Discover our non-invasive sensor technology
CQUniversity's non-invasive sensor systems for testing the ripeness of mangoes.
Well as you know as a consumer yourself, you go into a retail store and you purchase fruit on the basis of what it looks like, take it home and you have an eating experience that’s bad and the research says that you won't go back to buy that fruit for four to six weeks, so it's not an instant decision but it’s certainly important to repeat purchase.
You know the big problem has been that to test mangoes in the past for maturity, they had to cut them. So the only tests we really had were the external visual cues, you know so whether it’s full, good shoulders, shape, bit of blush colour. But the reality is there's a big variation between varieties in that area and some varieties are really hard to tell by external cues, so the only sure method we had before was to actually take a mango and cut it in half and have a look at the flesh colour. So with one of the NIR guns we are now able to walk through the orchard testing fruit, seeing if there’s enough to go through a spot pick or whether they’re all ready and train up our workers as to the visual cues as to what a mature mango looks like. It just means we can get our start date correct so that we're picking fully mature fruit that the customer is going to be happy with.
Initially, we were prompted by growers to look at estimating the quality of fruit non-invasively, its internal quality being sugar content or dry matter content and that took us down the path of measuring in-line. So you’re on a pack line, you’re sorting on colour and weight. Now we were adding in another facility, that is estimating that the sugar content or dry matter content of that fruit. We were in the fields doing the dry matter measurements and we could see the grower practice of trying to estimate fruit yields, that is how much crop was on the tree, so that they could be organised in terms of labour requirements, packhouse requirements and that was all being done manually with a hand counter, so that lead us into a new line of work looking at machine vision in the field, so rather than just machine vision in the pack house, taking it into the field to estimate crop load across the field. The next step on from that of course, having seen the fruit, is to try and reach out to pick the fruit to automate the harvest.
It’s actually getting more advanced and so now the machinery is able to identify and count fruit in the orchard and last year it turned out to be with only a few percents wrong from the actual count of numbers of mangoes are in that entire block. Now that technology is also able to give us the size range of that fruit and so knowing how much fruit is in the block, knowing when it’s going to be mature, knowing the size of the fruit means we can schedule our workforce, we can order the right number of cartons, we can order the right numbers of inserts to go in those cartons. This is going, this could be a real game-changer for not only our farm but for the industry.