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In the evolving landscape of agronomy, integrating technology with traditional practices offers unprecedented benefits. Precision agriculture, especially, is transforming the meticulous care of vines and orchards, allowing for smarter, more sustainable interventions. Georeferencing: More than Just a Location Marker Georeferencing does more than just pinpoint locations—it meticulously logs actions and quantities. – For example, in […]
Often fine-tuning a deep neural network is more art than science/engineering. Which is the most optimal learning rate? How many layers shall we freeze? Should we use different learning rates depending on the layer? Most of the time, many of these questions are solved by trial and error (and some AutoML). Would it be a […]
During the last years, the tendency for applying transfer learning was to directly fine-tune the ImageNet weights in the new domain problem where (usually) there was a lack of images available for training. However, a new trend has arisen recently; in this case, before fine-tuning, there is a previous step called pre-training where the neural […]
Apple tree is one of the most important crops due to the high nutritional value of the apples intended for human consumption. The apple tree cultivation is of high importance for farmers and especially for those who are in areas with very low temperatures, as in order to create apples of excellent quality it needs […]
The webs have been buzzing with the new shiny technology, ChatGPT – it managed to create even more noise than its previous version, GPT-3. Both are large language models (Generative Pre-trained Transformers) developed by OpenAI and have been trained on massive text datasets, which allows them to generate human-like text in a wide range of […]
Imagine that you have trained several models to find the most suitable one for your application and the one that obtained the best performance is also the largest. And, additionally, the slowest one at inference time. This problem is rather common. Very large models that fit the training objectives, but often fail to meet latency, […]
Artificial Intelligence, Machine Learning, Computer Vision: all of these sound like cool buzzwords, but what kind of problems can they help us with? Here we will try to present some classes of tasks that the aforementioned fields of study can help us tackle, both in the field and anywhere else. Image classificationYou give us a […]
In any crop, the total production is definitely the most important criterion that shows the agricultural business’s success in revenue in relation to the expenses and simultaneously indicates if the producers achieved their targeted goals. Tree crops put some challenges in counting the fruits and as a consequence making a yield prediction or estimation. However, […]
Spot spraying is a new method. It is based on a concept that only the infected areas of the crop should be sprayed and not the whole field. This novelty can be implemented by having a modern sprayer, electronics and the target to spray in the crop. This last requirement is provided by the image […]
Some posts ago, we were discussing that AutoML is the best way to avoid the overwhelming process of tuning all the hyper-parameters that modify the behavior of a neural network. However, in AutoML you can also choose which hyper-parameters should be the most relevant ones to explore. So the question is: which hyper-parameter should we […]