Agricultural Chemicals

AI for Good: How advanced crop intelligence can help solve food production challenges


By being more effective in how they target crops, growers expe rience the economic benefits of reduced costs for crop protection, reduced crop threats, and therefore increased yields. Gamaya's precision agriculture services equip growers with information about the location, type, and intensity of the weeds infesting their fields. Targeted spraying reduces chemical usage, thereby significantly reducing the farm er's costs, lowering the negative impacts to the environment, and on human health, and likely slows the spread of herbicide-resistance. Not only is the cam era itself small, but it compresses data 100 times more efficiently than other hyperspectral cam eras, making our data processing quicker, less complex, and less expensive than with other hyperspectral sensors.

BOSS Magazine Deep Learning AI Could Help End World Hunger


It increasingly seems that "deep learning" artificial intelligence will have a significant role to play, and a number of notable technologies are now making a real difference for the world's farms. With a deep learning AI system distinguishing between weeds and the sprouts that farmers want to cultivate, Blue River's LettuceBot cuts those losses by up to 90 percent. Agricultural IoT provider OnFarm figures that this ramp-up will see the average farm generating four million data points each day by 2050. In the past, some fairly crude approaches produced notable successes of this basic kind.

Robotic Farmer

AITopics Original Links

Scientists in Denmark are developing an agricultural robot for identifying and eliminating weeds. Using a vision-based approach ensures that the robot covers the field more accurately, turning when it reaches the edge of a field to continue winding its way across the entire plot. "We have shown that [Hortibot] is easy to work and can make turns without a lot of planning," says Jørgensen. The Hortibot team is now planning to equip the robot with modular tools for precision spraying and mechanical weed removal.

Farm Robot Learns What Weeds Look Like, Smashes Them

AITopics Original Links

Bonirob is more than 90 percent effective in destroying weeds in carrot cultivation trials. Bonirob employs a type of machine learning (a stab at artificial intelligence) called decision tree learning. Researchers show Bonirob lots of pictures of healthy leaves that are tagged to be good, and pictures of weeds that are tagged to be bad, and the machine makes a series of choices based on observed in new data to judge whether a plant in the field is good or bad. In carrot cultivation trials, the death stick was more than 90 percent effective, according to Deepfield communications lead Birgit Schulz.

How AI is helping farmers to save thousands of dollars – AI.Business


Farmers use farm management software and GPS receivers to map their fields, and to track the yield (amount of crop) that they get from every square meter. Based on their collected and mapped (georeferenced) data, farmers can generate prescription maps which specify how much fertilizer to apply in each region of the field, how densely to plant seed in that region, and so on, in order to optimize yield and minimize unnecessary chemical applications. The driver steers the tractor, the tractor pulls the planter, and the onboard computer controls the seeding rate based on where the planter is in the field. By incorporating artificial intelligence into greenhouses, tomato farmers can maintain centralized visibility to production while greatly reducing the need for daily manual labor to maintain these new facilities.

How AI can help identify weeds and increase field productivity and profitability – AI.Business


Modern technologies of cultivated crop growth during plant growing, which use chemical compositions to avoid weeding, lead to pollution of the environment and products of plant growing with these compositions leading to decayed production. AI was used for weeds identification using an evolutionary artificial neural network to minimize the time of classification training and minimize the error through the optimization of the neuron parameters by means of a genetic algorithm. They presented evolutionary self- organizing map neural network and demonstrated its capabilities to recognize and classify image patterns that represents different planet leaves including weeds. Since neural network classifiers are well suited for real-time control applications, the achieved results suggest that vision systems can achieve real-time discrimination of weed so hardware and software design must be conducted to develop an integrated real-time image processing and an evolutionary neural network classification system to be used directly in the field for real time classification and control for the fertilization machines.

Robots Are Growing Tons of Our Food. Here's the Creepy Part.

Mother Jones

It got another major boost in 2013, when Monsanto, a top producer of genetically modified seeds and pesticides, bought a Silicon Valley weather prediction startup called the Climate Corporation for $930 million. These massive corporations can be trusted to steer their data clients toward smart farming decisions...right? In short, these globe-spanning companies are vying to become one-stop farm shops, selling seeds (often genetically modified), pesticides, data analysis, and ultimately advice on how to knit it all together. These massive corporations can be trusted to steer their data clients toward smart farming decisions…right?

Machine food

BBC News

So autonomous tractors, ground-based sensors, flying drones and enclosed hydroponic farms could all help farmers produce more food, more sustainably at lower cost. As well as big kit, small kit is giving farmers up-to-the-second data on the state of their fields and produce - what Dr Roland Leidenfrost of Deepfield Robotics calls the "internet of plants and fields". Wine makers have used drones to inspect their vineyards for several years, with high-definition cameras and sensors assessing crop and soil health. Japanese firm Spread's automated vegetable factory in Kyoto, due to launch next year, could produce 30,000 lettuces a day, the company says.

How Machine Learning Will Change What You Eat


During the 20th century, advances in fertilizers, irrigation, and mechanized farming technology helped make it possible to feed a dramatically growing world population. Now, advocates say, the next big advance in agricultural technology may come from the digital world, as modern computer vision, precision sensors, and machine-learning technology help farmers use last century's advances more efficiently and precisely to grow healthier and tastier food. And the American Farm Bureau Federation, a farming industry group, has cautioned farmers to make sure they understand how their data is stored by digital providers. They're also trying to help feed a still-growing world population as climate change disrupts farms and populations, and expanding middle-class societies around the world purchase more food.

Will machines help us to be better people?


Cisco defines the Internet of Everything (IoE) as bringing together people, process, data, and things to make networked connections more relevant and valuable than ever before-turning information into actions that create new capabilities, richer experiences, and unprecedented economic opportunity for businesses, individuals, and countries. No more frustration with rude or aggressive drivers, the future connected cars will make us better drivers. Many farmers waste money on excessive chemicals and nutrients that pollute the environment and weaken the ecosystems farmers depend upon: clean water, healthy soils, beneficial insects, mistreated animals, and more. Might seem like an impossible dream today, but it is not; in the future interconnected machines will allow farmers to create profitable farms that will produce an abundance of healthy food while improving the soil, enhancing biodiversity, and protecting habitats.