as an increase in plant volume and mass by producing new structures, such as organs or tissues. Development and reproduction are frequently linked to the concept of growth. Supervised artificial learning includes classification models, and they read the data and provide an output that classifies it into one or more categories—the public health field deals with environmental health, including all components of the natural and constructed environment. Environmental health is concerned with the health of the natural and built surroundings. Statistically speaking, a probability distribution is a function that specifies all possible values and probabilities that a random variable can have within a specific range of values. International human rights legislation recognizes the importance of economic and social rights, such as the right to education, work, a sufficient standard of living, health, and social security, which governments should grant without discrimination.

$$\mathrm{J}={\mathrm{z}}^2\iint \mathrm{e}-\sum \mathrm{x}\sin \mathrm{z}\frac{\mathrm{x}}{\mathrm{n}}\times \frac{\mathrm{r}1+\mathrm{r}2}{2}$$

(1)

In Equation (1), $\mathrm{J}$ for ANN and $\mathrm{n}$ is a neural network, and $\mathrm{e}$ represents the greenhouse, and $\mathrm{x}$ represents social, $\mathrm{r}$ represents probability, and $\mathrm{z}$ represents microclimate. $\frac{\mathrm{r}1+\mathrm{r}2}{2}$ represents environmental health and $\sin \mathrm{z}\frac{\mathrm{x}}{\mathrm{n}}$ in a model classification. $\iint \mathrm{e}$ represents the energy optimization and can be processed in agriculture in Equation (2), which is based on the following.

$$\mathrm{U}=\sqrt[\mathrm{t}]{\mathrm{Q}}\int \mathrm{b}~\sum \mathrm{t}\times \frac{\mathrm{Q}}{\mathrm{b}}\csc \mathrm{t}+\sqrt{\frac{\mathrm{i}-\mathrm{p}}{\mathrm{G}+\mathrm{N}}}$$

(2)

$\mathrm{U}$ is plant growth, $\mathrm{Q}$ is an economic probability, $\mathrm{i}$ is an environmental economy, $\mathrm{G}$ is agriculture, $\mathrm{N}$ is a hybrid, $\mathrm{p}$ is organic growth, and $\mathrm{t}$ is an inorganic product for humans; the agricultural revolution has a wide range of effects. Economy and industrial investment are part of the main familiar drivers of economic development. Understanding the linkages and interfaces of agricultural systems and farming communities were improved due to this research.

3.2. Framework of Ecological Agriculture

Figure 2 shows ecological agriculture aims to use natural recourses to replenish their productive potential while minimizing negative consequences on the ecosystem beyond the field’s borders. A crop is a power station and a plant product that may be produced and harvested for profit or survival. According to their intended use, crops can be classified into food and feedstock, fibers and oilseeds, ornamentals, and industrial crops. Harvested food crops, such as fruits and vegetables, are consumed by humans. To be economically viable, the project’s economic benefits must surpass its social costs. The economic prices of the project are not the same as the financial costs, and externalities and environmental impacts should be taken into account. Farmers are identified to be the most influential stakeholder group, since they are the ones that make the final choices on land usage. A wide range of local and regional actors impact farmer decisions and are, in turn, influenced by further stakeholders.

Farming uses chemical products, including fertilizers, insecticides, and plant growth hormones. This sector of the economy has been dramatically enhanced by modern farm technology. Rotavators and ploughs include tractor-trailers, power harrows, levelers, rippers, and disc harrows, among the most often used machinery. A cultivator tills or loosens the soil before sowing seeds in it. This practice benefits significantly from soil health, weed prevention, and crop growth. Fertilizers replace the nutrients that crops extract from the soil. Failing to utilize fertilizers can dramatically reduce crop yields and agricultural production.

Consequently, mineral fertilizers increase the soil’s nutrition supply with minerals that crops may quickly absorb and use. Anything that involves transporting people or things from one place to another, regardless of the form of transportation, qualifies as transportation. Storage is a crucial marketing function that consists of storing and preserving commodities from the time of production until consumption is needed. To maintain a steady flow of things in the market, the storage of goods is necessary from the time of production through the time of consumption.

$$\mathrm{T}=\int {\mathrm{k}}^{2-1} {\ast \mathrm{v}}^{\mathrm{u}-1}\pm \iiint {\mathrm{s}}^{\mathrm{t}-1}÷\frac{{\mathrm{q}}_1-{\mathrm{q}}_2}{2}\times \frac{\mathsf{\pi }}{2}-{\mathsf{\alpha }}_1$$

(3)

