The Agriculture sector, also known as Arable farming, horticulture and animal husbandry sector, exist of farms which grow plants for production of food (potatoes, vegetables and fruit), fibre (flax), biofuel (rapeseed), dyes (madder), medicines (chicory), decoration (garden and indoor plants) and/or farms which breed animals for the production of food (meat, milk and eggs) etc.
After the introduction of GPS on the tractor, there is a growing interest to use sensors to collect data and images of crops and soils. This with the purpose to manage place specific and to save resources and to optimize the culture. This is called precision farming or smart farming.
From the ICAReS project, we want to the use of UAV’s (drones) and remote sensing in the Agriculture sector to develop and promote collecting place specific data of fields. This allows the cultivation and production to be optimized with an efficient use of adjuvants, like fertilizers and pesticides and natural sources as water. This gives an improvement of the financial result by increasing the production on one hand, and, on the other hand, saving costs.
We want to achieve this by building a network around demand, supply, research and regulations and by realizing test sites and demonstrations.
Detection of (soil-bound) diseases in potato is crucial for maximizing yield and quality in regular production, but also for the production of seed potatoes. The spotwise occurrence of these diseases hinders early detection and opens opportunities for a local treatment. To realize this, there is a need for objective criteria, based on plant physiology, which make it possible to evaluate plants/crops in a fast way with a high resolution both in time and space. In this case, ILVO aims to demonstrate how different potato diseases can be detected in an early stage using snapshot hyperspectral imaging from an RPAS (i.e. drone) combined with image processing techniques. This information can then be used for site specific disease management. Economically, this case will show to farmers and to technology and service providers how yield losses and costs related to pesticide use are reduced. Environmentally, less pesticides will be used due to an early detection and a local treatment of the disease. The ultimate goal is to accelerate the application of remote sensing technology in agriculture.
Weeds cause crop yield losses with a global average of 34% and in certain cases, the losses may exceed 70%. The most common tool for weed removal is still blanket spraying of herbicides which raises environmental concerns. In order to reduce the amount of herbicides, the dosage, timing and location of the herbicide application should be optimized using Site Specific Weed Management which requires in field detection and localization of weed patches. Clearly this offers interesting opportunities for the application of remote sensing technology. Therefore, ILVO will demonstrate in this case how RPAS systems equipped with different types of sensors can be used to map and identify weeds in different economically important crops. This information can then be used to generate task maps and precision spraying.