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.
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.
17 pages, Southern Australian farming systems operate predominantly under Mediterranean climatic conditions, which limit the choice of cover crops suitable for enhancement of ground cover and soil moisture retention, erosion control, atmospheric soil nitrogen (N) fixation, and weed suppression between cash crop rotations. Given that the successful establishment of cover crops is climate-driven and also influenced by edaphic factors such as soil pH and salinity, there has been increased interest by southern Australian producers in identifying potential cover crop species well adapted to specific Australian farming systems, which provide vital ecosystem services and sustainable economic benefits through the improvement of soil properties. This review summarises recent findings on cover crop inclusion in diverse farming systems in southern Australia, including continuous and mixed broadacre cropping as well as viticulture and horticulture systems, to identify opportunities and limitations related to their use. Cover crop inclusion in viticulture and pasture systems with lower moisture stress was observed to benefit the subsequent cash crop through enhanced production potential. Long-term, multi-site field experimentation incorporating summer cover crops in winter crop rotations showed that cover crops enhanced ground cover and soil water infiltration in some locations across southern Australia while sometimes increasing winter crop yield, suggesting that soil type and regional climatic conditions greatly influenced the delivery of multiple cover crop benefits. Collectively, these studies have suggested a need for longer-term field evaluations using multiple cover crop species and investigations of termination options under varying environmental and soil conditions to better quantify the legacy effects of cover crops.
16 pages, The importance of smallholder farming is increasingly recognized in rural areas where increased crop productivity and market participation can effectively improve their dietary diversity and nutrition quality. However, rural households are still faced with severe food insecurity and malnutrition. The study sought to assess the role of smallholder farming in crop productivity and market access on rural household dietary diversity. The secondary data were collected using a quantitative research method, and 1520 participants were selected using a stratified random sampling technique. The descriptive results showed that cereals were the most (98%) consumed food group, while vegetables and fruits were the least consumed food groups, at 37% and 23%, respectively. The results from the Household Dietary Diversity Score (HDDS) showed that 57% of smallholder farmers consumed highly diverse diets (more or equal to six food groups), whereas 25% and 18% of smallholder farmers consumed medium dietary diversity (four to five food groups) and low diverse diets (less or equal to three food groups), respectively. The findings from the Conditional Mixed Process (CMP) and Poisson endogenous treatment effect models showed that household size, ownership of livestock, wealth index, and involvement in crop production positively influenced household dietary diversity. On the other hand, output and access to market information showed a negative effect. Social grants had contradicting effects: they had a negative impact on the HDDS received from crop productivity while they had a positive effect on the HDDS from market participation. Providing different ways smallholder farmers can use their funds effectively can help improve household dietary diversity and nutrition quality. The study recommended that more workshops and training be conducted that cover all the sustainable production systems that smallholder farmers can undertake to produce different food groups. These will raise awareness among smallholder farmers about the requirements for balanced diets for food and nutrition security.