In Equation (3), $\mathrm{T}$ is storage, and $\mathrm{k}$ is an agrochemical for $\mathrm{v}$ in ecological viability and $\mathrm{u}$ for crop type in $\mathrm{t}$ the location and $\mathrm{s}$ in an agriculture ecological, and ${\mathrm{q}}_1$ is transportation for the following in Equation (1). $\mathsf{\alpha }$ is an economical process, and $\mathsf{\pi }$ represents the stakeholders, and $\int {\mathrm{k}}^{2-1}$ represents the fertilizers’ fairness in economic $\frac{{\mathrm{q}}_1-{\mathrm{q}}_2}{2}$ values, notably in taxes and social policy. ${\mathrm{v}}^{\mathrm{u}-1}$ is equity, $\frac{\mathsf{\pi }}{2}$ is economic equality, and $\iiint {\mathrm{s}}^{\mathrm{t}-1}$ is economic equality, which may be calculated.

$${\mathrm{E}}_{\nabla \left(1\right)}={\mathrm{D}}_1^2-\frac{\sqrt{{\mathrm{T}}^{\left(\mathrm{H}-1\right)}}}{\sqrt[2]{{\mathrm{H}}_{\mathsf{ϵ}}}}$$

(4a)

In Equation (4a), $\mathrm{E}$ is a cultivation process,$\mathrm{D}$ represents stakeholders, $\mathrm{T}$ is storage, and $\mathrm{H}$ is the transportation. $\nabla$ is sustainability, and $\mathsf{ϵ}$ in a chemical is represented by (4b),

$${\mathrm{E}}_{\nabla \left(2\right)}={\mathrm{D}}_2^3-\frac{\sqrt{{\mathrm{T}}^{\left(\mathrm{H}-1\right)}}}{\sqrt[2]{{\mathrm{H}}_{\mathsf{ϵ}}}}$$

(4b)

In Equation (4b), E is a cultivation process in the economic theory in agricultural production, $\mathrm{H}$ is the distribution in maximizing food, and $\mathrm{T}$ is fiber production, which can be discussed,

$${\mathrm{E}}_{\nabla \left(3\right)}={\mathrm{D}}_3^4-\frac{\sqrt{{\mathrm{T}}^{\left(\mathrm{H}-1\right)}}}{\sqrt[2]{{\mathrm{H}}_{\mathsf{ϵ}}}}$$

(4c)

In Equation (4c), agricultural location $\frac{\sqrt{{\mathrm{T}}^{\left(\mathrm{H}-1\right)}}}{\sqrt[2]{{\mathrm{H}}_{\mathsf{ϵ}}}} \mathrm{is} \mathrm{a}$ theory that is concerned with the way farmers, ${\mathrm{D}}_3^4$, allocate land for various uses, and land is organized geographically. In its classical forms, economic rent is significant.

3.3. Overview of Ecological Agriculture Economies

Figure 3 shows that farming’s resources and the goods produced are allocated, distributed, and employed in agricultural economics studies. Various environmental issues, including global warming, genetic engineering, irrigation problems, pollution, and degraded soils, result from agriculture’s ecological impact. In an ecosystem, species of several sorts work together to create a bubble of activity in a specific land region. Parts of an ecosystem are made up of live organisms and nonliving organisms. Plants, animals, and various species are characteristics of biotic factors. Agro-biodiversity is influenced by multiple factors, including the environment, genetic resources, and different cultures’ management strategies and practices. Various methods are used in the utilization of water and land.

Improving productivity is a matter of obtaining more value for investment. Performance management represents: strategic objectives, environments, and functions. Each effort is focused on boosting industrial and agricultural output. If they discuss food security, researchers mean that individuals have access to nutritious food that fit their dietary requirements, food choices, and social, economic, and physical wellbeing. A conservation policy aims to protect and restore a dwindling species, a group of people, an ecosystem, and a natural or semi-natural area. Conflict ensues when multiple parties come to a standstill, regardless of their diverse opinions, interests, and perceptions. There is no peace or harmony if people conflict; this is all angry words, threats, and forms of physical violence. Higher agricultural productivity means more food can be produced with fewer people, shifting labor from farming to urban industries. Agriculture and urbanization negatively correlate in open economies. It is the outcome of the interplay between farming and the natural environment in a region that creates the agricultural landscape. Most land is covered by soil, which is a loose, granular substance. There are inorganic and organic components. Plants in agriculture rely on soil for structural support and water and nutrients. The chemical and physical qualities of soils are distinctive. Agricultural output relies heavily on the availability of water, which has a significant impact on food security. It is described as an increase in the economy’s production of goods and services. Increases can aid economic growth in capital, labor, technology, and human capital.

