Machine Learning and Artificial Intelligence In Agriculture
The increasing demand for agricultural products has involved Machine Learning and Artificial Intelligence in the agricultural sector. There is a high demand for automation, guidance, and analysis.
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Agriculture is one of the major sources of income all over the world. Almost 85.6% of a country’s GDP is dependent on agricultural produce and it provides maximum benefit for the overall growth and development of a nation. Along with all this, agriculture plays a significant and predominant role in the economical development of a country. The population is increasing rapidly and to fill the increasing number of stomachs, there is a high demand for an increase in agricultural production.
There are various traditional methods for agriculture, but these require heavy machinery, loads of manpower, hefty equipment, and lots of hard work. This all is time-consuming and without proper soil analysis, temperature analysis, humidity check, ph level check, seed quality check, and other factors, the products that the farmers get aren’t of good quality. It takes months for crops to harvest and if the output, at the time of harvest, is not of good quality then it is of no use.
With the increasing population and growing requirements, it is not possible to continue with old traditional practices. We require the power and support of emerging technologies to make our work easy, fast, and precise. We require proper analytical and predictive tools that could help the farmers in the right decision making and support them by guiding them with the proper set of inputs, tools, and current market trends so that they could plan their execution accordingly.
These factors play an essential role in the development and increase of agriculture. In India, agriculture takes more than 80% of the job sector but the output isn’t good enough. The farmers want to change their occupation as they find it difficult to work in this field due to decreasing output hence decreasing income. Many farmers even commit suicide as they are unable to sell their produce due to its low quality or the produce isn’t enough to get them sufficient income.
This is where Artificial Intelligence and Machine Learning aims and promises to give better results. Machine Learning is the science, where machines are trained to analyze the input and provide the best decision possible. It has reduced a high amount of manual work and hence burden on farmers can be reduced. It is way much better than traditional farming, as the machines take the inputs, and consider all the relevant factors like soil nutrient levels, humidity, acidity, weather condition, etc. for analysis. After processing this data, we get the output.
By this output, the farmers get to know about the particular nutrient that is insufficient for a particular crop and needs to be provided, or about the particular nutrient that needs not to be given more. In this way, they can check the ph level of soil and add fertilizers accordingly, and as per the moisture level of crops, water could be provided. This would lead to proper utilization of resources as well and the probability of an increase in yield is high.
For this, various applications are available in the market. These applications are associated with a proper set of hardware, consisting of an assembly of sensors and detectors, which measure the moisture level, ph, or nutrient level. Then these sensors provide the calculated output to the application. The application uses these calculations as input, and process them to provide predictive and prescriptive analysis. In this way, farming can be done effectively and efficiently.
There are various startup plans also for improvement in agriculture and every year, the Indian government invests more than 2 crore rupees for the improvement and development of the agricultural sector. Hence there is an epoching demand for the automation of this sector.
Artificial intelligence tries to fill the gap between automation and Machine Learning agricultural models. It teaches the machines to act the humans, and think like human brains. Once the machines have calculated all the relevant and required data, there comes the time of decision making. Proper steps have to be taken based on the predictive and prescriptive analysis. For this humans require hefty calculations and in-depth brain-storming.
For the same purpose, artificial intelligence is used. Machines are capable of taking decisions on their own. When there is a shortage of water levels in the crops, the machine learning model detects and artificial intelligence makes it possible for the machines, to supply water through the established source, to the fields. In case of a decrease in nutrients level, the model would be able to spray fertilizers to the crops, or in case of detection of pests, the machine would spray the required amount of weedicides.
The spray of pesticides or weedicides is carried by drones, mostly. A camera is also embedded in them, to take note of every minute details of the crops. Drones have proven to be quite useful in agriculture. Similarly, many other technologies are contributing to agricultural development and automation. We will discuss those things, one by one.
