Challenges Faced In Agriculture And How Machine Learning Can Be Applied

Whilst the number of population increases – challenges for agriculture increases. But to resolve them with preoccupied land limitations and scarcity of water is a bigger challenge. Modern agriculture is growing powerful which is built on pillars of technology.

Modern agriculture exhibits capabilities of how technology is succeeding to help greater problems.

List of challenges jot down as follows

1. Right Seed –> Right Area

Currently, agriculture is facing a hideous problem. Where in spite of all right knowledge consumed, an agriculture sector is facing a huge loss. Why? To put simply the crops aren’t supervised properly. classification analysis can help you with finding the right area for your crop, further resulting to control damage and more revenue generation.

2. Irrigation

To be able to successfully yield crops, foremost and major key role is proper irrigation functionality. Machine learning algorithm can help with better irrigation resulting in following ways:

• Maintaining a desired soil water range in the root zone that is optimal for plant growth.
• Low labor input for irrigation process management.
• Increase the ratio of average vegetable yield.
• Sensors that collect soil moisture data for monitoring plant needs at real-time.

Here are some systems for irrigation in the field of machine learning:

• Closed loop system: When and how much water to apply
• Open loop system: The amount of water to be applied at each irrigation
• Time-based system: The pre-set of an amount to be added to the field.

3. Predictive Analysis

A decision is said to be the key player in the agriculture sector. The right decision will lead to better revenue outcomes and happiness. Predictive Analysis is a huge asset of machine learning which performs leading role such as :

• Precise decision of sowing
• Determine healthy crop yield
• Addition of fertilizer recommendation which will add great deal of value to a business

4. Diagnosing Soil Defects

Farming is about risk calculation – But what if the risk can be calculated and cured beforehand. Anomaly analysis can help you with identifying the weakness and strength of the soil, resulting in more revenue generation and saving ample amount of time.

5. Production Forecasting using weather condition:

“Climate is now a data problem,” says Claire Monteleoni. Earlier, Improper weather predictions lead to many crops lost- resulting in loss of money and time invested. But technology has evolved over years leading businesses to higher stable growth. Regression analysis will help you with better production forecasting using weather conditions.

6. Weed Detection:

“In the developing world, 40 – 50 percent of all crop yields are lost to pests, crop diseases, or post-harvest losses. Even in the United States, that number is 20 – 25 percent”. Image analysis can help you with detecting the present object on the farm. Furthermore, by performing the classification of image objects we can be able to find the weed on the farm to help with healthy crop growth.

7. Water Treatment

Right minerals are foremost important necessity for the growth of plants. Anomaly detection with the use of unsupervised analysis will help you select the right amount of minerals, resulting in faster-growing crops and will further help to produce more crops compared to competitors.

8. Recommender System:

We understand that it is always more difficult and expensive to acquire a new customer than it is to retain a current paying customer. Recommender system analysis will help you identify the customers who are more likely to purchase your product and the likelihood of the existing customers. Furthermore, resulting in retaining the customers and expand the range of innovation while serving.