Agriculture is a key sector for food security and economic development in many countries. However, effective management of agricultural resources and improvements in agricultural productivity often depend on sophisticated solutions based on big data analytics. In today’s material an agritech expert Denis Bunkov shared his insights on how agriculture data can be analyzed with AI and ML.
For example, to compare the effect of different tillage practices on maize yields, it is necessary to take into account the latitude and longitude of the trial plots, the number of years since the land was first used, and the crop rotation;
soil texture; weed and pest control; precipitation and potential evapotranspiration during the growing season, and their differences in water availability.
There is a lot of data. This is where artificial intelligence (AI) and machine learning (ML) help: they are increasingly used for analysis and forecasting in agriculture and often outperform traditional statistical parametric models such as generalized linear models,
Artificial intelligence in agriculture
Cognitive computing includes a range of technologies and algorithms that allow computers to imitate human thinking and decision-making.
Starting in 2015, intelligent automation technology started to be applied in several ways in the field of agriculture:
— yield estimation;
— classification of species and traits of crops using satellites;
— classification of soil texture;
— classification of leaf diseases;
— water quality flow assessment;
— agricultural land identification.
Autonomous decision-making systems can predict crop yields and recommend the best time to plant and fertilize by analyzing climate, soil, and crop data. It helps optimize the use of water, energy, and fertilizers, which helps reduce costs and environmental impact. Monitoring systems powered by intelligent automation technologies can detect plant diseases and yield irregularities, enabling rapid response and minimizing losses.
Machine learning and data analysis
The focus of machine learning is on the creation of algorithms that can learn from data and make predictions. In agriculture, machine learning finds application in the following areas:
— leaf image analysis;
— soil analysis;
— cattle management.
Machine learning can detect pests and diseases in photos of plants, helping farmers quickly identify the presence of these pests. Such algorithms can analyze soil chemistry and make recommendations for optimal fertilizer and tillage techniques. ML is also used to monitor and manage livestock, determine optimal feeding rations, and detect animal diseases.
Comparative studies have shown that random forests is the optimal ML algorithm for use in agriculture: the algorithm chose the interaction of the most important factors. The random forests forecast is obtained as a result of aggregating the responses of many trees. The training of trees can be carried out independently of each other in different subsets. This makes it easier to retrain, and the accuracy of an ensemble is higher than that of a single tree.
Advantages and features of using AI and ML in agriculture
Both local agricultural issues and global problems can be solved through the use of intelligent automation:
— increased productivity. By analyzing data, you can optimize processes and resources to increase yields and reduce losses;
— cost reduction. AI and ML help manage resources more efficiently, reducing fertilizer, water and energy costs;
— agricultural sustainability. Agriculture becomes less harmful to the environment through more precise management;
— hunger relief. Analyzing data and predicting crops helps improve the world's food security.
But the exact model of analysis and forecasting is often too complex for people to interpret the logic of the prediction. It's a "black box" effect. We cannot explain what the model has extracted from the data, why it is predicting a particular value for a particular case, and when it is tending to make an error. For example, an AI model might suggest that a farmer change his current tillage method from conventional tillage to no-tillage. The farmer would increase his yield by 10%. Surely the farmer wants to know why the model predicted this. The model developer also needs to know whether the model has extracted meaningful patterns from the agricultural data. For these purposes, intelligent automation solutions must be interpretable and explicable. It is critical to find a compromise between the accuracy and interpretability of statistical models. With the development of ML, it has become easier to do this.
Artificial intelligence and machine learning provide agriculture with powerful tools for analyzing data and making decisions. These technologies help farmers increase productivity, reduce costs, and improve agricultural sustainability, which is an important step toward improving global food security and reducing environmental impact. Agriculture remains an area where cognitive computing can revolutionize, and its potential is just beginning to be explored.
by Denis Bunkov
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