Machine Learning in Agriculture: What It Can Do Now and in the Future
In this article, we explore various machine learning applications that can help farmers to increase yield, meet the ever-growing demand for agricultural products, and save the environment.
In a way, successful farming comes down to making complex decisions based on interconnections of a multitude of variables, including crop specifications, soil conditions, climate change, and more. Traditionally, farming strategies have been applied to an entire field or its part at best. Machine learning in agriculture allows for much higher precision, enabling farmers to treat plants and animals almost individually, which in turn significantly increases the effectiveness of farmers’ decisions.
Effective harvesting comes down to detecting the most potent acres and crops on a particular day. Today’s yield prediction technologies don’t base decisions solely on historical data but also utilize computer vision software coupled with smart weather analysis to meet the ever-growing agricultural demand. ML has a tremendous impact on the effectiveness of crop classification and quality, agrochemical production, disease detection and prevention.
Even experienced farmers have a hard time differentiating two very similar plants as in many cases it’s only slightest variations in color or shape that set them apart. Image analysis significantly raises the accuracy and speed of species identification, which also saves farmers’ time and resources. ML algorithms can precisely detect a particular plant type by assessing the leaf vein map containing the decisive information.
Breeding for the desired features in crops is a very resource-intensive and time-consuming process, which however drives much value in commercial agriculture. Currently, the world’s brightest minds in prescriptive plant breeding and AI consultants are exploring various ML applications to make their decisions more informed.
A study in the 2019 volume of Plant Phenomics describes how researchers used drones and on-field sensors to gather lavish amounts of data related to soybean cultivation nuances. The gathered data sets include basic information about environmental conditions such as humidity and temperature and in-depth information about row spacing and seeding density.
By feeding cleaned data sets to ML algorithms along with yield results, researchers were able to create a predictive model that can successfully identify which plant types would be the most prolific under specific conditions. Although the research is based solely on soybean cultivation, these findings unveil extremely promising business opportunities for the agricultural sector as a whole.
Despite that agricultural professionals’ instincts, knowledge and experience are undeniably useful, this ML-driven approach is superior to conventional techniques of selective breeding. It allows farmers to identify what exact plant traits would result in the most fruitful yield under specific growing conditions.
Decades ago, agrochemical products entered the mass market and revolutionized agriculture. Various chemical products including pesticides, antibiotics, and insecticides had finally made it possible for farmers to combat the havoc wreaked by unwanted insects and bacteria. However, lessening crop losses with the help of these chemicals has a detrimental impact on the environment and human health.
ML can make these chemicals much less hazardous and more environmentally friendly. The researchers at the University of Delhi have developed an ML-based tool called NeuroPIpred, which helped them to come up with chemicals that are deadly only to specific insects. So-called neuropeptides are protein-like molecules that are responsible for insects’ essential biological and behavioral activities such as metabolism and mating. NeuroPIpred takes the neuropeptide composition of a specific insect as input, and based on this information develops a new mutant neuropeptide that is lethal only to that insect species. The described method is nothing short of revolutionary as the problem of harmful agrochemicals have been a global concern for the past few decades.
Soil Analysis and Disease Detection
The wide range of soil characteristics including moisture, temperature, and nitrogen levels play an important role in crop wellbeing. Traditionally, farmers spread equal amounts of pesticides per square meter. Such a wasteful usage of resources not only significantly affects farmers’ budgeting but also tampers with flora and fauna, reducing the number of pollinator species.
ML tools in conjunction with image analysis software can analyze the soil erosion levels and health conditions of individual crops. The obtained data is then used to identify which exact regions of the field are infested, allowing farmers to use pesticides exactly where it’s needed. Such treatment customization can have a tremendous environmental impact globally.
For example, the Plantix app, developed by Berlin-based AI startup PEAT, combines ML with image recognition in one mobile app to analyze plant diseases and nutrient deficiencies. Accessible tools like this are real game-changers, especially for smaller farmers. Watch the video below to see Plantix in action:
On a larger scale, these apps are substituted with computer vision-powered drones connected to IoT systems that can collect visual and thermal data. Switzerland-based startup Gamaya, for example, puts exactly this concept into practice.
California-based Trace Genomics takes a different approach to soil health assessment. Instead of detecting already infected crops, the company focuses on preventing diseases by providing in-depth soil analysis with the help of ML. Farmers send their soil samples directly to Trace Genomics and receive back a comprehensive report of soil condition and evidence-based recommendations on soil management. Watch Trace Genomics’ CEO explain how ML can drive value for farmers:
Weeds are the next most serious threat to crop farming since they grow very quickly, compete with the crop, cause various plant diseases, and ultimately lower yield and farmers’ profits. Currently, herbicides are the most popular solution, but at the same time they raise many environmental and economic concerns. Moreover, as time goes by, weeds are learning to adapt to chemicals and resist them, which makes widespread usage of herbicides even more questionable.