$${\mathrm{D}}_1=\cot {\mathrm{N}}_1\pm \frac{{\mathrm{Y}}_1^2}{{\mathrm{X}}_1^2}+\frac{{\mathrm{X}}^2}{{\mathrm{Z}}^2}-\frac{2{\mathrm{X}}_1{\mathrm{Y}}_1}{\mathrm{X}.\mathrm{Z}.}\times \iint \sqrt{〚\mathrm{N}〛-〚\mathrm{Y}〛}\ast {\mathsf{\phi }}_1^2$$

(5)

In Equation (5), $\mathrm{D}$ is the agriculture environment economy, $\cot$ is improved productivity of $\mathrm{N}$, the water, and $\mathrm{X}$ is a soil for $\mathrm{Y}$ in the landscape of $\cot {\mathrm{N}}_1,$ which represents economic growth and development of the process.

$$\mathrm{A}=\left(\mathsf{\alpha }+\mathsf{\beta }\right)\ast \left(\frac{1}{{\mathrm{x}}^2}\right)\pm \prod {\mathrm{y}}^{{\mathrm{x}}^2}÷\frac{1+\tan \mathsf{\theta }\pm \sqrt{2\mathrm{C}}}{2}-{\mathsf{\gamma }}^2$$

(6)

In Equation (6), $\mathrm{A}$ represents agrobiodiversity, and $\mathsf{\beta }$ is a coherency of $\mathsf{\alpha }$, the food security, $\mathrm{y}$ is a conservation policy and $\tan$ in conflicts, and $\mathrm{C}$ is the population for $\mathsf{\gamma }$ the urbanization, $\frac{1+\tan \mathsf{\theta }\pm \sqrt{2\mathrm{C}}}{2}$, and the ecological process $\prod y^{x^2}$ is the growth of an environment.

$$\mathrm{V}=\log \mathrm{D}-\int \mathrm{C}{\sin }^{-1}\mathrm{r}\times \underset{\mathrm{C}}{\max } \mathrm{r}$$

(7)

$\mathrm{V}$ is an ecosystem, $\mathrm{D}$ is the prevention, $\mathrm{r}$ is an ecological condition and $\max$ maximum damage in crops, $\mathrm{C}$ is environmental health in Equation (7) and $\underset{\mathrm{C}}{\max } \mathrm{r}$ intensification of the provision in an agricultural environment, and the biodiversity of $\int \mathrm{C}{\sin }^{-1}\mathrm{r}$ is the technology of growth. As a result of farming systems, improved pollination increases agricultural output. Natural ecosystem activities, including water filtration, pollination, oxygen production, disease, and pest control, are all included in ecological farming systems.

4. Result and Discussion of the Ecological Agriculture Environment Economy

The agricultural analysis is critical to the success of a farm’s crops. The soil’s existing nutrient levels must be understood to determine which regions need improvement if quality and yields are to be improved. Producing safe, high-quality food that benefit the natural environment, farmers’ livelihoods, their employees, and the local community, while protecting farm animals and their wellbeing, can be indicated by sustainable agriculture. Organizations can use environmental analysis to uncover internal and external elements that might benefit or hurt their operations. Future challenges and possibilities for organizations can be anticipated by considering factors, such as the economy and technology. This increases the global pressure on limited land and water supplies, accelerating soil degradation and eutrophication.

Dataset description: multiple sources brought this dataset to life. This data were first made available to me via the dataset posted by user Vivek on Kaggle, the environmental impact of food production, which identifies Our World in Data as its source. Next, I searched Our World in Data (OWID) and found their series. Environmental impacts of food production involve thoroughly researched data storytelling, compelling data visualizations, and the option to download the data behind each visualization as a separate .csv file. OWID also identifies the source of published data: a study was published in Science in 2018 in an article, reducing food’s environmental impacts through producers and consumers, by J. Poore and T. Nemecek.

Working lands are the rangelands, farms, and forestlands used to support livelihoods.

Table 1. The activity of ecological agriculture.

Number of Lands	IoT	BS	DD	ML	MBT	EA-ANN
1	23.8	18.8	17.5	36.5	57.5	71.2
2	19.2	29.2	31.5	23.5	50.4	66.4
3	22.6	31.6	19.1	38.6	54.5	60.5
4	13.8	23.8	34.3	45.1	59.3	73.2
5	31.9	21.9	30.2	51.9	67.5	72.7
6	26.6	16.6	31.2	53.5	77.6	95.2
7	35.5	37.5	42.8	62.4	75.3	84.4
8	20.7	49.7	37.5	54.3	78.3	92.4

says the activity of ecological agriculture impacts of an ANN can be reduced by increasing agricultural input efficiency or the food produced for each pound of fertilizer or feed input. Additionally, it can have positive impacts, such as trapping greenhouse gases inside crops and soils or minimizing flood risks by utilizing certain agricultural practices. It is widely accepted that organic farming is more ecologically friendly, since it uses renewable resources, recycles nutrients, relies on natural pest and disease controls, preserves ecosystems and soil, lowers pollution and pollution, and conserves biodiversity. In addition, raw food utilization, animal welfare, product variety, and waste minimization are all encouraged in organic farming. However, the future of organic agriculture will depend on its economy. With the help of external sensing, the IoT in agricultural production aims to improve crop forecasting, yield, quality monitoring, and several other crucial statistics for growers.

