Imagine a farmer walking through a field, the plant leaves are a vibrant, bright green. All appears normal to the naked eye. But down below, a disaster is occurring. A fungal spore, which has been picked up in the wind from a border area, has settled on a leaf. With the proper balance of heat and water, it will start to develop, creeping unseen through the crop, and by the time brown patches begin to show, it will already be too late to prevent the damage.
For thousands of years, this has been the way it was with farming: an ongoing struggle against unseen foes, waged with response, rather than foresight. A new revolution is about to alter this old battle. With predictive analytics, the farmers are not waiting to see the enemy; they are viewing it approaching and stopping it before it has even begun.
Predictive analytics is essentially high-tech weather forecasting, but for plant disease. There is no crystal ball involved or guessing. Instead of using vast quantities of data, gigantic computers, and machine learning software to pick out patterns that are beyond the capacity of a person to notice, though, it starts with gathering data. Satellites orbiting the Earth transmit high-resolution images of fields, measuring plant health by how much light they reflect. Field sensors measure the soil, tracking moisture, temperature, and nutrients. Weather stations give hyper-local readings of rain, humidity, wind velocity, and air temperature. This steady stream of information creates a crazily detailed digital image of the farm, updated in real-time.
Raw data is useless by itself. That's where the machine learning programs come in. Scientists first "train" these software programs by giving them access to old data.
For example, they subject the model to the weather conditions—a specific string of three days of high humidity and 70- to 80-degree temperatures—that led to a disastrous potato blight outbreak in the past. They do this thousands of times, with various diseases and crops. The program is not programmed with a single rule; instead, it is taught to recognise the subtle, underlying patterns that cause issues. It learns to see that there are specific sets of variables that create a "perfect storm" for some specific fungus or bacteria.
Once the model is trained, it can be let loose on the new, real-time streams of data coming in from the fields. It's scanning the data all the time, looking for that fatal pattern it learned to recognise. Instead of letting the crop get sick, the system alerts the farmer to possible sickness. It can post a message on a phone: "There's an 85% likelihood of gray leaf spot developing in the southeast quadrant of your field in the next 72 hours. Conditions are very favourable." This is a complete game-changer. It repositions agriculture from a reactive to a proactive activity. The farmer is not the victim of circumstance but a commander in chief executing a strategy of defence.
Pre-warn allows for being precise and targeted. Instead of spraying an entire farm with fungicide every week as a precaution—a costly and environmentally damaging gesture—the farmer can act surgically.
They can address only the hotspot that poses a great risk, and only at the exact time it will be most beneficial. It is an efficient method, known as precision agriculture, that saves tremendous money. Farmers spend less on chemicals, gasoline, and manpower. But the benefits far exceed the checkbook. It slashes significantly on chemicals that are dumped into the environment, conserving local water systems, insects like essential bees, and other wildlife. It avoids pests becoming resistant to treatment, as they become less exposed to them.
The power of these forecasts also makes it easier to provide answers. If the model predicts an enormous risk of disease due to rising humidity, the farmer can possibly do away with chemicals altogether. They can alter their watering pattern to have drier leaves or slim plants to create more air space in between them.
The data provides the intelligence required to collaborate with nature to prevent the problem, rather than trying to fight it using harsh chemicals after it has already taken hold.
The impact of this technology on food security is gigantic. Crop blights are more than just minor annoyances; they have shaped the course of human history. The Irish Potato Famine during the 1840s, which was due to a potato blight, resulted in a million deaths and another million people emigrating. Plant diseases still destroy as much as 40% of food crops globally each year. In a world where populations are increasing and climates are shifting, wasting that much food is just not on the agenda.
Predictive analytics functions as an early warning system for the world. By analysing global weather and satellite trends, models can predict the migration of diseases across continents and allow countries to gear up for invasion from surrounding states. A Kansas wheat farmer can be warned of a wind-spreading rust disease that started in Mexico and is traveling north with the weather patterns.
This is not a fantasy of the future. These systems are already in use globally. Universities and companies have created models for high-value crops such as wheat, corn, soybeans, and rice, and for high-value fruits and vineyards.
The farmers pay for these services and are notified just like one would get a warning about a storm. The technology improves and becomes more affordable every year, spreading from big factory farms to smaller family farms.
Of course, the system isn't infallible. It is based on having good, clean data. A poor sensor or a dearth of weather data can affect the accuracy of a prediction. The models would also need to be updated and fine-tuned regularly as climates shift and plant diseases evolve. There is also the accommodation period for farmers to accept the algorithmic recommendations against centuries of customary wisdom. But the potential is there. Through the power of data, we are beginning to learn the language of the plants and the world.
We are beginning to listen to the whispers of an impending blight long before it begins to scream. This silent revolution in farming promises a future of richer, more sustainable, and more secure crops, finally laying to rest the ancient struggle between farmer and blight.