Solar and Terrestrial Radiation

Agriculture plays a significant role in the economic sector. The automation in agriculture is the main concern and the emerging subject across the world. The population is increasing tremendously and with this increase the demand of food and employment is also increasing. The traditional methods which were used by the farmers, were not sufficient enough to fulfill these requirements. Thus, new automated methods were introduced. These new methods satisfied the food requirements and also provided employment opportunities to billions of people. Artificial Intelligence in agriculture has brought an agriculture revolution. This technology has protected the crop yield from various factors like the climate changes, population growth, employment issues and the food security problems. This main concern of this paper is to audit the various applications of Artificial intelligence in agriculture such as for irrigation, weeding, spraying with the help of sensors and other means embedded in robots and drones. These technologies saves the excess use of water, pesticides, herbicides, maintains the fertility of the soil, also helps in the efficient use of man power and elevate the productivity and improve the quality. This paper surveys the work of many researchers to get a brief overview about the current implementation of automation in agriculture, the weeding systems through the robots and drones. The various soil water sensing methods are discussed along with two automated weeding techniques. The implementation of drones is discussed, the various methods used by drones for spraying and crop-monitoring is also discussed in this paper.

Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides

Machine learning technology in biodiesel research: A review

The existence of sunlight, air and other resources on earth must be used in an appropriate way for human welfare while still protecting the environment and its living creatures. The exploitation of sunlight and air as a substantial Renewable Energy (RE) source is an important research and development domain over past few years. The present and future overtaking in RE mainly comprises of (i) the development of novel technology for optimum production from the available natural resources (ii) environmental awareness, and (iii) the better management and distribution system. Like other domains (food, health, accommodation, safety, etc.), Artificial Intelligence (AI) could assist in achieving the future goals of the RE. Statistical and biologically inspired AI methods have been implemented in several studies to achieve common and future aims of the RE. The present study summarizes the review of reviews and the state-of-the-art research outcomes related to wind energy, solar energy, geothermal energy, hydro energy, ocean energy, bioenergy, hydrogen energy, and hybrid energy. Particularly, the role of single and hybrid AI approaches in research and development of the previously mentioned sources of RE will be comprehensively reviewed.

Renewable energy: Present research and future scope of Artificial Intelligence

Hydrogen is a clean energy source with a relatively low pollution footprint. However, hydrogen does not exist in nature as a separate element but only in compound forms. Hydrogen is produced throug...

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2018.1452296

Computational intelligence approach for modeling hydrogen production: a review

Application of ANFIS to predict crop yield based on different energy inputs

Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: Comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS)

The idea of irrigation is not new, irrigation stems as far back as the Egyptians and probably further in unrecorded history. Even the idea of automated irrigation is not new, mankind has figured out how to irrigate large areas of foliage through the use of automated and drop irrigation systems. Efficient, automated irrigation systems, which can irrigate plants to a desired level and supply those plants with just the amount of water required for normal an uptake plant growth, are currently not available. These systems, if developed, could reduce waste of irrigated water. The irrigation controller is the "brain" of an entire irrigation system. It supervises the flow of water and fertilizer to the plants, therefore, enables the farmer, or the gardener, to obtain optimized results: A successful crop or a beautiful garden, by using an optimum amount of water and fertilizer. Nowadays computerized control is very essential for the greenhouse irrigation control. Many conventional methods for controlling greenhouse irrigation are not effective since they are either based on on-off control methods or proportional control methods. This results in a loss of energy and productivity. The paper presents a solution for an irrigation controller based on the fuzzy-logic methodology. First, it describes the general problem of irrigation. Then, it discusses the physical control model. The developed Fuzzy Logic Controller (FLC) prototype is based on a Mamedani controller and it is built on MATLAB software. Following the discussion and the formal presentation of the fuzzy controller, the paper provide examples that will show the simplicity in designing and constructing such a system and other advantages of using fuzzy logic in the feedback control problem. The developed fuzzy logic controller can effectively estimate amount of water uptake of plants in distinct depth using the reliable irrigation model, evapotranspiration functions, environmental conditions of greenhouse, soil type, type of plant and another factors affecting the irrigation of greenhouse.

Intelligent Control Based Fuzzy Logic for Automation of Greenhouse Irrigation System and Evaluation in Relation to Conventional Systems

https://doi.org/10.1016/j.scitotenv.2022.154416

Principal of environmental life cycle assessment for medical waste during COVID-19 outbreak to support sustainable development goals

Effects of tillage depth and forward speed on draft of three primary tillage implements were studied by using a tension load cell in clay loam soil. Implements included a moldboard plow, a disk plow and a chisel plow, each of them with one tillage unit. A photoelectric speed sensor was used for measuring forward speed. The effects of forward speed and tillage depth on draft measurements were investigated through strip-split plot design. A significant increase in draft was observed for all the implements with increase in tillage depth and forward speed. Comparing of average draft in treatments with Duncan’s multiple range test showed that moldboard plow in highest forward speed and tillage depth had maximum draft and disk plow in lowest forward speed and tillage depth had minimum draft values. The relationships between draft and forward speed are presented graphically.

Leila Naderloo

Papers

Application of ANFIS to predict crop yield based on different energy inputs

Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: Comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS)

Intelligent Control Based Fuzzy Logic for Automation of Greenhouse Irrigation System and Evaluation in Relation to Conventional Systems

Principal of environmental life cycle assessment for medical waste during COVID-19 outbreak to support sustainable development goals

Tillage depth and forward speed effects on draft of three primary tillage implements in clay loam soil.