BS offers promise for improving sustainable agriculture by enabling supervision or the accurate diagnosis of food poisoning. In addition to pesticides and veterinary medications, it likewise checks for heavy metals, viruses, poisons, and illegal additions to food.

Data-driven agriculture is a strategy for increasing traditional farming practices with big data by collecting, analyzing, and acting upon the right kinds of farm data at the right time and in the appropriate formats.

$$\mathrm{M}=\mathsf{\alpha }\ast \mathsf{\beta }\left(\mathrm{B}|\mathrm{C}\right)\times \mathsf{\omega }\pm \sqrt[2]{\mathrm{w}\frac{\mathrm{O}}{\mathrm{H}}}$$

(8)

In Equation (8), $\mathrm{M}$ is an ANN, $\mathrm{C}$ is machine learning, $\mathrm{w}$ is for the internet of things, and in the bio-sensor, $\mathsf{\omega }$ is ecological agriculture, and $\mathrm{H}$ is data-driven. Βs in microbial biotechnology are activated by technology, and they are calculated better for EA-ANN.

Table 2. Development of organic agriculture.

Number of Lands	IoT	BS	DD	ML	MBT	EA-ANN
1	26.5	19.8	41.5	36.5	57.5	60.2
2	29.2	19.2	31.5	49.5	32.4	50.4
3	16.6	25.6	39.1	57.6	50.5	70.5
4	19.8	17.8	29.3	39.1	56.3	68.2
5	35.9	29.9	36.2	55.9	68.5	78.7
6	39.6	19.6	37.2	49.5	67.6	85.2
7	30.5	32.5	42.8	59.4	70.3	76.4
8	15.7	29.7	39.5	58.3	79.3	90.4

says that agricultural development generates the right conditions for the farm’s potential to be realized. An essential factor in achieving success is the capacity to allocate resources efficiently throughout different stages of the production cycle. Agricultural communities began to form simultaneously as humans started domesticating plants and animals. Domesticity allowed families and larger groups to build communities and escape the nomadic lifestyle of hunter-gatherers who relied on gathering and hunting for daily sustenance. In addition, organic farming practices, such as composting, mulching, and employing bio-fertilizers can improve crop growth and soil quality. Composting using vermin compost is another excellent way to provide vital nutrients to the soil organically.

$$\mathrm{H}=\sqrt{\mathrm{g}-}\mathrm{c}\times \pm \sum {\mathrm{i} \mathrm{c}}^{\mathrm{d}}\times \left(\frac{\mathrm{c}}{\mathrm{d}}\right)$$

(9)

In Equation (9), $\mathrm{H}$ is for the development of organic agriculture, $\mathrm{c}$ is demonstrated, $\mathrm{d}$ is environmental, $\mathrm{g}$ is for soil quality and $\left(\frac{\mathrm{c}}{\mathrm{d}}\right)$ health in advanced agriculture, and $\sum {\mathrm{i} \mathrm{c}}^{\mathrm{d}}$ is a sensor, and agricultural development may positively impact the economy outside of organic, resulting, and economic growth.

Figure 4 shows the output value at current and constant prices is used to evaluate the firm’s performance, not the product’s price. Measurement of the whole sector, including its sub-commodity groups and commodities, is the basis of this effort. Agriculture’s economic performance can be assessed by looking at net value added at factor costs, gross weight added after deducting the fixed capital cost, and production subsidies and taxes. It is possible to define ecological performance as the optimal distribution of a performance feature along an environmental gradient. In contrast to traditional agriculture, where external inputs mainly impact biomass and grain production, we find that past management, landscape structure, and soil quality contribute to organic barley performance.

$$\mathrm{F}=\mathrm{v}\sqrt{\mathrm{u}}\pm \int \mathrm{g}\times \sin {\mathrm{Y} \mathrm{h}}^{\mathrm{X}}$$

(10)

In Equation (10), $\mathrm{F}$, or performance ratio, and $\mathrm{u}$, are agriculture production, and $\mathrm{h}$ is a domestic ecological, and $\sin \mathrm{Y}$ is a trigonometric function in which there are several ways to increase the value of a product or service while still being environmentally responsible. Agriculture for food is more performed, but the technology is less productive than for animals—the farms’ performance is unaffected by their involvement in limited food supply chains or multifunctional agriculture.

