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How AI is changing agricultural practices in India

The agriculture industry is hoping that more modern technology can make a big difference in productivity.

Listen on Marketplace Morning Report (December 27, 2023)

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Scale and speed bumps for technology in tree fruit

A case study in crop load management: The challenges encountered as new technology tried to find its footing in the fruit industry can illustrate the speed bumps to ag tech success.

December 2023 Issue

Kate Prengaman // December 11, 2023

The first time I watched a camera sensor scan an orchard row and size every apple, I was amazed. The second time, I wondered what the value of such data would be to the grower. 

Turns out, I’m not the only one wondering. 

Growers and tech developers don’t always know, either.

Read full story here from GoodFruit Grower here…

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Crop Load Technology Adoption Survey

Please help us gauge adoption but completing this survey, created by Mauricio Guerra Funes, a PhD student with Dr. Miguel Gomez in the Charles H. Dyson School of Applied Economics and

Management at Cornell University. You can reach Mauricio at mg2344@cornell.edu.

Below is the active formal survey link:

https://cornell.ca1.qualtrics.com/jfe/form/SV_bxXH5orx3GwZD6u

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Evaluation of Computer Vision Systems and Applications to Estimate Trunk Cross-Sectional Area, Flower Cluster Number, Thinning Efficacy and Yield of Apple

by Luis Gonzalez Nieto, Anna Wallis, Jon Clements, Mario Miranda Sazo, Craig Kahlke, Thomas M. Kon, and Terence L. Robinson

Edited by Mike Basedow, the full original article can be accessed here.

Precision crop load management of apple requires measuring trunks, counting flowers, fruitlets, or fruit at various times during the year to guide management decisions. While previous studies have found ways to incorporate these manual measurements into helpful orchard tools, time has often been a limiting factor in the adoption of these practices. There are an increasing number of tech companies working in agriculture that are incorporating these models into their portfolio. Our hopes are that the time required to collect the necessary data can be minimized so that more growers can have actionable information to manage their orchard more precisely.

Read more on Mike Basdedow’s blog here…