Indian Agriculture: Reinventing Agriculture disease identification and crop health management: Agentic AI of Agriculture

Indian Agriculture: Reinventing Agriculture disease identification and crop health management: Agentic AI of Agriculture.

 

Introduction

Indian economy is dependent on agriculture as it boosts the millions of farmers and it also takes a substantial share on the GDP of the country. Crop diseases however have been among the worst fears with the productivity, food security and the incomes of the farmers too. The traditional methods of detecting diseases use manual inspection which is extremely slow, subjective and inaccurate. Growing out of artificial intelligence, more precisely, a new paradigm, called Agentic AI, is now being proposed, which would have intelligent, autonomous and proactive agricultural systems able to detect, diagnose and even cure crop diseases at real-time.

The intelligent agents and the possibility to observe the conditions of the environment, analyze information and make a choice and do some actions independently are the substitutes of the reactive structure to the agentic AI. The Indian agriculture can be revolutionized using this technology that will assist in the diagnosis of diseases to enable farmers to have the accurate and timely information to act on it.

Experience on the Agentic AI on Agriculture.

In the agricultural field, agentic AI is a term in reference to describe the intelligent systems that act as autonomous agents to monitor the health of crops, detects disease in crops and prescribes or implements interventions without oversight of human beings. The systems are connected in such a way that they can form a complete farming system including computer vision, machine learning, IoT sensors, drones and satellite imagery.

In contrast to the classical AI systems that could solve only one task, e.g. image classification, an Agentic AI system is a system of repeated action, that will vary depending on the state conditions of the rest of the elements of the environment and vary in response to a previous data. They can be used to learn patterns of an extremely wide range of variables such as temperature, moisture content of the soil and health prognostics of crops to give them an informed decision making.

In the Andhra Pradesh case, an Agentic AI system, periodically monitoring the crops in a paddy field, is fixed on the crops, drones and IoT sensors. Through early indication of the presence of fungus, the initial signs can be noticed and alert the farmer, prescribe and even automatic pesticide sprinklers.

There is a need in detecting the Indian progressive disease.

The farming distress does not elude India due to the existence of many climatic areas, poor land areas and inaccessibility of the contemporary farming tools. The bacterial blight Disease in rice, the late blight in potato and the powdery mildew in wheat are some examples of the diseases which have been caused in crops that have resulted in massive losses in terms of annual yield.

The conventional techniques of identifying disease normally rely on the presence of the disease manifestation that could only be detected when the disease has already diffused. In addition, the farmers might not be in a position to get professional advice; hence, misuse and wrong diagnosis of the pesticides.

 

The answer to these issues is the agentic AI which will enable tracking of the issues at an early phase diagnosing the issue and taking certain action. This will not only help in reduction of wastage on crops, but also it would help in ensuring that minimum level of chemicals is used thereby ensuring sustainable farming.

Strong Agricultural uses of the Agentic AI.

a)Data Collection Layer

The basis of Agentic AI is information. The information collected in agriculture is founded on the numerous sources such as:

Humidity and temperature sensor Soil moisture, temperature and humidity sensor Soil IoT.

•          Drones having high resolution images of crops.

• Mass surveillance is an aspect that is offered by selective satellite surveillance.

Weather information of the weather services.

Such multi source information has a holistic perspective of farm environment.

b)Perception and Analysis

Deep learning system and computer vision are transformed to detect the abnormality of crops, such as discoloration, spots or wilting patterns in the corresponding image to the Agentic AI systems. This kind of features is an indication of diseases.

With minor deviations in patterns, greater models can be applied to give a line between diseases having similar symptoms.

c)Decision-Making Engine

Machines learning algorithms and rule based systems are used in the unit making decision on the most appropriate course of action. It takes into account such factors as the severity of the disease, type of crop and environment.

d)Action and Execution

The agentic AI system can be able to propose what the farmers should do or to do it through the help of the automated systems (smart sprayers or irrigation systems).

E) Learning and Adaptation.

The systems are also becoming better since they are constantly updated according to the new information in their systems making them more accurate and efficient. Greater is in the agricultural sector whereby the conditions vary in different locations.

