Agricultural fields are no less than a battlefield. Irrespective of terrain, geography and type, crops have to compete against scores of different weeds, species of hungry insects, nematodes and a broad array of diseases. Weeds, or invasive plants, aggressively compete for soil nutrients, light and water, posing a serious threat to agricultural production and biodiversity. Weeds directly and indirectly result in tremendous losses to the farm sector, which convert to billions each year worldwide.
To combat these challenges, the farm sector is looking at Artificial Intelligence (AI) based solutions. Here’s a look at two such initiatives powered by NVIDIA Corporation (NVDA).
The damage wrought by plant pests and diseases can reach up to 40% of global crop yields each year as per estimates by the Food and Agricultural Organization of the United States. Among the pests, “weeds are considered an important biotic constraint to food production.” The competition for survival between weeds and crops reduces agricultural output both qualitatively and quantitatively.
It is estimated that the annual cost of weeds to Australian agriculture is $4 billion through yield losses and product contamination. The Weed Science Society of America (WSSA) reports that on an annual basis, potential loss in value for corn is $27 billion and for soybean it is $16 billion based on data from 2007 to 2013. In India, an assessment by the Directorate of Weed Research shows that India loses crop worth $11 billion every year to weeds.
One of the most common ways to control weed is to spray the entire field with herbicides. This method involves significant cost, wastage, health problems and environmental pollution. While the real cost of weeds to the environment is difficult to calculate, “it is expected that the cost would be similar to, if not greater than, that estimated for agricultural industries,” according to a note by the department of environment of Australia.
Today, advanced technologies are being increasingly applied to a number of industries and sectors, agriculture being one of them. One such technique is that of precision farming, which allows for farmers to reduce their use of chemical inputs, machinery and water for irrigation by using information about the soil, temperature, humidity, seeds, farm equipment, livestock, fertilizers, terrain, crops sown, and water usage, among other things. A growing number of companies and start-ups are creating AI-based agricultural solutions.
Cameras, sensors and AI on the fields allow farmers to manage their fields better and use pesticides more precisely. Blue River Technology's See & Spray uses computer vision and AI to detect, identify, and make management decisions about every single plant in the field. In 2017, Blue River Technology was acquired by Deere & Company (DE). Today the See & Spray, which is a 40-feet wide machine covering 12 rows of crops, is pulled by Deere tractors and is powered by Nvidia.
The machine uses around 30 mounted cameras to capture photos of plants every 50 milliseconds and these are processed through its on-board 25 Jetson AGX Xavier supercomputing modules. As the tractor continues to move, the Jetson Xavier modules running Blue River’s image recognition algorithms make super quick decisions based on the image inputs received on whether it is a weed or crop plant. See & Spray machine has been able to achieve good success by using less than 1/10th the herbicide of typical weed control.
Further, a research paper published in 2018 by M Dian Bah, Adel Hafiane and Raphael Canals proposed “a novel fully automatic learning method using convolutional neuronal networks (CNNs) with an unsupervised training dataset collection for weed detection from UAV images.” Drone images of beet, bean and spinach crops were used for the study. The researchers used a cluster of NVIDIA Quadro GPUs to train the neural networks. The researchers say that, “using NVIDIA Quadro GPUs shrunk training time from one week on a high-end CPU down to a few hours.” The study archived a precision of 93%, 81% and 69% for beet, spinach and bean, respectively.
While these initiatives are working on precision-based use of any chemical product in the fields, neural networks can be trained to detect ‘infected areas’ in plants using images. One such study is being done on the detection of symptoms of disease in grape leaves. Early detection can play an important factor in preventing a serious disease and stop an epidemic spread in vineyards.
The use of technology can help in solving multiple problems faced by farmers, saving valuable resource and reduce the damage done to the environment. The statement by FOA chief, “the future of agriculture is not input-intensive but technology-intensive” aptly sums up the role that technology and technology providers will play in the farm sector.
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