Figure 5 shows probabilistic reasoning measurement errors in the self-reported agricultural data used in the research, which are connected with the fundamental values of the variables under investigation. Measuring mistakes in self-reported input and output data on agricultural production have implications for marginal returns to these contemporary agrarian inputs, which specialists explore in this paper. It investigates a general multiple measurement error problem, where both output and information can be measured with error, and these mistakes can be associated. Based on this, this paper provides a practical case study research on incorporating the human error component into risk assessment methods for agricultural operations in grape production. These results are effective because they can guide traditional technology dissemination tactics and invalidate long-held assumptions about the profitability of contemporary agriculture.

$$\mathrm{P}=\mathrm{f}\sqrt[2]{\mathrm{q}}\int \mathrm{s}\times \cos \mathrm{f}\frac{\mathsf{\pi }}{2}\times \frac{\mathsf{\beta }}{\mathsf{\alpha }-\mathsf{\mu }}$$

(11)

In Equation (11), $\mathrm{P}$ is for self –report, $\mathrm{q}$ is an error rate, $\mathrm{f}$ is agriculture, $\mathsf{\beta }$ is production, $\mathsf{\alpha }$ is operation, $\mathsf{\mu }$ is output, $\cos$ is a trigonometric function, $\mathsf{\pi }$ is agriculture data, and $\mathrm{f}\sqrt[2]{\mathrm{q}}$ is uncertainty in weather, yields, pricing, and other farming-related issues that can significantly influence farm revenue and determination.

Figure 6 shows that agriculture productivity assessment helps identify regions that operate less effectively than their neighboring regions regarding agricultural output. Regional disparities can be minimized in agricultural planning by defining low, medium, and high production zones. Efficient productivity makes farming more productive and lucrative, decreases waste and expenses, and has less impact on the environment via our technological innovations. The balance between utilizing natural raw resources and technical-economic materials and the advantages they provide, whether in production or consumption, constitutes resource efficiency. Product and service advantages are maximized, while consumption and waste are minimized.

$$\mathrm{V}=\sum \mathrm{I}\left(\cot \mathrm{K}\right)\iint \mathrm{I}\log \mathrm{L}\frac{\mathrm{K}}{\mathrm{Z}}$$

(12)

In Equation (12), $\mathrm{V}$ is the efficiency ratio, $\mathrm{K}$ is framing, $\mathrm{L}$ are fertilizers, $\mathrm{K}$ is an ecosystem, $\mathrm{Z}$ is oxygen generation and $\log$-in logarithm function, and $\mathrm{I}$ is non-renewable and ecological, which is a method for boosting output $\iint \mathrm{I}\log \mathrm{L}\frac{\mathrm{K}}{\mathrm{Z}}$ while reducing the negative $\left(\cot \mathrm{K}\right)$. Organic resources are used to restore their productive capacity while minimizing the environmental effect and efficiency of the ecosystems beyond a field’s surface in an economically successful agricultural practice.

5. Conclusions

The pollination process is enhanced in the environment, resulting in higher yields. Ecological farming uses ecosystem services, such as water filtering, pollination, oxygen generation, and disease and insect management. Limiting non-renewable energy and chemicals is possible by adopting sustainable farming techniques. Maintaining a healthy and renewed land can significantly contribute to meeting the growing population’s food needs. Reduced agrochemical usage minimizes the requirement for non-renewable energy. It is possible that organic farming can reduce greenhouse gas emissions, as well as global warming by trapping carbon in the soil. An ANN can emulate brain-like neural networks. Numerous classification and forecasting tasks have been applied to large agricultural regions. In the context of precision agriculture and decision support systems, they can play a role. Researchers worldwide have been utilizing these technologies for a long time to enhance agricultural production, making it increasingly efficient and producing the best quality supplies possible. Robots, temperature and moisture sensors, aerial photographs, and global positioning systems can be used in agriculture in the future. Farms can become highly lucrative, productive, safe, and ecologically friendly due to these technological gadgets and precision farming and robotic systems. Agricultural production on limited farms increases food production and improves the ecology by increasing soil, limiting erosion, and enhancing biodiversity. Limitations on genetic variation within the crop and the fact that all qualities differentiating between parents are segregated, and therefore huge populations are not possible with conventional methods, are the main drawbacks of genital activity.