IoT and AI are increasingly redefining industrial processes around the world. IoT and AI have combined to unleash the potential of data faster than ever before, with smart energy grids, predictive maintenance sensors, and wearable gadgets like smartwatches and AR/VR goggles.
No sector of the economy is immune to the benefits that IoT and AI can provide. It is not any different in agriculture.
These technologies have successfully transformed the agriculture sector in unimaginable ways, addressing the world’s most pressing issue—bridging the gap between food demand and supply.
It is critical for the farming sector to use technologies such as artificial intelligence (AI), machine learning (ML), the internet of things (IoT), analytics, and blockchain. It not only promotes intelligent agriculture, but it also reduces stake and input costs by 40-60%, increasing savings and output while preserving soil fertility.
According to a recent report, AI in agriculture will grow at a CAGR of 25.5% and reach $4 billion by 2026. Growing food demand and natural resource depletion are the driving forces behind AI in agriculture. Technological advancement has brought about a new revolution in the industry, with new stakeholders stepping forward to participate in this agricultural revolution. Businesses are now developing a scalable agtech software solution that aims to streamline the farming process by hiring a top-notch agtech mobile app development company in the United States.
What is the impact of AI on agricultural practices?
AI in agriculture is still in its early stages. After harvest, the technology has the potential to dramatically improve agricultural consulting services, aid farmers, and provide impartiality and transparency to value chains. As a result of the opportunity, a big number of startups are flourishing in the area.
1. Crop and Soil Monitoring
Historically, crop and soil monitoring relied on human observation and assessment to determine crop health and soil quality. This technique, however, is neither exact nor timely.
Conversely, we may now employ drones (UAVs) to collect aerial image data and train computer vision models to use it for informed crop and soil status monitoring.
This data can be analyzed and interpreted by visual sensing AI to:
- Track crop health and anticipate yields with accuracy
- Quickly identify crop malnutrition compared to people
Farmers may take immediate action by employing AI models to alert them to specific problem areas. Farmers can take use of the benefits of AI in crop planning, reporting, inventory, accounting, and equipment maintenance by developing agriculture software.
2. Observation of Crop Maturity
Manual observation of wheat head growth phases is exactly the type of labor-intensive task that AI may help with in precision agriculture.
Researchers were able to build a “two-step coarse-to-fine wheat ear recognition system” by collecting pictures of wheat over three years and in varied lighting situations at various “heading” stages.
Farmers no longer required to go out into the fields on a regular basis to monitor their crops since this AI model could detect wheat growth phases more precisely than human observation.
3. Insect and Plant Disease Detection
It’s well known that AI and machine learning can identify and analyze crop fertility, maturity, and soil quality, but what about agricultural circumstances that are less predictable?
With the introduction of AI, it is now possible to detect plant ailments and pests using deep learning-based image recognition technologies. This is accomplished by developing models that can “keep an eye” on plant health through the use of image classification, detection, and segmentation approaches.
4. Spraying with Intelligence
Computer vision is useful for detecting agricultural problems, but it can also help avoid them. Drones equipped with computer vision AI can apply fertilizer or pesticides uniformly across a field.
Because of real-time detection of target spraying areas, UAV sprayers can operate with high precision in terms of both the area and volume to be sprayed. As a result, there is a much lesser risk of poisoning water supplies, crops, humans, and animals.
5. Spraying with Intelligence
Not every AI is pursuing weeding in the same way as clever sprayers are. Other AI-powered robots are eradicating unwanted plants in more direct ways.
Yet, weed detection does not save the farmer as much time as computer vision can spot an insect or an abnormally behaving chicken. To be even more helpful, the AI must locate and remove the weed.
Agriculture’s Artificial Intelligence Future
Agritech specialists expect that in the future, agritech enterprises will increasingly use artificial intelligence (AI) to help crop insurance and loans in order to better training and validation of scaling solutions that use satellite data.
Because of the large variance in crop farmed, varieties, staggered sowing intervals, and the time-consuming nature of traditional crop-cutting trials for yield evaluations, there will be more opportunity to use AI for quick ground yield evaluation.
If you are in the agriculture industry and want an automated software solution to streamline your operations, contact a skilled agriculture software development company immediately!
