ARTIFICIAL INTELLIGENCE (AI) IN AGRICULTURE
In the 19th century in the times of industrial revolution machines were deployed as a substitution or reduction for human labour. This in course of time, with the advancements and in information technology in the 20th century, post the arrival of the computers, initiated the vision for artificial intelligence (AI) powered machines. In the preset day it’s a reality that AI is tardily taking over the human labour.
In agriculture there is a quick adaptation to AI in its various farming techniques. The concept of cognitive computing is the one which imitates human thought process as a model in computer. This results as turbulent technology in AI powered agriculture, rendering its service in interpreting, acquiring and reacting to different situations (based on the learning acquired) to enhance efficiency. To harvest benefits in the field by catching up with the recent advancements in farming sector, the farmers can be offered solutions via platforms like chatterbot. At present in India, Microsoft Corporation is working in the state of Andhra Pradesh with 175 farmers rendering services and solutions for land preparation, sowing, addition of fertilizers and other nutrient supplements for crop. On an average, a 30% increase in crop yield per ha has already been witnessed in comparison to the previous harvests. The various areas where the solutions for benefitting agriculture involving cognition possess knowledge are furnished below.
The Internet of things (IoT) driven development
There are massive volumes of data getting generated each day in structured and unstructured format. These data are regarding weather pattern, soil reports, new research, rainfall, vulnerability to pest attack, imaging through drones and cameras. IoT solutions relating to cognition would sense, recognize and yield smart solutions to enhance crop yields.
There are two primary technologies deployed for intelligent data fusion, namely proximity and remote sensing. The important application of these high resolution data is for testing the soil. Unlike remote sensing, proximity sensing doesn’t need sensors to be built into aerial or satellite systems; it only requires sensors that are in contact with the soil at a close range. This facilitates in the characterization of the soil based on the soil beneath the surface at a particular region.
The hardware solutions like Rowbot (concerning to crops like corn) has already begun pairing software that collect data with robotics to develop the best fertilizer for the cultivation of corns in to maximizing the most possible crop yield.
Image-based insight generation
In the current world scenario one of the most dissertated areas in farming today is Precision farming. Imaging through drones can assist in rigorous field analysis, in monitoring crops and scanning of fields. With a combination of Computer vision technology, drone data and IoT will ascertain that the farmers take rapid actions.
Data fed from drone image could bring forth alerts in real time which would accelerate precision farming. Commercial drones makers like Aerialtronics have enforced IBM Watson IoT Platform and the Visual Recognition APIs for real time image analysis. Some areas computer vision technology can be put to use are as follows,
The image sensing and analysis ensure that the plant leaf images are sectioned into surface areas like background, diseased area and non-diseased area of the leaf. The infected or diseased area is then cropped and sent to the laboratory for further diagnosis. This further renders assistance in the identification of pest and sensing nutrient deficiency.
Identify the readiness of the crop
Images of various crops captured under white light and UVA light are to check how ripe the green fruits are. From this analysis the farmers could create different levels on the readiness of the fruit or crop category. Then add them into assorted stacks before sending them to the market.
Employing images of high definition from drone and copters systems, real time estimations can be attained during the time span of cultivation by building a field map and discovering areas where the crops require water, fertilizer and pesticides. The optimization of resource is assisted to a huge extent by this.
Identification of optimal mix for agronomic products
Cognitive solutions recommend the farmers on the best choice of crops and hybrid seeds which is grounded on multiple parameters like soil condition, weather forecast, type of seeds and pest infestation in a specific area. A personalized recommendation based on the farm’s requirement, native conditions, and data pertaining to successful farming in the past. The other external factors like trends in marketplace, crop prices, consumer needs, requirements and aesthetics may also be factored to enable farmers take a clued-up decision.
Crop health monitoring
Remote sensing (RS) techniques along with hyper spectral imaging and 3D laser scanning are crucial to construct crop metrics over thousands of acres of cultivable land. It has the potential to introduce a revolutionary shift in how farmlands are monitored by farmers from the perspectives of both time and effort. This technology will also be employed in monitoring crops throughout their lifecycle including genesis of report in case of abnormalities.
Automation techniques in irrigation and enabling farmers
Irrigation is one of the most labour intensive processes in farming. AI trained machines aware of historical weather pattern, soil quality and kind of crops to be grown, can automate irrigation and increase overall yield. Nearly 70% of the world’s freshwater resource is utilized for irrigation; such automation can conserve water and benefit farmers in managing their water probs.
Significant of drone
According to a recent study, the total available market for drone based solutions throughout the world is $127.3 billion. And for agriculture is at $32.4 billion. Such Drone based solutions in agriculture sector have a lot of implication like dealing with adverse climatic conditions, productivity gains, precision farming and crop yield management.
In conclusion the future of farming in the times to come is largely reliant on adapting cognitive solutions. Though a vast research is still on and many applications are already available, the farming industry is still not having sufficient service, remains to be underserved. While it comes down in dealing with realistic challenges and demands faced by the farmers, using AI decision making systems and predictive solutions in solving them, farming with AI is only in a nascent stage.
To exploit the tremendous scope of AI in agriculture, applications should be more robust. Then alone it will be in a position to handle frequent shifts and changes in external conditions. This would facilitate real time decision making and sequentially utilize appropriate model/program for gathering contextual data efficiently. The other crucial aspect is the extortionate cost of the various cognitive solutions for farming readily available in the market. The AI solutions have to become more viable to assure that this technology reaches the farming community. If the AI cognitive solutions are offered in an open source platform that would make the solutions more affordable, which eventually will result in faster adoption and greater insight among the farmers.