2 results
Search Results
Now showing 1 - 2 of 2
Conference Object Citation - WoS: 12Vulnerability of groundwater to pollution from agricultural diffuse sources: a case study(I W A Publishing, 2002) Muhammetoglu, H; Muhammetoglu, A; Soyupak, SKumluca, near Antalya in Turkey, is an important plain with its intensive agricultural activities employing greenhouses. The chemical fertilizer application practices caused excessive increases of the nitrogen, phosphorus and salinity within groundwater. A study has been initiated to assess the present state of the groundwater pollution problem of Kumluca Plain. A total of nine measurement and sampling stations have been selected to represent different depths groundwater table, different types of agricultural activities and different soil types. The magnitudes of the parameters: temperature, salinity and conductivity, nitrate, nitrite, ammonia, orthophosphate and fecal coliform were determined for groundwater. Soil samples collected from the stations have been analyzed for several parameters such as texture, total salinity, total nitrogen, and total phosphorus. The measurement and analyses results of the groundwater showed wide spatial variations depending on factors such as the quality of irrigation water, depth groundwater, soil characteristics, type and age of agriculture and hydrology. Groundwater vulnerabilities to pollution have been analyzed using the SEEPAGE Model approach. Furthermore the soil, aquifer and groundwater characteristics, which will be utilized to establish "cause" and "effect" relationships in future, have been clarified.Review Citation - WoS: 8Citation - Scopus: 11A Review on the Applications of Machine Learning and Deep Learning in Agriculture Section for the Production of Crop Biomass Raw Materials(Taylor & Francis inc, 2023) Peng, Wei; Karimi Sadaghiani, OmidThe application of biomass, as an energy resource, depends on four main steps of production, pre-treatment, bio-refinery, and upgrading. This work reviews Machine Learning applications in the biomass production step with focusing on agriculture crops. By investigating numerous related works, it is concluded that there is a considerable reviewing gap in collecting the applications of Machine Learning in crop biomass production. To fill this gap by the current work, the origin of biomass raw materials is explained, and the application of Machine Learning in this section is scrutinized. Then, the kinds and resources of biomass as well as the role of machine learning in these fields are reviewed. Meanwhile, the sustainable production of farming-origin biomass and the effective factors in this issue are explained, and the application of Machine Learning in these areas are surveyed. Summarily, after analysis of numerous papers, it is concluded that Machine Learning and Deep Learning are widely utilized in crop biomass production areas to enhance the crops production quantity, quality, and sustainability, improve the predictions, decrease the costs, and diminish the products losses. According to the statistical analysis, in 19% of the studies conducted about the application of Machine Learning and Deep Learning in crop biomass raw materials, Artificial Neural Network (ANN) algorithm has been applied. Afterward, the Random Forest (RF) and Super Vector Machine (SVM) are the second and third most-utilized algorithms applied in 17% and 15% of studies, respectively. Meanwhile, 26% of studies focused on the applications of Machine Learning and Deep Learning in the sugar crops. At the second and third places, the starchy crops and algae with 23% and 21% received more attention of researchers in the utilization of Machine Learning and Deep Learning techniques.
