Every sphere of life is being impacted by COVID-19, but little is known about how the pandemic is affecting agricultural entrepreneurs’ quality of life in developing nations. Therefore, this study examined how COVID-19 affected agricultural entrepreneurs’ quality of life by utilizing the data collected from 220 females and 1501 males through multistage purposive and random sampling methods. The dataset was analyzed using ordered logistic regression and principal component analysis. The study results indicated that female agricultural entrepreneurs’ quality of life was more adversely affected than that of male agricultural entrepreneurs during COVID-19. The findings also showed that male and female agricultural entrepreneurs with a lower socioeconomic status were more severely impacted than male and female agricultural entrepreneurs with a higher socioeconomic status. More female agricultural entrepreneurs compared to males reported that the pandemic had a negative impact on their mental health. More than two-thirds of both male and female agricultural entrepreneurs reported that the pandemic had a negative impact on their ability to purchase both food and non-food items. Similar to this, a sizable majority of people of both sexes stated that COVID-19 had an impact on their ability to access medical facilities. The results also showed that COVID-19 had a greater impact on married agricultural entrepreneurs’ quality of life than on single people. Therefore, a paradigm shift in agricultural policy is required for the pandemic response to account for COVID-19’s various effects on different genders and socioeconomic groups in rural areas.

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.