How machine learning is gradually changing modern agricultural practices

By admin

Just like checkers, machine learning is a game of weighing dozens of options and factors. It puts all risk factors into consideration and plans the most efficient move. This is one of the fastest growing fields under the artificial intelligence (AI) field – in fact, it is a subset of AI. This technology has been deployed and it is in use in most of the modern agricultural technology to create accurate solutions and unprecedented scale.

Remote sensing systems are widely used in building efficient decision support tools in the farming system so as to improve nutrients in the soil that will eventually translate into improved yield production. This implies that there will be reduced operating costs and environmental impact. However, remote sensing based methods needs enormous amounts of remotely sensed data to be processed by other tools. This is where machine learning kicks in. The machine learning based systems analyses a series of inputs and handles non-linear tasks.

With the impact of global climate change, majority of the crops are badly affected in terms of performance over the past two decades. Forecasting the farm productivity well ahead of its harvest is critical for the policy makers. For farmers it will them take appropriate measures for marketing and storage. These predictions will further help the other associated parties or industries with planning the logistics of their business. This is because crop production is a complex process that is often influenced by agro-climatic input variables. Agricultural input variable range from farmer to farmer and field to field. Collecting such data sets on a vast piece of land is not an easy task. These data sets can be used to predict the trends and their influence on crops of that particular region. Thus, machine learning uses algorithms to parse data, then learn from it and make informed determination without any human help.

In the recent years, plant breeders have been looking for specific trait that suits environmental conditions by looking for traits that will help a certain type of a crop that uses water efficiently together with nutrients, climate change, and resist disease. So that the plant inherits that beneficial trait, the right sequence of genes must be found. However, which sequence exactly is the right one? This clearly shows that plant breeders face millions of choices when trying to come up with a new variety.

Deep machine learning algorithms may even take more than a decade of field data. Insights of how the crops performed under different climatic conditions and the inherited characteristics are critical in developing probability models. Machine learning uses this information that is beyond human grasp to predict which particular genes are most likely to contribute into a beneficial trait.

With machine learning developments, plant breeding has become more efficient and capable of analysing a much wider set of variables. These computer simulations are used by scientists to conduct early tests to evaluate productivity of a variety of crops and their performance under different climatic conditions, weather patterns, soil types, and other factors.

When trying to tracking any kind of diseases, early and accurate identification is a very important factor. The traditional ways of spotting plant disease can only be done by visual examination. This process is characterised by inefficiencies and is often prone to human error. For a well programmed and trained computer, identifying plant disease is just but pattern recognition. Hundreds of thousands of high defined images of diseased plants are collected and the machine learning algorithm spots the severity and disease type. It is though that even in the near future, machine learning may even recommend the appropriate management practices to limit loss from diseases.

Machine learning in the agricultural field allows more accurate disease diagnosis – eliminating any inefficiencies and time wastage. Farmers can upload field images that are taken by satellites, drones, smartphones or land based rovers and use the software to diagnose and come up with the ideal management plan. This tool will help manage food insecurity and famine around the world.

Machine Learning is eliminating the time wasted in traditional programming and instead gets computers to program themselves. In a nutshell – machine learning is like farming; nutrient is the main data, gardener is the farmer, seed is the algorithm while the plant is the program.

Thus the key areas machine learning plays its critical role is:

  • Computational biology – designing remedy to diseases or simply drugs;
  • Breeding and coming up with ideal traits;
  • Disease identification;
  • Predicting the climate change;
  • Robotics and auto driven tractors
  • Soil nutrient evaluation; among others