IoT and AI are increasingly redefining industrial processes worldwide, from smart energy grids to production maintenance sensors, smart watches, AR/VR goggles, etc. Fortunately, no sector is exempted from the advantages AI/IOT has to offer, as recent reports from Markets&Markets show agriculture will expand at a CAGR of 25.5%, reaching $4 billion by 2026.
It is therefore important to utilize such technologies in the agricultural sector because it brings about intelligent agriculture and reduces stake and input by 40-60%, boosting savings and output while preserving the totality.
This blog post will focus on how these technologies have transformed the agricultural sector unimaginably, bridging the gap between demand and supply.
Crop and Soil Monitoring
Back then, crop and soil monitoring was handled through human observation, which was neither precise nor timely. However, with the emergence of AI technologies, it is possible to get accurate aerial image data with drones (UAVs), train computer vision models, and refer to them for informed crop and soil status monitoring. This data can be analyzed using visual sensing AI to
- Track the crops’ health and accurately anticipate the yield
- Identify crop malnutrition at an early stage
Framers can leverage the advantages of AI in crop planning, reporting, inventory, etc.
Crop Maturity Observation
Observing the growth stages of wheat heads manually is a highly demanding task, but it can be accomplished with absolute precision using AI.
A “two-step coarse-to-fine wheat ear identification system” was created by researchers by compiling images of wheat taken over three years under varied lighting circumstances and at various “heading” stages.
Because our AI model could more accurately identify wheat growth stages than human observation, the farmers no longer needed to make daily treks out into the fields to check their harvests.
Intelligent Spraying
Computer vision, combined with AI, helps identify agricultural issues and plays a crucial role in preventing them. Drones equipped with computer vision AI offer a valuable solution through automated and uniform application of fertilizers or pesticides across entire fields.
Using UAV sprayers ensures remarkable precision regarding the areas and volumes to be sprayed, enabled by real-time detection of target spraying areas. This advanced technology significantly minimizes the risk of contaminating vital resources such as water supplies, crops, and the well-being of humans and animals.
Farmers can effectively optimize chemical usage, reduce environmental impact, and promote sustainable agriculture by leveraging intelligent spraying techniques. This innovative approach improves crop health and productivity and fosters a safer and healthier ecosystem for all living organisms.
Detection of Insect and Plant Diseases
AI and ML have proven effective in detecting and analyzing crop fertility, maturity, and soil quality. However, there has been a need for technology to address unpredictable crop conditions. Fortunately, with the emergence of AI, there is now a solution to automatically detect plant diseases and pests using deep learning-based image recognition technology.
By developing models that employ picture classification, detection, and segmentation techniques, AI can effectively monitor and assess the health of plants. This breakthrough allows for timely identification and intervention, helping farmers mitigate the impact of diseases and pests on their crops.
With AI-powered plant health monitoring, farmers can make informed decisions and implement targeted measures to protect and optimize their yields, contributing to sustainable and efficient agricultural practices.
Yield Prediction Mapping
A farming method called yield mapping uses supervised machine learning algorithms to uncover patterns in massive data sets and recognize their orthogonality in real-time, both of which are crucial for crop planning. Before the beginning of the vegetative cycle, estimating the prospective yield rates of a given field is feasible.
Agricultural experts can now forecast the potential soil yields for a given crop using a combination of machine-learning approaches to assess 3D mapping, social condition data from sensors, and soil color data collected by drones.
Conclusion
In conclusion, as Agri-tech experts continue to enhance the training and validation of scaling solutions utilizing satellite data, the future holds promising prospects for integrating artificial intelligence (AI) in crop insurance and loans within the industry.
With the significant variations in crop types, staggered sowing periods, and the time-consuming nature of traditional crop-cutting trials for yield assessments, the increasing utilization of AI presents a remarkable opportunity for rapid ground yield evaluation. This technology enables efficient and accurate assessments, providing Agri-tech businesses with valuable insights for crop evaluation and decision-making.
By leveraging AI, the agricultural sector can unlock new avenues for improved risk management, precise yield estimation, and enhanced financial services for farmers. The convergence of AI and Agri-tech paves the way for a more resilient and sustainable future in agriculture.
