How can the agricultural sector successfully incorporate sustainable, green practices? As the clock ticks on widely known climate change ‘deadlines’, the day to day realities involved in executing efficient, profitable food production continue. Regardless, the need for a huge shift in reducing industry waste is critical to addressing environmental limitations. Improving packaging throughout the supply chain means reducing single-use plastics, developing viable biodegradable and recyclable alternatives from sustainable sources, and limiting the amount of packaging materials required to safely transport produce and food products from farm to table.

Last week we took a look at computer vision; what it is, how it works, and some of the applications for computer vision in agtech. This week we’ll go over some further applications for machine vision technology in autonomous vehicles, drone and UAV monitoring and mapping, and quality assurance.

Self driving cars, facial recognition software, mobile cheque deposits, the way facebook knows to tag your friend Andrew in all your pictures of him without asking; a huge variety of technologies, even those we take for granted, employ state of the art technology in computer vision. In agriculture, the applications of computer vision are also growing fast. In part one of our series on computer vision in agtech, we’ll answer some basic questions about the nature of computer vision and discuss its applications in autonomous weeding, harvest and quality control.

In 2018, The Agricultural Improvement Act called for the removal of hemp, cannabis sativa with a THC level of 0.3%, from the schedule one classification to allow for easier agricultural production. Hemp is an extremely versatile crop that is used to make food, oils, animal feed and fibres/textiles.