Abstract
World population is expected to reach 9.7 billion by 2050. Thus, a significant increase in agricultural production is required to accommodate population growth. However, environmental crises and urbanization pose a threat to agriculture. Vertical farming can partially mitigate these effects by growing plants while optimizing space and maximizing several outdoor resources. However, this approach necessitates thoroughly re-evaluating cultivation techniques and environmental factors. This chapter proposes a holistic, sustainable agricultural framework for developing indoor farming systems, which consists of a methodology for creating crop growth procedures from experimental data using statistical analysis and artificial intelligence algorithms. The proposed method aims to balance resource utilization and productivity in vertical farming. Furthermore, the authors propose a design framework to create a sustainable aeroponic system structure. This structure aims to provide a test bench to prove the effectiveness of the said methodology.
TopIntroduction
To meet the food demand from the world’s growing population, food production must double by 2050 (United Nations, 2022). A significant increase in agricultural production is essential to meet the food demand growth. Such an increase can be challenging to achieve, mainly due to the climate change occurring in the recent years.
One of the main effects of climate change is the temperature increase. Higher temperatures lead to higher crop respiration and evapotranspiration rates, wider pest infestation, a shift in weed flora, and reduced crop duration (Malhi et al., 2021). The higher temperatures also increase the microbial population present in the soil and their enzymatic activities. If the enzymes rise above a critical range, they denature (Tudi et al., 2021). Also, soil salinity is affected by the temperature increase and the consequent water reduction (Kumar et al., 2020). The increase in salt concentration harms plants due to osmotic stress and ion toxicity (Safdar et al., 2019). The soil pH, influenced by soil conditions and anthropogenic activities, impacts plant growth and development by affecting nutrient uptake, root diameter, and root length. For instance, acidic pH inhibits mineral translocation, while basic pH reduces plant zinc accumulation (Rahman et al., 2021). Climate change also affects the concentration of nutrients in the soil. Particularly, it leads to shortages of macro and micronutrients, such as phosphorus and nitrogen (Young et al., 2021). Soil usually contains a considerable amount of total phosphorus, but the plants can absorb only orthophosphate ions found in low concentrations in the soil solution (Alotaibi et al., 2021). Even if organic and inorganic fertilizers increase phosphorus availability, orthophosphates in the soil solution react rapidly with other soil components and become unavailable for plants.
On the other hand, the increase in current agricultural activities also negatively impacts the environment. The first negative effect is that, due to deforestation and intensive agriculture, a large amount of CO2 is released into the atmosphere (Lal, 2004). Another adverse effect is due to the use of fertilizers and pesticides. Even if they are essential to provide plants with the necessary nutrients, they cause water and air pollution (Lask et al., 2021). Crops absorb only one-third of the applied fertilizer, while the leftover gets lost in the environment (Kumar et al., 2021). The use of pesticides also results in a loss of biodiversity (Young et al., 2021). About one-third of agricultural products are produced based on the application of pesticides. However, their use affects environmental pollution of water, air, soil, and biodiversity (non-target organisms) through adsorption, leaching, volatilization, spray drift, and runoff (Tudi et al., 2021). Lastly, some agricultural techniques hurt the environment. For example, continuous cropping has decreased rhizosphere soil pH, nutrient imbalance, reduced enzyme activity, and modified microbial community composition, causing an increase in pathogenic microorganisms (Zhao et al., 2021).
Key Terms in this Chapter
Macronutrients: are nutrients that provide energy to the plants and are required in larger amounts to maintain their development, growth, and photosynthesis. Nitrogen (N), Phosphorus (P), and Potassium (K) are the primary macronutrients for plants.
Atomization: The act or process of splitting into smaller parts, sections, or groups. In this work it refers to the breakdown of water droplets for the irrigation of plants in aeroponics
Controlled Environment Agriculture: CEA refers to the practice of producing crops while adjusting environmental conditions such as lighting, temperature, CO2, humidity, irrigation, fertigation, and other elements that affect plant physiological responses.
Vertical Farming: is a form of horticulture where plants are grown vertically in layers. Usually, it can be done in a controlled environment with different techniques such as Hydroponics, Aeroponics and Aquaponics.
Micronutrients: are essential plant nutrients that are found in trace amounts in plant tissue, but play an imperative role in plant growth and metabolism
Hyperplane: In Machine Learning, a hyperplane is a decision boundary that divides the input space into two or more regions, each corresponding to a different class or output label. It is used to separate the dataspace into one less dimension for easier linear classification.
Crop Growth procedure: A recipe that specifies the ideal growing environment parameter settings to create a more effective system.
Aeroponics: It is a growing soilless technique where the nutrient solution is directly nebulized on the plant’s roots.
Standardization: is the process of rescaling a dataset to have the properties of a Gaussian distribution (a mean of 0 and a standard deviation of 1).
Cross Validation: is an evaluation method used in machine learning to find out how well a machine learning model can predict the outcome of unseen data. In k-fold cross validation the data sample is split into ‘k’ number of smaller samples.