With the flourishing field of Machine Learning and Artificial Intelligence, the sector of agriculture can be improved. There is an emergent need for improvement and development in this sector and this can be achieved with the help of ‘Smart farming’ techniques which is widely referred to as ‘Digital Farming’. Though digital farming is not a new concept, it is evolving at a higher pace. Due to rapid and tremendous changes in the technical domain, new technologies are emerging which make things easy, efficient, and are productive and with the rise of these technologies, the scope of farming also increases.
As we know, agriculture is the most important sector, both for progress and life, hence it requires maintenance, monitoring, and guidance. With Machine Learning, crop production has increased and the product is of high quality. These techniques make hard work worthy and there is a high chance of growth and development.
There is a wide-ranging requirement for pest tracking and control, disease analysis, soil quality measure, plant species analysis, risk management, and much more. By working in all these areas the demand is for sustainable development with the use of technology and artificial intelligence, and machine learning is there for the same reason. These two fields are trying hard to cope with all the loopholes in this sector so that it would become one of the largest self-sufficient work hubs.
To predict harvest, disease, weather, or any other dependency, our machine learning model and artificial intelligence model must be well trained so that they can consider all the possible situations and give an accurate result. In this way, one of our major problems would be solved. This field can be highly automated as there is a vast requirement of labor, analysts, and scientists. If somehow, we can fulfill all these requirements through the technology that is emerging, then we can provide the farmers, the best mechanism to grow and monitor their produce.
To achieve this, there are various key features that the farmer and the technicians should keep in mind and we will discuss all those points one by one in a proper sequence so that we can understand most of them.
Satellite Crop Monitoring
The satellite crop monitoring technique is widely used to get the proper analysis at the proper time. The satellite data provides knowledge about weather, pests attack, soil types, vegetation maps, etc. In this way, the farmers and the government get quite valuable information about various crops and the associated risk factors and other measures.
In this way, all the information about crops are gathered at a particular platform and can be used by the farmers and research agencies for further analysis. Satellite images and input signals are measured and analyzed. This provides proper output for decision-making. In this way, the framers can decide the proper action that they need to take. It prevents them from pests attack as they can protect their fields at the initial stage.
I am using an application that collects the data from satellites and an application uses this data as input. If there is a high probability of a pest attack then this application commands the hardware, to which it is associated, to protect the fields. In this way, pest attacks can be controlled, and hence, it would be reduced.
There are various such ideas, which uses the satellite information to encounter various loopholes in this sector like soil type analysis to provide the soil with a better type of nutrients, etc. The major advantage of using satellite information is that it provides real-time tracking, i.e., real-time weather risk alerts, real-time pest migration information, and much more.
This information keeps the farmers prepared and the loss o the fields can be prevented. It follows the basic steps of imagery, insight, and monitoring which help in the proper regulation of crops and aids in decision-making to the farmers.
Monitoring and Analysis of Crop Health via Drones
To track the growth of crops, spray pesticides or weedicides, or for crop monitoring, drones are the most efficient and effective tools. Crop health can be monitored via drones. This can be done as the drones use high resolution RGB cameras and professional multispectral sensors which helps them to detect the quality of the crops. They cover a large distance and provide us the real-time imagery of the crops. In this way, they identify the areas where there is a need for attention from the farmers.
They complement the other tools used for agriculture as the maps and plans the framers can make after analyzing the inputs from the drones, can be used to take proper action like irrigation, pest attack prevention, and much more.
This helps in tracking the growth of the crop from sowing to the harvesting season. The drones monitor the fields properly and help in taking the proper measures if the crop is not healthy, which means that if there is a requirement for fertilizers, irrigation, pest control, etc. These measures define the health of the crops which affects their growth and quality.
The drones are used for health analysis of crops. The UAV technology, creates health maps, identifying and highlighting the area of concern. In this way, we can monitor the overall health of the crops.
There is a vital role in weather forecasting in agriculture. It tells about the weather condition that will occur for some time and in this way the farmers can plan various other things that they need to do for their crop maintenance and growth.
The weather information plays an important and predominant role in the field of precision in agriculture. It guides the farmers on the usage of various inputs during varying weather conditions so that their crops could be healthy. It gives information about crop growth, irrigation rate, fertilizer timing and delivery, pests and disease control, field workability, and many other things.
