The estimated population of the world by 2050 will be 9 billion, according to a credible environmental study source. The food requirement is gradually going heights with the growing population. To meet with these requirements, the farming should upgrade and update with the newer technologies.
With IoT already in the house of agriculture, to give easy agriculture another boost, Artificial Intelligence has made it much more flexible to the farmer’s earnings. This will also boost the growth of production of vegetables and will help the nation develop. Proximity Sensing and Remote Sensing are two technologies which are used primarily for intelligent data fusion.
Various robots, bots, drones that are powered with AI can be deployed into a farm and can be powered with the advantages of IOT that will connect directly to the farmer’s handy devices: mobile phones and other screens.
Applications in the Agriculture industry:
Soil and crop monitoring:
The primary requirements of remote sensing techniques along with hyper spectral imaging and 3d laser scanning that are powered with algorithms with AI can monitor the crop and soil health. Drones, which are always active in the farms, are powered with High-resolution cameras and RGB colors. They help understand and identify areas with weeds, which crops need water, plant stress level in mid growth stage. In terms of infected plants, by scanning crops in both RGB and near-infra red light, it is possible to generate multi spectral images using drone devices. This makes the farmers usually find this easy to monitor their crops and soil, with less humanly effort.
With AI powered gadgets in the irrigation fields, precision farming is put into practicality. With right amount of water being used while farming, a lot of waste is reduced. The usage of right amount of water, pesticides, seeds and land, there is a lot of saving on the revenue. With less budget, the farmers will gain a better profits in the proportions accordingly. The ratio will likely double to the input budget and the output profit. According to mindtree, Key technologies that enable precision farming are: • High precision positioning system • Automated steering system • Geo mapping • Sensor and remote sensing • Integrated electronic communication • Variable rate technology
This is one of the other key applications of AI in agriculture. With satellite technology, IoT and AI, sowing date, farm yard manure application, seed treatment, optimum sowing depth, land preparation, soil test-based fertilization, are monitored easily. The farmer can know his convenient and suitable time to sow and reap the crops on his fingertips. With the help of Artificial intelligence, the farmers get notifications and updates regarding the rainfall and soil health which will lead to a rise in the yield. Real time Moisture Adequacy Data (MAI) is tested and sent to the farmer’s screen every single day.
Fertilizers, pesticides and insecticides can be sprayed aerially with the help of drones. The drones carry the pesticides and all the necessary requirements that are required for the crop health. The drones spray them accordingly with the monitor crop health. The drones predict the need and requirement of pesticides in the crop. This gradually reduces the chance of spraying less or excess amount of pesticides in the farms. Technology takes its turn with the Artificial intelligence in agriculture along with the health of the crop. The drones are also capable of carrying liquid medicine. This will keep all the crop eating bugs away.
Choosing hybrid or other types of seeds is made possible with AI. Satellite intelligence monitors the overall crop land health. This predicts the water and soil situation and analyses the kind of seeds that can be sowed soon after the current crop. Climatic changes, soil erosion, weather and crop conditions, that can be calculated using Artificial intelligence will sum up to sowing hybrid seeds in the fields. The information reaches the farmer through his mobile phone application.
Identification of various diseases: both spreadable and un spreadable of crops can be monitored by remote sensors. Farmers are notified immediately when the crop is being affected by any internal or external means. Reprocessing of image ensure the leaf images are segmented into areas like background, non-diseased part and diseased part. The diseased part is then cropped and send to remote labs for further diagnosis. It also helps in pest identification, nutrient deficiency recognition and more. These bots can also identify the remedy and prevention of further capabilities in the crops.
However, Artificial intelligence in agriculture in India can also be challenging. India is a country with 40% of its population employed as farmers. AI requires a huge revenue as it will require a continuous monitoring of the drones. Storage is expansive, but access and retrieval may not lead to connections in memory as well as humans could.