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.
Broughton, Duncan (author) and Win, Su Su (author)
Format:
Research summary
Publication Date:
2019
Published:
Myanmar: Department of Agricultural, Food, and Resource Economics, Michigan State University, East Lansing.
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 166 Document Number: D11673
Notes:
7 pages., Feed the Future Innovation Lab for Food Security Policy, Research Paper 131, Burma Food Security Policy Project (FSPP)., Analysis revealed that Myanmar has one of the smallest, most underfunded agricultural research systems in Southeast Asia. It is cited as having critical gaps in research capacity, an agricultural research capacity that is highly fragmented, weak linkages between research and extension at local level, and lack of overall strategy for development of agricultural research and extension. Strong economic justification was cited for higher rates of investment in agricultural research, along with recommendations for action.
26 pages, via online journal, Purpose
This paper is concerned with the impact of the University of California Cooperative Extension (UCCE) on regional productivity in California agriculture. UCCE is responsible for agricultural research and development (R&D), and dissemination of agricultural know-how in the state.
Method/methodology/approach
We estimate the effect of UCCE on county-level agricultural productivity for the years 1992–2012, using an agricultural production function with measures of agricultural extension inputs alongside the traditional agricultural production inputs at the county level.
Findings
Results show a positive impact of UCCE through its stock of depreciated expenditures. For an additional dollar spent on UCCE expenditures stock, agricultural productivity, measured as value of sales at the county level, improves by $1–9 per acre of farmland for knowledge/expenditure depreciation rates between 0 and 20 percent.
Practical implications
Results suggest that county differences in productivity could affect extension expenditures. The high level of contribution found in the results would be especially useful during a period of political pressure to reduce public spending for agricultural extension in the state.
Theoretical implications
Theoretical implications suggest that agricultural systems with higher level of knowledge depreciation are associated with higher resulting incremental agricultural productivity per an additional dollar spent on UCCE expenditures stock. This suggests that extension policy should consider also the agricultural system (crop mix).
Originality
We use original budgetary data that was collected especially for answering our research questions from archives of UCCE. We estimate impact of extension at the county level in California, on the value of agricultural sales (of crops and livestock). We developed an extension expenditure stock, using current and past expenditures data, and different depreciation rates, following the theory of Knowledge Production Function.
