22 pages, The objective of this study was to provide a comprehensive overview of the recent advancements in the use of deep learning (DL) in the agricultural sector. The author conducted a review of studies published between 2016 and 2022 to highlight the various applications of DL in agriculture, which include counting fruits, managing water, crop management, soil management, weed detection, seed classification, yield prediction, disease detection, and harvesting. The author found that DL’s ability to learn from large datasets has great promise for the transformation of the agriculture industry, but there are challenges, such as the difficulty of compiling datasets, the cost of computational power, and the shortage of DL experts. The author aimed to address these challenges by presenting his survey as a resource for future research and development regarding the use of DL in agriculture.
17 pages, The purpose of the study was to design a device for the dissolution of fertilizers for agricultural use in an automatic and environmentally sustainable way to facilitate the work of farmers. To achieve this goal, an outdated blade design was used, which generates turbulent and laminar flows thanks to the angle of inclination of its blades. In tests, the combination of these two flows gave a better result compared to laminar and turbulent flows separately. The best results were achieved by varying the spin and speed, the time between spins, and the rest time. The time it would take to dissolve the mixture was drastically reduced if it were conducted in the traditional way (manually) or compared with commercial mixers. In conclusion, the technique used for the dissolution of agricultural minerals is more effective and reduces time, energy, and effort. This was able to reduce the time necessary to dissolve the fertilizer by 93 percent compared to doing it manually and by 66 percent compared to using commercial mixers, in a solution of 100 L of water per 100 kg of ammonium sulfate.
Hundal, Gaganpreet Singh (author), Laux, Chad Matthew (author), Buckmaster, Dennis (author), Sutton, Mathias J (author), and Langemeier, Michael (author)
Format:
Journal article
Publication Date:
2023-01-09
Published:
Switzerland: MDPI
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 206 Document Number: D12835
16 pages, The production of row crops in the Midwestern (Indiana) region of the US has been facing environmental and economic sustainability issues. There has been an increase in trend for the application of fertilizers (nitrogen & phosphorus), farm machinery fuel costs and decreasing labor productivity leading to non-optimized usage of farm inputs. Literature describes how sustainable practices such as profitability (return on investments), operational cost reduction, hazardous waste reduction, delivery performance and overall productivity might be adopted in the context of precision agriculture technologies (variable rate irrigation, variable rate fertilization, cloud-based analytics, and telematics for farm machinery navigation). The literature review describes low adoption of Internet of Things (IoT)-based precision agriculture technologies, such as variable rate fertilizer (39%), variable rate pesticide (8%), variable rate irrigation (4%), cloud-based data analytics (21%) and telematics (10%) amongst Midwestern row crop producers. Barriers to the adoption of IoT-based precision agriculture technologies cited in the literature include cost effectiveness, power requirements, wireless communication range, data latency, data scalability, data storage, data processing and data interoperability. Therefore, this study focused on exploring and understanding decision-making variables related to barriers through three focus group interview sessions conducted with eighteen (n = 18) subject matter experts (SME) in IoT- based precision agriculture practices. Dependency relationships described between cost, data latency, data scalability, power consumption, communication range, type of wireless communication and precision agriculture application is one of the main findings. The results might inform precision agriculture practitioners, producers and other stakeholders about variables related to technical and operational barriers for the adoption of IoT-based precision agriculture practices.
