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CQ cotton farmers to reap rewards of AI pesticide technology


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Central Queensland cotton farmers are set to reap the financial rewards of research using Artificial Intelligence technology from computer games to reduce spray drift from pesticides.
Crop losses from the unwanted spread or drift of pesticides have cost the industry more than $18 million in previous seasons.
Pesticides are used in farming to protect crops against pests, weeds and diseases, and help the Australian agricultural industry remain productive, profitable and sustainable.
Reducing or eliminating spray drift will minimise the impact on neighbouring crops and wildlife.
The Revolutionising Agricultural Spray Applications for the Cotton Research and Development Corporation project aimed to address the challenge of obtaining immediate feedback on the accuracy of spraying set ups. ....

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iTWire - AgTech applications could 'revolutionise' cotton farming industry, say Monash researchers


iTWire
Monday, 03 May 2021 11:31
AgTech applications could ‘revolutionise’ cotton farming industry, say Monash researchers
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Researchers at Monash University are applying Artificial Intelligence (AI) technology and game-design practices to reduce spray drift from agricultural pesticides, minimising the negative impact on neighbouring crops and wildlife - and potentially “revolutionising cotton farming.
Monash says that while there is currently no way of receiving immediate feedback to indicate if spray set up and operation is correct, the “revolutionising agricultural spray applications” for the Cotton Research and Development Corporation (CRDC) project aims to address this challenge. 
According to Monash spray drift cost the Australian cotton industry more than $18 million in crop losses in 2018 alone, and that with spray drift and the unwanted movement of pesticides are a global environmental issue.  ....

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How can Bayesien Inference support complex decisions? A practical guide to an overlooked approach


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Summary:
For decision makers grappling with data, Bayesian Networks are an overlooked asset. Affordable? Yes. Performance and applicability to edge devices? Yes again. Here s a practical guide to how Bayes Nets can solve enterprise problems.
In part one of this series, we covered some basic probability theory principles - and compared Machine Learning approaches to Bayesian Belief Nets (Can Bayesian Networks provide answers when Machine Learning comes up short?). In this article, we ll dig a little deeper into Bayesian Belief Networks and how they can be applied to complex decisions.
Understanding Bayesian Inference
In my practice, I find most people involved with advanced analytics, such as predictive, data science, and ML, are familiar with the name Bayes, and can even reproduce the simple theorem below. Still, very few have any experience implementing Judea Pearl s Bayesian Belief Networks: ....

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