By considering all these factors, the farmers can decide the suitable working hours as it becomes difficult to work in fields during adverse weather conditions like storms, heavy rain, snow, etc. This also helps the farmers to decide the proper irrigation mechanism and pattern that they should use as, during the rainy season, the irrigation requirement is less. It also helps the framers in tracking the right amount of pesticide that they should use.
This information also helps in pre-agriculture decisions, i.e., when is the right time for sowing the seeds so that maximum benefit can be made from the yield, and also, it tells the proper time of harvesting. It also aids in post-harvesting practices, i.e., storage. The type of storage depends heavily on agriculture. According to the particular weather condition, the farmers can store their produce at a safe place and a particular temperature so that it can sustain for a longer period.
In this way, the weather forecasting is highly beneficial for the framers to maintain their crop growth.
Crop and Soil Health Monitoring and Estimation
Precision agriculture is measured by various technologies to ensure proper crop growth and crop monitoring. For this purpose, weather forecasting, UAV technology, satellite monitoring, etc. are in play. It includes soil monitoring as well. The soil quality is the basic factor for crop growth. For a particular crop, the soil type should be suitable and the soil quality should be high. Various dielectric soil moisture sensors are used to measure soil compaction.
The EC and ph sensors provide valuable information about the soil which helps in proper nutrient regulation by controlling the number of fertilizers and pesticides. The CO2 and water level sensors also play a major role in crop health estimation which reduces the chances of risks. The soil fertility meter and all other equipment have contributed a lot towards crop and soil health monitoring and estimation.
To protect the crops from weeds or reduce the labor work-force, AI bots are highly beneficial. The labor work time can be reduced and the same task of harvesting can be done more efficiently and in a shorter period by the AI bots. The helps in leveraging, spraying pesticides, removal of weeds, irrigation, etc. They also help the farmers to protect their crops from weeds.
Artificial intelligence solves the various complex challenges in agriculture through automation. This helps the farmers to complete their work efficiently, effectively, and in a shorter period. This provides them with proper analysis and the best possible solution for any particular condition. For the development and progress of the agricultural sector, automation is the key requirement.
Species identification is one of the crucial tasks in agriculture. It helps the farmers in identifying the valuable and unnecessary crops that grow in the field. Most of the time it happens that along with a particular crop species, various other unknown species grow. They may even harm or hinder the growth of the actual crop or they may boost its growth. For this, there is a high demand for the identification of the crop species which is taking space and other inputs in the field.
If that species is a weed or any other dangerous plant type then it needs to be removed as it is wasting the space, sunlight, water, and other inputs. It may also affect the crop adversely. SO, to prevent this, species identification is a must. Various applications check this. They have to be provided the image of the crop as input and then they will identify the species of the crop. This is how one type of application works. Several other applications take different types of input for the same.
All these technologies which we have studied above, provide the predictive analysis. The satellite information along with weather forecasting provides us information about how the pattern will be followed. The weather prediction helps the farmers to be prepared for the changes that will happen and also guides them on how to use and manage the other resources.
The satellite information, on the other hand, provides information about weather, pest attack, or any plant disease that may spread across a particular region. By having such prior information, the farmers can plan things accordingly and they can manage the required resources on time and protect their crops.
Machine Learning and Artificial Intelligence also provide them the bots or machines that could aid in better decision making and performing a task in a fixed amount of time with high accuracy and efficiency.
Robotics in Agriculture
There are various operations for which robotics is used in agriculture. These techniques include weed control, harvesting, environmental monitoring, cloud seeding, planting seeds, and soil analysis. These robots help to perform the repetitive tasks which require high labor work and precision. In this way, the task can be completed in a shorter duration with high precision, and efficiency.
The future scope of Machine Learning and Artificial Intelligence in agriculture is high as the demand for automation and analysis increases day by day. This will ensure that the farmers get a properly analyzed dataset.
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