Our Archive
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, […]
Over the past decades, agricultural sciences relied principally on reflectance (in the visible, VIS, 0.4–0.7 μm, near-infrared, NIR, 0.7–1.3 μm and short wave-infrared, SWIR, 1.3–2.5 μm regions), thermal (in the thermal infrared, TIR 7.0–20.0 μm region) and fluorescence (at 0.68 and 0.74 μm wavelengths) sensors. Depending on the application, these sensors can be used for […]
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 […]
One high-value crop that needs careful handling both throughout the growing season and during post-harvest care is broccoli. Broccoli heads are still plucked by hand since they are easily damaged. Moreover, to harvest broccoli plants when they are in their best condition, human scouting is also needed to initially locate the field segments where multiple […]
Any team that wants to implement AI methods in their pipeline to offer novel solutions to more complex tasks they are facing, will sooner or later have to deal with the necessity for plenty of relevant data. However, what constitutes plenty? Are 100 samples enough? 1000? 10000? Where does that stop? The lower limit should be […]
In agriculture, it is really difficult to precisely distinguish the diseases, the pests that have infected your crop, and also some nutrient deficiencies, because of the symptoms’ similarities, especially at the beginning of their emergence. This task, although it seems to be easy even for farmers and agronomists, has the possibility to cause serious problems […]
Smart Farming technologies hold the promise to bring on the next (fourth) agricultural revolution. The first agricultural revolution occurred roughly 12.000 years ago, with humans farming for the first time. The second revolution came with the reorganization of the farmland during the 17th century, while the third revolution, aka “The green revolution” or modern farming as […]
We know deep learning is one of the main techniques to achieve state-of-the-art performances in computer vision methods applied to agriculture. However, this technique requires very large datasets (thousands of images) so that the automatic feature extraction can work accurately, generalize and not overfit. What can be done if we have a small dataset? There are […]
Image labeling or annotation is fundamental for every Artificial Intelligent (AI) project. The performance of the trained model is highly dependent on the quality of annotations because this process transfers the human “wisdom” into the AI domain. Lack of clear project objectives, annotation rules, and annotator’s experience are the most common reasons many AI projects fail to achieve their goals. […]