The way to analyze agricultural data with artificial intelligence and machine learning

Agriculture is a key sector for food security and economic development in lots of countries. Nevertheless, effective management of agricultural resources and improvement of agricultural productivity often depend upon sophisticated solutions based on Big Data evaluation. In today’s material, agricultural technology expert Denis Bunkov shared his observations on the probabilities of analyzing agricultural data using artificial intelligence and machine learning.

For instance, to check the consequences of various tillage practices on corn yields, the latitude and longitude of the test plots, the variety of years for the reason that land was first used, and crop rotation must be taken into consideration;

soil texture; weed and pest control; precipitation and potential evapotranspiration throughout the growing season and differences in water availability.

There may be loads of data. That is where artificial intelligence (AI) and machine learning (ML) help: they’re increasingly used for evaluation and forecasting in agriculture and infrequently outperform traditional statistical parametric models comparable to generalized linear models,

Artificial intelligence in agriculture

Cognitive computing covers a variety of technologies and algorithms that allow computers to mimic human considering and decision-making.

Since 2015, intelligent automation technology has began to be utilized in agriculture in several ways:

— yield estimation;

— classification of crop species and characteristics using satellites;

— soil texture classification;

— classification of leaf diseases;

— water flow quality assessment;

— identification of agricultural land.

Autonomous decision-making systems can predict crop yields and recommend the perfect time to plant and fertilize by analyzing climate, soil and crop data. It helps optimize the usage of water, energy and fertilizers, which helps reduce costs and environmental impact. Monitoring systems using intelligent automation technologies can detect plant diseases and crop abnormalities, enabling quick response and minimizing losses.

Machine learning and data evaluation

Machine learning focuses on creating algorithms that may learn from data and make predictions. In agriculture, machine learning is utilized in the next areas:

— leaf image evaluation;

— soil evaluation;

— cattle management.

Machine learning can detect pests and diseases in plant photos, helping farmers quickly discover the presence of those pests. Such algorithms can analyze soil chemistry and make recommendations on optimal fertilizer and tillage techniques. ML can be used to watch and manage cattle, determine optimal food rations, and detect animal diseases.

Comparative studies have shown that the optimal ML algorithm to be used in agriculture is random forests: the algorithm chosen the interaction of a very powerful aspects. The random forest forecast is obtained by aggregating the responses of multiple trees. Tree training might be performed independently on different subsets. This makes retraining easier and the accuracy of the ensemble is larger than that of a single tree.

Benefits and features of using AI and ML in agriculture

Each local agricultural problems and global problems might be solved through the usage of intelligent automation:

– increased productivity. By analyzing data, you possibly can optimize processes and resources to extend yields and reduce losses;

– cost reduction. AI and ML help manage resources more efficiently, reducing the prices of fertilizers, water and energy;

— sustainable development of agriculture. Agriculture becomes less harmful to the environment due to more precise management;

— satisfying hunger. Analyzing data and predicting crop yields helps improve global food security.

Nevertheless, the accurate evaluation and forecasting model is commonly too complex for humans to interpret the forecast logic. That is the “black box” effect. We cannot explain what the model extracted from the information, why it predicts a particular value for a particular case, and when it makes an error. For instance, an AI model might suggest that a farmer change his current farming method from conventional to no-till. The farmer would increase his yield by 10%. Surely the farmer desires to know why the model predicted this. The modeler also must know whether the model has extracted meaningful patterns from the agricultural data. To do that, intelligent automation solutions have to be interpretable and explainable. Finding a compromise between the accuracy and interpretability of statistical models is incredibly necessary. With the event of ML, this has change into easier.

Artificial intelligence and machine learning provide agriculture with powerful data evaluation and decision-making tools. These technologies help farmers increase productivity, reduce costs and improve agricultural sustainability, a crucial step towards improving global food security and reducing environmental impact. Agriculture stays an area where cognitive computing has the potential to revolutionize, and its potential is just just starting to be explored.

by Denis Bunk