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Project Profile

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Kibbutz Samar

Precision farming in the date plantation of Kibbutz Samar


Precision farming involves gathering and analyzing information from the field, in order to utilize resources with maximum efficiency, based on the diverse needs of the field. Information-gathering systems form the base of precision farming. Optimally managing water, soil, and fertilizer resources while tracking plant development and estimating yields requires a constant collection of geo-spatial information. A combination of GPS and sensory systems for an exact mapping of the placement and situation of each plant throughout the growth cycle, allows the grower to receive a maximum yield while saving scarce resources.

For the past 20 years, Kibbutz Samar has developed a wide variety of complex agricultural machinery, including sorting systems based on computer optics that streamline the date harvest.  During the course of the growth cycle, workers treat each tree 13-15 times.  A system that combines results of the sorting of each tree’s fruit with physical information from the field is needed to improve the quality and quantity of yield from each tree. The length of the Arava and Jordan Valleys is planted with hundreds of thousands of date palms, which produce tens of thousands of tons of dates each year. Precision farming can contribute considerably to increasing the yield and quality of this fruit.


  • The proposed technology should significantly reduce the work force needed in the date industry. This is a product known for its dependence on a large work force, and the use of technology in the different stages of development and harvest should reduce this need.

  • The technology developed for this project can be applied to other crops and other geographical areas.


The proposed project includes electronically identifying and mapping all parts of the plantation (trees, crates of fruit, pallets, etc.), gathering integrated data, processing and analyzing the data in order to make better managerial decisions.

  1. Marking each of the 7,000 trees with RFID (Radio Frequency Identity) tags or by mapping the entire plantation with GPS. 

  2. Adding scales to the existing harvesting wagons.

  3. Marking each crate of pre-sorted dates (36,000) with RFID tags.

  4. Marking each pallet of sorted dates with identification tags (RFID), including date, plantation section, and moisture level.

  5. Weighing the pallets before, during, and after moisture content treatment to learn the effects of the treatment on fruit that arrived from different areas of the plantation.

  6. Preparing a user interface program for inputting and processing these data.

All these data will lead for gathering information on the quantity of fruit produced by each tree.

Dr. Hanan Ginat