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Agriculture and AI: How to optimize your harvest!
Katharina Rösner
AI also reaches the farming, also known as Smart Farming, precision agriculture or Agriculture 4.0. Which possibilities and challenges come along with it?
Artificial intelligence can be used in a variety of ways. For example, for a resource-saving use of waterr, pesticides and herbicides.
An Australian team of researchers found that the decisive factors for AI models to predict the cultivation are the amount of precipitation, the use of herbicides and sulfur as well as the heat flow. Satellite images play a central role in this using multispectral images to identify, for example plant stress because of dryness or pests. Satellites and drones even document the soil condition which are used by technologies to create forecasts. Satellites also provide the GPS data and make them available to smart irrigation systems which then precisely distribute exact amounts of water.
Sensors in the field record when plants are stressed through Pests or drought before they show symptoms that are visible to the human eye. They also measure the nutrient content and soil moisture as well as soil compaction, which are relevant for irrigation. This also involves weather forecasts so that water is used sparingly and only where it is needed. The data is continuously documented. Neural networks recognize patterns in historical data and compare them with current data on environmental conditions. So, they associate weather fluctuations with crop yield. Sensors also record temperatures and pH-values.
Camera-controlled systems recognize rows of plants and weeds in between, which they then eliminate without damaging the plant. Irregularities in the soil can cause water to seep away, store or converge differently in different places, so that you can adjust irrigation specifically to these irregularities. This is possible through high-resolution images and links to irrigation systems.
With such data points, you can create harvest forecasts. These are not just useful for small - or even big - institutions, but also important in order to ensure and predict global food supply.
Machine learning algorithms recognize patterns in historical crop yields and compare the patterns with current environmental conditions in real time. The data points come from sensors in fields, satellite images and images from drones. The satellite images are required to train neural networks, which then identify pest infestations or deficiency symptoms. In a second step, pre-trained models are fine-tuned with specific tasks. Based on these images as well as weather forecasts and data and information about soil conditions, algorithms use machine learning to determine the optimal harvest time.
A major advantage is that due to the precision agriculture resources such as water and pesticides can be used in a targeted and environmentally friendly way. If you use less herbicide Do you increase your earnings in the long term. It is also more environmentally friendly and less expensive. The use of such systems ensures precision and saves enormous amounts of time at the same time through automation.
Since the computing power of such AI models is large, a lot of electricity is needed. Power consumption may not be as sustainable. Data storage is expensive and can be a risk factor. Critics see risks in the fact that the volumes of data and AI models are often in the hands of large corporations that can influence political decisions or at least strive to influence political decisions. This can also lead to monopolization and make companies dependent. How likely is it then that large companies will put sustainability above financial interests? There is also a fear that small companies will be displaced if they cannot afford such technologies.
It is important to emphasize that automated processes through AI do not replace farmers' work and experience, but complement them. The decisions are still up to human workers, as the systems only identify a need for action. Such systems work around the clock without getting tired, work precisely and take on laborious work. Intelligent machines can thus make everyday work easier.
One solution is definitely to use smaller software solutions that already exist that specialize in image recognition. As researchers have discovered, satellite images are the most important factor for forecasts and recommendations for action. Of course, this also raises the question of how transparent algorithms are which provide recommendations for action. And if they are not transparent, are there even more interests in the foreground than sustainability and efficiency?
Here it may be a good solution for small companies to use solutions from smaller software manufacturers. Meanwhile FrachtPilot also offers for example a harvest app, which creates harvest forecasts with the help of Data analysis and evaluation. The advantage of such lean cloud software is that it always is scalable: You only pay for what you need, which is made possible by interfaces, so-called APIs. Small software companies prevent market dominance from individual, large companies such as SAP, for example. Their solutions are also more adaptable to the size of the company.
Artificial intelligence may enrich agriculture and make it more sustainable. What risks this actually entails remains to be seen. It's worthwhile for small businesses lean software, which is scalable. You can also get harvest forecasts with FrachtPilot, for example. But we can do a lot more ;) If you still look for an ERP system for your regional direct marketing, have a look at our website or try FrachtPilot for free or book a free webinar to get to know us. We're looking forward to seeing you!
