ML and Data Engineering Applications in Agriculture

The primary purpose of integrating agriculture with ML and DE is to boost ultimate crop yield

Machine Learning and Data Engineering are being developed. The combination of Agriculture and Machine Learning aims to boost ultimate crop output, save time and money, and regulate every stage of plant and animal growth. ML approaches assist in collecting agricultural data and make the process easier for farmers. The applications listed below might be beneficial in establishing communication between data collecting and actual manufacturing.

1. A unified protocol: A single, defined protocol for integrating electric and electronic components across manufacturers is advantageous. All mechanical and automotive equipment must be assembled into one giant machine like a LEGO set. Protocols should be used to connect all components of the final structure.

2. Internet of Things (IoT): Various gadgets in a system must be connected to the Internet and capable of real-time interaction. The number of sensors and their applications is increasing yearly and is expected to reach 250 billion in the next five years. The development of software solutions based on this tremendous number of sensors makes it difficult to gather and store information in a single location.

3. Drones and remote sensing: The advancement of information technology and agricultural research has enabled the integration of drones and sensing, resulting in the growth of precision farming. Such a plan maximizes profit and output while requiring the most minor input and using available resources best. One of the intriguing uses is based on GPS and GIS technology, and it assists in calculating the best pathways for tractors.

4. Data collection and social network communication: The construction of an efficient chain involving local food production and livestock systems is critical to this. This method will result in a better knowledge of the overall efficiency of the food supply chain, and the integration of these two systems will have a long-term positive environmental effect and increased food security.

5. Computer vision: Image analysis and detection are two of the fastest expanding areas of informatics study. All automated machines begin with sensing, most commonly employing cameras to collect data about the crop and the location of the harvesting system. This is usually an RGB camera, a depth camera, or a lidar system.

6. Data transparency and blockchain: A blockchain is an encrypted data technique that searches for every change made to a target entity, such as storing, connecting, and recovering. The modern agricultural sector has advanced and now employs blockchain in the agriculture value chain to address many difficulties.

7. Augmented Reality: This subject is still in its early stages but has already shown great promise in a specialized industry that demands 3D picture resolutions. Visualization of animals, illnesses, and agricultural damage to assess and cure them. Augmented Reality holds great potential shortly, especially when paired with Artificial Intelligence (AI).