Computers and Electronics in Agriculture

A number of research is underway in the agricultural sector to better predict crop yield using machine learning algorithms. Many machine learning algorithms require large amounts of data in order to give useful results. One of the major challenges in training and experimenting with machine learning algorithms is the availability of training data in sufficient quality and quantity remains a limiting factor. In the paper, “Wild blueberry yield prediction using a combination of computer simulation and machine learning algorithms”, we used dataset generated by the Wild Blueberry Pollination Model, a spatially explicit simulation model validated by field observation and experimental data collected in Maine USA during the last 30 years. The blueberry yields predictive models require data that sufficiently characterize the influence of plant spatial traits, bee species composition, and weather conditions on production. In a multi-step process, we designed simulation experiments and conducted the runs on the calibrated version of the blueberry simulation model. The simulated dataset was then examined, and important features were selected to build four machine-learning-based predictive models. This simulated data provides researchers who have actual data collected from field observation and those who wants to experiment the potential of machine learning algorithms response to real data and computer simulation modelling generated data as input for crop yield prediction models.

The soil chemical quality index data.

Data for computing a chemical soil quality index.

Here are codes and customized dataset used in this study. The folder named as Codes contains all scripts to realize object detection, obstacle avoidance and autonomous flight control. The folder named as Dataset contains all images and corresponding label files for training the CNN model.

Interpolated crop-specific and Germany-wide phenological phases for the period between 1993 and 2018 Naming convention: DOY_[crop code]-[phase code]_[year].tif Crop code: 202 - winter wheat, 204 - winter barley, 205 - winter rape, 208 - summer oats, 215 - corn, 253 - sugar beet. Phase code: 5 - beginning of flowering , 10 - sowing , 12 - emergence, 13 - closed stand, 14 - 4th leaf unfolded, 15/67 - shooting/stem elongation, 17 - bud formation , 18/66 - heading/tassel emergence , 19 - milk ripening, 20 - early dough ripening, 21 - yellow ripening , 22 - full ripening, 24 - harvest, 65 - tassel emergence. Cell size: 1 km² Projection: EPSG code 31467 (https://spatialreference.org/ref/epsg/31467)

To reflect the difference, four query methods were designed: (1) single-table query; (2) left join query; (3) right join query; and (4) inner join query. The single-table query operates on one table, and the other three methods query data from two tables. Every table contains 20 database fields. To reflect the impact of different numbers of nodes on query efficiency, we designed four database environments: a centralized database, and a blockchain storage structure with one, two, and three nodes. We recorded the execution time of a query every time the amount of data increased by 10,000 rows.

The estimated and measured data on fish body length is showed in the excel.

All data and implementations for coverage problem used in "Use of UAVs for an efficient capsule distribution in areas of any shape and with internal exclusion zones for biological pest control". The release points are used to path planning too. Files: 1. Polygons.py: generates the polygons; 2. def4.py: calculates the Definition 4 for all areas; 3. Graphs.py: plot graphs; 4. main.py: do all the job; 5. Metrics.py: contains definitions 1, 2 and 3; 6. Solutions.py: contains Heitor's and Dongy's solutions for coverage problem; 7. best_ind-120gen-200pop-600cup.pkl: individual generated by Genetic Algorithm; 8. best_ind-120gen-200pop-800cup.pkl: individual generated by Genetic Algorithm; 9. best_ind-120gen-400pop-600cup.pkl: individual generated by Genetic Algorithm; 10. best_ind-120gen-400pop-800cup.pkl: individual generated by Genetic Algorithm; 11. best_ind-120gen-800pop-600cup.pkl: individual generated by Genetic Algorithm; 12. best_ind-120gen-800pop-800cup.pkl: individual generated by Genetic Algorithm.

Experimental data & computer program.

A custom dataset of 1500 target images after shell breaking