Applications Disease Identification.

a)Early Disease Detection

In agentic AIs equipment, sicknesses are not only identified at an early stage of their progression but also in the majority of occasions even prioritizing their formation. This would be by observing the minor variations in the leaf color,leaf texture and reflectance patterns.

An example is in tomato crops whereby the leaf curl virus can be spotted early hence mass destruction is not achieved.

b)Disease Classification

Such disease classifier agents utilizing a deep learning model are able to pick out diseases with great accuracy. Suppose, it is able to differentiate a bacterial leaf spot and fungus of crop such as chili.

c)Predictive Analysis

Knowing that the outbreak of the disease may happen, Agentic AI will be able to predict the event by relying on the historical and past climatic events. This will enable farmers to make precautionary measures.

d)Recommendation of Treatment (Computerized).

The system can also make suggestions on the type of pesticides/organic type and quantity required based on the treatment.

e)A case of an Indian Agriculture real time.

Consider the example of a cotton farm in Maharashtra, farmers have fallen prey to such pests as a bollworm in their midst. When the AI system is used as a single drone with sensors that continuously patrol within the field it is referred to as Agentic.

The system identifies the early symptoms of the pest when it identifies the abnormalities of the form and color of the leaf. It will then provide a fast text to the farmer by use of a mobile application and order a certain spray pesticide. Using automated equipment, the system is capable of going a step further and beginning spraying on diseased parts.

The plan is shortsighted and will minimise the usage of pesticides and cost as well as the large production of crops will be eradicated.

The benefits of the Agentic AI on Agri.

i)Increased Crop Yield

Detecting at earlier stages and the use of proper medication will reduce significantly the losses of crops and thus high production.

ii)Cost Efficiency

Farmers would be in a position to save the operating cost through the reduction of pesticides use as well as maximizing resources.

iii)Sustainability

Sustainable agriculture can be realized with the help of agents AI that involve decreasing the use of chemicals and saving water.

iv)Accessibility

Small and marginal farmers, with the help of mobile-based AI systems, can get access to advanced technology.

v)Scalability

The systems can be applied in large farms and therefore this renders its applicability to the heterogeneous farming system in India.

The following are some of the implementation challenges:

However, there are several threats to the implementation of Agentic AI in the field of Indian agriculture:

Crippling Infrastructure: Making access to electricity and the internet connection in the rural areas easy.

•          Very costly in the first place: Drones and sensors and artificial intelligence equipment are quite costly to deploy.

Data Availability: good models (require good labels) are made of good data on labels.

•          Farmer Awareness: Not all farmers are aware of the modern technologies.

Policy and Regulation There needs to be policy and regulation on the application of AI and drones in agriculture.

The India government and industry do it in the following way.

The Indian government has made some moves that have been steered towards promoting digital agriculture and these are:

Digital Agriculture Mission: The aim of the project is to modernize the already available agricultural sector introducing technology to strengthen production and development of farm produce and services to the Singaporeans.

•          PM-KISAN and AgriStack

•          Use of the AI in monitoring crops and predicting yields.

An upsurge in the presence of the Indian farming in the role of the Indian farming conditions is also set to go through the increasing presence of the private companies and start-ups that build AI-related solutions, depending on the conditions of the Indian farming.

Animal Agentic AIs Perspective.

With the Indian agricultural Agentic AI, the application is a promising one. These systems will be viable and will improve when the edge computing has been optimized, the 5G connection has been established, and hardware is available.

Both transparency and supply chains level could be enhanced with the help of introducing blockchain technology; collaboration with the agricultural professionals will help to make the white-collar choices more accurate.

There are multi agent systems opportunities, where different AI agents can manage the farms and areas where work is performed to control diseases and optimization of resources.

Conclusion

The Indian agricultural industry will be digitalized, and the diagnosis of diseases and crop control will radically transform the agentic AI. It will assist farmers to make wise decisions, minimize losses and maximize productivity with the deployment of autonomous, intelligent and adaptive systems.

Despite these problems, it means that it will continue to utilize the Agentic AI faster through continuous investment in technologies, infrastructure and education. With India heading towards the digital and sustainable food future, the role will be increasingly important to the work of Agentic AI in forming the food security and economic growth.

 

Prepared by

Dr Balajee Maram,

SR University