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.

Water, Resources, and Resilience: Insights from Diverse Environmental Studies

“…Water resources touch upon a multitude of aspects, encompassing public health, environmental conservation, energy production, and infrastructure engineering. These studies reflect the interconnected nature of water, emphasizing the need for cooperative efforts among researchers, policymakers, and practitioners to develop holistic solutions [54][55][56][57][58][59][60].…”

Multispectral Plant Disease Detection with Vision Transformer–Convolutional Neural Network Hybrid Approaches

“…However, agriculture faces numerous threats that can negatively impact crop production, food security, and rural livelihoods. These threats can arise from various sources, including pests and diseases, environmental challenges, and social and political issues [3]. From the above-mentioned threats, plant diseases can significantly negatively impact food production.…”