Successful agricultural operations depend on crop monitoring for nutrients, irrigation, diseases, and overall plant health. Traditionally this has been carried out by visual examination of crops on the ground. However, these methods are limited and only fighting the symptoms instead of proactively preventing them. Fortunately, precision agriculture provides solutions using artificial intelligence and hyperspectral imaging for optimized crop yield and fertilization while reducing the environmental impact. The key is targeted application rather than whole-field treatment. But let’s not put the cart before the horse and define the underlying concepts before we dive deeper into the matter.
Hyperspectral imaging collects and processes information based on the amount of reflected light from a surface to accurately identify agronomic variables like water deficiency. The derived data, coming from a range of wavelengths across the electromagnetic spectrum, is then visualized by a software to monitor agricultural characteristics. The benefits are usability, rapid assessment, accuracy and consistent results. Unsurprisingly, it is considered a breakthrough in the proliferation and practical application of precision agriculture.
Crop intelligence serving local farm challenges
Drought is a significant factor in terms of crop yields. Thus, early detection of water related stresses allows farmers to accurately irrigate before the effects result in yield losses. Hyperspectral imaging detects such changes long before it is visible to the human eye. The practical applicability was inter alia demonstrated in a trial with barley, where the development of water or nutrient stress has been detected four days before it was observable with the naked eye.
In terms of fertilization and soil characteristics, hyperspectral imaging can measure both deficiencies in nutrients as well as heavy metal contamination of soils. In addition, this technology can map fields for much lower costs than traditional direct sampling methods. Besides investigating contamination, hyperspectral imaging will also determine areas in a crop field that are nutrient poor, even for the ones under vegetation. This dynamic mapping of soil improves precision agriculture and decreases environmental impacts since the application of fertilizer is reduced to its minimum.
Another threat can equally be tackled with hyperspectral imaging: fungal pathogens. Fungal diseases are responsible for losses up to 40 % of global agricultural productivity. Moreover, those consequences can have long-term effects on consumers, public health, societies, environments and farmers. Hence, the losses itself inadequately reflect the overall repercussions. The early detection of plant disease in the field allows producers to rapidly treat affected areas before the optimum time for counteractive measures has passed. Apart from mitigating individual yield losses, this method prevents diseases from spreading to neighbouring fields or crops.
The possibilities for precision agriculture are numerous and will keep on growing as indexes for each plant species, nutrient or soil property are continuously developed and improved. Currently, many other applications of hyperspectral imaging are being tested in post-harvest quality control, food safety of agricultural products or insect and contaminant detection. Portable devices have become a vital tool for researchers and farmers alike because they deliver accurate data while keeping the costs low. These devices enhance and facilitate day-to-day monitoring processes, and thus create a new paradigm of agricultural efficiency.