Agriculture is a so old human activity that popular wisdom is full of proverbs and sayings giving recommendations about the best way to precede in farm duties. Popular wisdom along with the knowledge transmitted from parents to children has determined agriculture practice for centuries. Only in 20th Century the advent of machines and chemical fertilisers and pest-controllers started to change the conditions.
Sensor Yara ALS (Active Light Source) to estimate the nitrogen needs.
Nowadays agriculture has to face a changing scenario. New cultures, new policies, less water available, less public tolerance towards chemicals, less people attracted by rural life, emergent countries and the market dominated by a few, big actors. This changing situation leads agriculture to adopting industrial-like principles: process optimisation, cost reduction, performance improvement.
Information and Communications Technologies (ICT) and Internet of Things (IoT) can help to improve agriculture activities according to the new paradigm. These technologies are related to the ability to generate, to process and to use data from the agriculture process.
Data source can be sensors in the process and activity logs. When data are accessed through Internet and processed in the cloud to provide autonomy to the process we have an IoT process because it is not the farmer who is using Internet but the things themselves, where a thing is the field, an irrigation device or a combine harvester. Let’s see some examples that can be applied to improve agriculture.
An irrigation systemcan be automatized using moisture sensors buried in the soil. When the moisture reaches a critical value determined by the farmer, the system starts to work and will go on while the moisture is below the threshold. When the field is wide enough sensors can be placed along it, and the irrigation system can apply different water flows depending on the local conditions. The system can be improved by incorporating weather prediction, which can be used to delay the irrigation when rains are foreseen. Alternatively, the system can warm the farmer who will make a decision based on the information provided by the system.
Other example is a combine harvester equipped with a sensor able to measure the production per square meter. At the end of the task, there is a map of the field reflecting the production meter by meter. This map can be used during the next season to adjust the fertilisation according to the local needs. Moreover, the most suitable time to fertilise is automatically computed considering weather and soil conditions and the forecasted values.
All these techniques based on sensors, data processing and Internet access to the data, machines and fields allow to improve the farm yield and to reduce the use of resources. At the same time, they allow to cover the blanks caused to popular wisdom by this changing world.
There is a growing concern caused by the possible effects Artificial Intelligence (AI) could have on everyday working life. Recently in the Davos Forum they have dealt with this issue, but two years ago The Economist published an article about the potential job lost that will be caused by this technology.
Films have made Artificial Intelligence familiar to everybody. From Colossus: The Forbid Project, where a super-computer managed to dominate the entire world and stole the girlfriend to its designer; to Ex Machina, where a heartless machine managed to fulfil its ambitions with no moral hesitation. Almost in all cases, it has been portrayed in a dystopian way. However, the AI we will see soon will not look like an android, as Ava in Ex Machina, but it will resemble HAL 9000, the moral disoriented computer from 2001: A Space Odyssey. I think the first AI materialisation we will see is the Cognitive Computation, named by IBM as Watson.
Watson is a machine able to answer questions posed in natural language capable of processing huge amounts of information to give the correct answer. It became known to general public in 2008, when it defeated two human opponents in Jeopardy!, a television contest featuring a quiz competition.
One of the first commercial Watson uses is to support lung cancer treatment by suggesting the best drug combination for every patient. Another application soon available will be to answer call phones in a call centre. Genesys, a company that develops and sells systems for that application, wants to include Watson in its portfolio. Watson will answer the phone, have a conversation with the user and refer him to a human operator if needed. The experience will be quite similar to the current one, but a machine will do a job that requires some intellectual abilities.
Aptitudes like the ones featured by Watson fear analysts there will be a job lostthere where intellectual and routine tasks are done, even if qualification is needed as in accounting, layer assistants, technical writers or drivers. This is similar to fears arisen when the artificial force appeared: machines whose power enabled them to do the same work than a dozen people while they were driven by only one.
Technology has improved artificial force. While at the beginning it was powered by steam pressure, today it is enabled by automation and robotics. Artificial force ousted many workers and make some professions disappear but, at the same time, new jobs requiring higher qualification emerged. Workers had to do a transition from muscle to brain.
On the advent of this new Artificial Intelligence technology, able to carry out intellectual, repetitive tasks, how will be the new transition workers will have to do? It will have to aim at those tasks machines by the moment cannot do: creative and emotional jobs. However, the transition period could be not easy. Required formation could not be afforded by everyone, or to hire a machine could be cheaper than to hire a person. AI cost will be determinant and, considering only Watson hardware cost around three million dollars, it seems not every company will be able to access it.
In any case, we will have to face the old question: to let others to develop the technology and became mere users, or to be the scientific, technological or commercial developers of this new industrial revolution. A Hamlet like decision.
One of those technologies allows machines to discover by themselves the different states an industrial process features. Imagine a computer repeatedly fed with values generated by the sensors installed in an industrial process. Non-supervised machine learning techniques make possible the computer finds out the sensor data belong to, let’s say, three classes and moreover it characterises the classes. What the computer could not do is to name the classes, unless a human operator provides it a clue. That is what the operator does when he examines the computer outcome and assigns the names starting, stopping and running, just to follow the example. But in spite of this limitation, the non-supervised machine learning can be successfully used to detect faults or malfunctions that have never been observed in the past. This is what CARTIF did in the hydroelectric sets of a hydroelectric power station.
Hydroelectric sets are at the heart of hydroelectric power stations. Its role is to transform the energy stored in the mass of water retained by a dam into electric power. Each set is monitored and hundreds of variables are registered: electric current and voltage, temperature measured in the mechanical elements, in the refrigeration and water streams used for refrigeration, flows of water and air, etc. In our case, we have the values recorded along two years during which no fault was detected, and so we had not information about the possible faults. The challenge was to design an algorithm able to detect faults.
The solution developed by CARTIF is based on the SOM (Self-Organising Map) neural network, which is capable of non-supervised learning. The network was fed with all the available data and she was able by herself of discovering the possible states the hydroelectric set could present. The network labels the states in an arbitrary way and to give the correct names a human operator has to collaborate. However, this is not required to detect faults. Since the data used for training represent all the possible non-faulty states, any network input that does not fully fit with those states corresponds to a fault.
This case can be easily identified by checking the similarity between the sensors signals and the prototypes stored by the neural network. When this similarity is too low, it indicates a fault is occurring.
During testing stage, the algorithm implemented by CARTIF was able to detect an overheating twenty minutes before the plant supervision system raised an alarm. It is important to note that our system used already available sensors and no new ones were required.
So, while we wait for the day machines will rule over us, we may use them to implement intelligent algorithms to improve industrial process supervision with no need for high investments.
