Yield Visualization Based on Farm Work Information Measured by Smart Devices

This paper proposes a new approach to visualizing spatial variation of plant status in a tomato greenhouse based on farm work information operated by laborers. Farm work information consists of a farm laborer’s position and action. A farm laborer’s position is estimated based on radio wave strength measured by using a smartphone carried by the farm laborer and Bluetooth beacons placed in the greenhouse. A farm laborer’s action is recognized based on motion data measured by using smartwatches worn on both wrists of the farm laborer. As experiment, harvesting information operated by one farm laborer in a part of a tomato greenhouse is obtained, and the spatial distribution of yields in the experimental field, called a harvesting map, is visualized. The mean absolute error of the number of harvested tomatoes in each small section of the experimental field is 0.35. An interview with the farm manager shows that the harvesting map is useful for intuitively grasping the states of the greenhouse.

[1]  Morikazu Nakamura,et al.  Development of a System for Recording Farming Data by Using a Cellular Phone Equipped with GPS , 2006 .

[2]  Silvio Savarese,et al.  Ieee Transaction on Pattern Analysis and Machine Intelligence 1 a General Framework for Tracking Multiple People from a Moving Camera , 2022 .

[3]  Sana A Survey of Indoor Localization Techniques , 2013 .

[4]  Ryosuke Shibasaki,et al.  A novel system for tracking pedestrians using multiple single-row laser-range scanners , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  Mi Zhang,et al.  Motion primitive-based human activity recognition using a bag-of-features approach , 2012, IHI '12.

[6]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[7]  Takayoshi Yamashita,et al.  Design of a Low-false-positive Gesture for a Wearable Device , 2016, ICPRAM.

[8]  Jason Weston,et al.  Multi-Class Support Vector Machines , 1998 .

[9]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[10]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[11]  Osama Masoud,et al.  Estimating pedestrian counts in groups , 2008, Comput. Vis. Image Underst..

[12]  Karel Charvát,et al.  Open Data Model for (Precision) Agriculture Applications and Agricultural Pollution Monitoring , 2015, EnviroInfo/ICT4S.

[13]  Sergiu Nedevschi,et al.  Stereo-Based Pedestrian Detection for Collision-Avoidance Applications , 2009, IEEE Transactions on Intelligent Transportation Systems.

[14]  Takaaki Tanaka,et al.  Agricultural worker behavioral recognition system for intelligent worker assistance , 2017 .