ML is set to transform how farmers detect weed-infested regions. For example, California-based Blue River Technology has developed an ML-powered robot called See & Spray, which can autonomously identify weeds and apply herbicides only on unwanted plants instead of the whole field. The technology’s results are jaw-dropping as it lowers the volume of chemicals used by 80%. Besides introducing these precision farming methods, See & Spray also helps farmers to identify the most persistent weeds, which makes tailoring herbicide programs easier and more effective. Watch Blue River Technology's C-suite explain how See & Spray helps farmers save money:
Given that 70% of global fresh water supply accounts for irrigation, according to the World Bank, you can imagine how essential water is for the crop wellbeing. Water management becomes especially relevant for regions where rain is a rare occurrence. However, a bigger problem here again concerns the environment, as the world population and food demands are growing. Water management is slowly becoming a global concern, and ML-powered smart irrigation tools can help mitigate this problem.
Advanced ML tools are connected to various on-field sensors or satellites that analyze soil temperature, moisture and nutrition levels and forecast weather conditions. With enough data, which is seamlessly shared among connected devices, the irrigation system becomes smart and automatic, which makes water usage as efficient as possible and reduces effort needed in the process.
For example, California-based ConserWater uses satellite data, weather, and topography to determine the exact amounts of irrigation needed for a specific field region. The beauty of the ConserWater app is in its convenience: it doesn’t need ground sensors to operate, which significantly lowers the adoption barrier. Despite the app’s simplicity, ConserWater claims that it saves farmers 30% of consumed water.
Livestock production systems also require substantial data analysis and prediction to be environmentally-friendly and economically effective. ML can be used in various livestock applications including for cattle and dairy production, selective breeding, and more.
Livestock Health Control
The more farmers know about the health of their herds, the more resource-efficient the production becomes. For example, CattleEye (Northern Ireland) leverages ML to identify the health conditions of cows to quickly react to possible diseases and control animals’ overall wellbeing.
By utilizing computer vision applications, the technology draws parallels between the current set of parameters and historical data. How does a cow moves compared to yesterday? Is it going lame or seeking heat spots? How well did it eat today? The software answers all those questions individually for each cow and notifies farmers if an animal needs attention and special treatment.
This significantly eases farmers’ jobs, lowers antibiotic usage, and eliminates the possibility of human-related errors in evaluating cows’ health. Essentially, it allows farmers to start treating diseases way before they aggravate. The company claims that it helps farmers save an average of $400 per cow per year and increases milk output up to 30%.
The demand for animal protein is growing at an accelerating pace each year, which calls for more cost-efficient approaches to meat production. The global meat sector value is expected to hit $1.1 trillion by 2023, while the cultured meat market will be valued at $214 million by 2025.
ML-driven innovation in livestock control allows farmers to increase the outputs while requiring fewer resources. For example, New Zealand-based company Vence utilizes ML in combination with connected sensors to provide farmers with virtual fencing. This method implies improved control of rotational and strip grazing to substantially increase the yield.
Each cow is now equipped with a collar that emits a sound whenever an animal walks near a virtual fence set up by a farmer in the app. This allows for remote and more efficient control of livestock grazing and enables more detailed grassland management. Vence claims that farmers are set to save up to 30% on fencing costs and minimize their expenditure down to $1.25 per animal per month. As an additional feature, the app can also monitor cattle health by analyzing data sent from the collar to a smartphone.
Despite agriculture being a very data-centric industry, there are significant challenges on the way to the global adoption of machine learning. The first layer of complexity lies in the variance of conditions based on location. For example, a fertilizer program applied in Australia would most likely be irrelevant in the United States due to significant differences in humidity, soil types, daylight hours, temperature, and many other factors. Currently, many promising startups’ solutions can be effective only for specific crops in specific regions.
Although AI hype is seemingly over, the technology is still nascent. It’s just a matter of time until each region has a few big players at the forefront of ML-driven services, or when already established startups will find ways to tailor their products based on regional conditions.
Solely for this reason we will have to wait another decade before ML will become an industry standard for agriculture worldwide. In most cases, testing and validation of the same concept in different environments is a very tedious and lengthy process. On the other hand, with the pace of today’s data accumulation and the advancement of image analysis, companies will inevitably come up with algorithms that can work across a much wider set of conditions.
Next, upfront costs are considerable right now. This will be a pressing issue especially in developing countries, where agriculture often plays an important economic role.
Moreover, some experts argue that farmers can be very reluctant to the idea of automation. Many farms across the globe have a complex set of principles and assumptions that impact their judgment of these technologies in relation to their industry. In livestock management, for example, linking farmer-animal relationship to the quality of output product is a common practice. Farmers’ attitude needs to be understood and considered when creating marketing strategies for such products, as only a full recognition of the technology will make this digital revolution complete and valuable.
The value of smart automation is widely recognized across many verticals, proved by examples of AI in fintech or AI in real estate. In agriculture, this segment of technologies is becoming essential. With data at the core of farming decisions and the development of agrochemical products, the potential is immense. Perhaps, more importantly, ML is set to become a behind-the-scenes enabler of a more sustainable use of natural resources and a huge contributor to a better environment.
However, for this technology to have a tangible impact on agriculture, it needs a widespread recognition among stakeholders, a different mindset from farmers, and sufficient funding. This is a long-haul game. Companies need to be ready to reinvent themselves, learn new skills, and adapt to the rules imposed by big data.
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