Machine Learning Applications in Agriculture
Published: 2022-12-07 16:34:45

In recent years, the application of Machine Learning has grown significantly. researchers from different disciplines.
Scientists exploit neural networks' capabilities for machine learning and machine vision in practical applications.
However, due to their highly developed functionality and variety, working with neural networks can be exceedingly challenging for a researcher who has not had recent hands-on experience. 


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Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the agri-technologies. In this blog, we will be presenting some **dedicated applications of machine learning in agricultural production systems**. By applying machine learning to sensor data, farm management systems are developing into real time artificial intelligence enabled programs.

## Applications of ML in Agriculture.

### 1. Yield Prediction
One of the most important areas of precision agriculture is yield prediction, which is crucial for mapping yields, estimating yields, matching crop supply and demand, and managing crops for maximum productivity.
Examples of ML applications in the works include: an efficient low-cost and non-destructive system that automatically counts coffee fruits on a branch. The method coffee fruits are classified into three types: harvestable, unharvestable, and fruits with disregarded maturation stage. In addition, the approach determined the weight and the maturation  of the coffee fruits. The purpose of this endeavor was to provide information to coffee growers to optimum economic benefits and organize their agricultural operations. Another example is a machine vision system for automating shaking and catching cherries during harvest. The system segments and detects obstructed cherry branches with full foliage, even when they are inconspicuous. The major purpose of the system was to reduce labor requirements in manual harvesting and handling processes.

### 2. Disease Detection

Disease detection and yield prediction are the sub-categories with the highest number of articles. presented in this review. One of the most serious challenges in agriculture is pests and disease. control in open-air (arable farming) and greenhouse conditions. The most generally utilised practise in pest and disease control is to uniformly spray insecticides over the farming area. 
This practice,although effective, has a high financial and severe environmental impact. Environmental implications can be residues in crop products, negative effects on ground water contamination, and repercussions on local wildlife, eco-systems, and so on. ML is an integrated aspect of precision agricultural management. where agrochemical input is tailored in terms of time and place. In the Machine learning model, a tool is described for the detection and distinction of healthy Silybum marianum plants and those infected by the smut fungus Microbotyum silybum during vegetative growth. In the work of the models based on an image processing procedure for the classification of parasites and the automatic identification of thrips in strawberry greenhouse conditions, for real-time control.

### 3. Soil Mangement

The third area of this review concerns the application of ML to the prediction and identification of agricultural soil parameters, such as the assessment of soil drying, condition, temperature, and moisture content.
A heterogeneous natural resource, soil has complex systems and processes that are challenging to understand. Researchers can use soil characteristics to comprehend how ecosystem dynamics affect agriculture. An precise assessment of the state of the soil can result in better soil management.
Soil temperature alone plays a significant role in the accurate analysis of the climate change effects of a region and its eco-environmental conditions. It is a crucial meteorological parameter influencing the interacting processes between the earth and the atmosphere. In addition, soil moisture has an essential impact on crop yield fluctuations. 
Although soil measurements are typically time- and money-consuming, computational analysis based on ML techniques can be used to achieve a low-cost and trustworthy solution for the precise estimation of soil.
The first study for sub-category is the work of More specifically, this study presented a method for the evaluation of soil drying for agricultural planning. The method accurately evaluates the soil's drying with evapotranspiration and precipitation data in a region located in Urbana, Illinois, of the United States.
The provision of remote agriculture management decisions was the aim of this technique. The second research was created to forecast the state of the soil. In particular, the study presented a comparison of four regression models for the prediction of soil organic carbon (OC), moisture content (MC), and total nitrogen (TN).

## Conclusion
So in this blog post I explained about 3 applications of Machine Learning in Agriculture.

By adding machine learning to farm management systems are turning into real artificial intelligence systems, delivering richer recommendations and insights for the subsequent decisions and actions with the ultimate scope of production enhancement in mind. For this reason, in the future, it is predicted that the usage of ML models will be even more prevalent, allowing for the possibility of integrated and applicable tools.