Labeling for AgriTech is not an easy task, because it requires annotators to develop a very deep expertise in the types of plants they are dealing with. ROORA have a proven track record of working with Agriculture sector companies and research institutes and producing high-quality labeling on both RGB and multispectral imagery.
With computer vision, it is now simple to monitor the health of plants in real-time. By analysing plants and utilising various annotation approaches to annotate the impacted or noisy parts, we process training data for these models.
Crop detection can be made easier with the use of perception models. In order to handle high-quality training data, we separate crops from weeds and label them using Different Annotation Teqniques. AI is used in precision agriculture to help find pests, diseased plants, and undernourished plants on farms. AI sensors can detect and target weeds and then decide which herbicides to apply. Computer Vision can help detect the type and severity of crop diseases quicker and more reliably. Farmers can then take swift action in curing the disease.
Computer vision models can be taught to categorise the soil in a region with the help of labelled data, which is frequently provided by aerial and occasionally even satellite photos. When looking for fertile land or monitoring the effects of erosion and deforestation, for instance, this can be helpful.
Obtain the high-quality data required to assist crop automation, wildlife conservation, and monitoring of livestock. When everything is done manually, managing a big number of animals in a dairy or husbandry industry becomes challenging and time-consuming. However, the livestock management system also becomes simpler and more effective when the AI-based automated system is integrated into it. To track animals and manage cattle using AI-based devices and automated systems, ROORA offers the training data for this purpose.
Label plants at various growth stages to train your computer vision AI to alert you when something needs quick attention or when it's just ready to harvest.
