IoT Devices
In the context of crop production, IoT (Internet of Things) devices refer to integrated systems and digital devices used for monitoring, controlling, and optimizing processes related to plant cultivation. These smart devices collect data from various sources and use it to automate and improve crop production processes, such as irrigation, fertilization, pest control, and yield forecasting. The term IoT originates from the English phrase „Internet of Things.” Examples of these devices include soil sensors, irrigation systems, drones, satellites, mobile apps, climate sensors, robots and autonomous vehicles, early warning systems for various threats, cameras and imaging sensors, communication networks, and platforms associated with IoT.
Currently, we are witnessing increasingly intensive implementation of technologies that fall under the IoT framework. These devices, in line with their purpose, often collect vast amounts of data, necessitating their appropriate real-time analysis for full utilization. Although manufacturers of these technologies often also provide the relevant software for data analysis, it is important to emphasize that the current capabilities of such software are just a fraction of the potential offered by software enhanced with AI. In the near future, we can expect to see comprehensive modernizations of existing programs, as well as the creation of entirely new ones capable of combining and finding analogies in data from a much broader spectrum of sensors that collect these data. This opens up entirely new possibilities in the context of making optimal decisions dependent on a range of factors. Bearing in mind that a modern farm should be run like a large company to achieve good financial results, it is essential to seek savings, which can also come from making optimal decisions. By reducing errors, we avoid triggering a chain reaction whose consequence is incurring additional costs to rectify our wrong decisions. Combining the practical knowledge of producers with data “provided” in the right form by AI could be key to achieving a competitive level relative to other producers. Producers who achieve higher yields of better quality while reducing costs will naturally develop faster, dominating the market and reducing the profitability of farms that lag behind. Let’s take a closer look at individual technologies.
We will likely observe changes most rapidly in typical agricultural crops, where there is a maximum degree of mechanization, and in the most advanced soilless cultivation systems. In these systems, thanks to a large number of sensors and sensors, incredible precision can be achieved, provided that we can effectively utilize these data.
Climate Factors, Irrigation, Nutrition, and Plant Protection
Discussing contemporary technologies related to irrigation, nutrition, and plant protection cannot be done without mentioning climate factors. Climatic conditions determine everything, which is why each growing season is unique, and just when we think we’ve achieved perfection, the weather brings us back to earth, teaching us something new. Therefore, we should start with two aspects, the first of which is weather forecasting, both long-term and for the next few hours. In both cases, more accurate forecasts help us make optimal decisions and, to some extent, „predict the future” in terms of the problems we will soon face.
Artificial intelligence is increasingly influencing climate models and weather forecasting, primarily due to its ability to process vast amounts of data and find analogies within them. AI, especially machine learning (ML), is used to analyze meteorological data from satellites, weather stations, atmospheric balloons, and other sources. Simultaneously, AI aids in creating more advanced climate models in shorter periods, which can simulate potential climate change scenarios. With improved understanding of climatic processes, accuracy, and speed of forecasts, we have access to more detailed predictions, facilitating decision-making, and, sometimes crucially, enhancing our ability to respond to future extreme weather events. Due to more efficient data analysis, AI will likely uncover new connections and dependencies in climate data that might have been overlooked in existing meteorological data analysis systems, which have significantly lesser capabilities.
Similarly, in the case of analyzing data from our meteorological stations located on our farms, we can expect significant improvements with the help of well-developed programs utilizing AI technologies. These advancements will provide much better data analyses, which can be interpreted in the context of irrigation, protection, and nutrition of plants.
Starting with plant protection, previously mentioned drones or other types of devices, including smartphones equipped with the appropriate software, enable us to identify pests. Applications designed for identification, based on simple algorithms, have been developed for a long time. AI will only enhance the capabilities of recognizing all signs of pests occurring on plantations, not limiting itself to a specific group. This will also allow for the further development of autonomous machines tasked with precisely destroying pests using various methods. The effectiveness of these machines depends on how efficiently and quickly the system they are based on can identify pests in crop cultivation.
The next significant step will be the development of comprehensive models for forecasting the occurrence of diseases, pests, and even weeds. Currently, disease models that allow, for example, assessing the risk of apple scab infection are particularly popular. Such models are costly to develop and require a vast amount of data to function correctly. However, once these data are available and the biology of each pest is known, artificial intelligence will enable us to elevate plant protection to an entirely different level. This is especially important in the context of reducing the use of plant protection products and meeting consumer demands for reducing residues in food. AI will undoubtedly contribute to building new models, as it is ideally suited to finding correlations between climatic conditions and the occurrence of pests. This technology could partially answer the challenging task set for food producers within the framework of the Green Deal. For these systems to function effectively, the previously mentioned local meteorological station is essential for obtaining detailed real-time data and analysis. Such a station measures various parameters including rainfall, wind speed, humidity, air and soil temperature, and even leaf wetness.
The situation is similar for irrigation and plant nutrition. Having more precise weather forecasts allows us to create more optimal fertilization schedules for both top dressing, foliar feeding, and fertigation.
In technologies used particularly in protected crop systems or closed systems, we have access to an even wider array of sensors, including soil/substrate pH sensors, water flow and pressure in the installation, electrical conductivity (EC), light and CO2 levels, sap flow in plant stems, and ion content. Together with previously mentioned measurable parameters, this gives us a lot of data that should ideally be analyzed in one system. Giving such a system control over fertilizer mixers used for fertigation, the entire irrigation installation, climate regulation, temperature, light, shading, nutrient composition, and ventilation of facilities is indeed an ambitious task. However, considering how much it could optimize such plant cultivation and take it to a higher level, we can expect such extensively developed autonomous systems controlled by artificial intelligence to emerge in the near future.
In technologies used particularly in protected crop systems or closed systems, we have access to an even wider array of sensors, including soil/substrate pH sensors, water flow and pressure in the installation, electrical conductivity (EC), light and CO2 levels, sap flow in plant stems, and ion content. Together with previously mentioned measurable parameters, this gives us a lot of data that should ideally be analyzed in one system. Giving such a system control over fertilizer mixers used for fertigation, the entire irrigation installation, climate regulation, temperature, light, shading, nutrient composition, and ventilation of facilities is indeed an ambitious task. However, considering how much it could optimize such plant cultivation and take it to a higher level, we can expect such extensively developed autonomous systems controlled by artificial intelligence to emerge in the near future.
Another example are studies focusing on the use of artificial intelligence (AI), deep learning (DL), and machine learning (ML) to develop fast, accurate, and reliable methods for analyzing soil water content (SWC) and soil texture.