A technological innovation for capsicums production in Peru: the mechatronics arms
Bibliographic record
Abstract
Peru agricultural exports have increased in recent years due to (i) free trade agreements with many countries (United States, Canada, European Union, China, Thailand, Singapore, Japan, Chile, among others), (ii) an increasing international demand for healthy products, (iii) country´s economic development and (iv) more private investments in this sector (Velazco 2012). Also, if we can compare among Peru three main regions (Coast, Andean highlands and the Jungle), It is the Coast (western region) that has a developed agricultural production due to unique weather conditions, private investments, public infrastructure, transport costs and quality of land (Gomez, 2008). This country development is also related to the production of non-traditional products for export like asparagus, artichokes, capsicums, bananas, grapes, among others; produced by agro industrial companies and small farmers and that are mainly labor intensive (Gomez, 2008 and Velazco, 2012). This very successful export diversification and self-discovery process was the result of a combination of strong natural comparative advantages (mainly excellent agro climatic conditions) and a significant innovation effort. It meant the introduction and expansion of new products and markets, the entry of new firms, and experimental research and the adoption of new techniques and process technologies developed abroad (in irrigation, crop management, post-harvesting, sanitary control, storage and packing) to produce high-quality, niche (gourmet) and higher value-added products, in line with consumer trends in sophisticated food markets. In products such as asparagus, mango, organic coffee and capsicums, Peru has become a leading world exporter (OECD). For this reason one of the government main tasks for the next years is to meet urgent agriculture producer’s needs in the areas of technological Innovation and business management (MINAG). In this context, this thesis analyzes the applicability of a new technology – the mechatronic arms – specifically to capsicums production sector in Peru. We chose Capsicums production sector (paprika, chilli pepper) because is mainly labor intensive and is the sector where my family company (DIROSE SAC) operates. This innovation consists in a 40 arms mechatronic combine, and it was first created in order to improve the efficiency on the labor intensive phase of harvest for this kind of agriculture products. It is estimated that a laborer with brief training operating the machine would be equivalent to 40 people that not only would work during daytime, but also on the night shift as well. Also, using this new technology can allow a company to make additional crops that would increase their yields and annual revenues. This thesis was developed as a business plan to make this new product available for other agriculture companies that operates in the capsicums production sector in Peru; however, this new technology has the potential to be modified in order to be available to other kind of agriculture products, in Peru and other countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".