Machine Learning in Agriculture
Machine Learning technology is helping different sectors to boost efficiency and productivity. Agriculture has a huge impact on the economic sector, machine learning can help make various new opportunities in the emerging field of automated agriculture with its high-performance computing technologies. In the field of agriculture, it is helping farmers to increase their efficiency and reduce environmental impacts.
Around the world, machine learning is being used to detect diseases in plants or the climate changes that are about to occur in the future so that people can respond smartly, also the business in this field is processing the data to reduce the harsh outcomes with help of ML.
There are too many advantages of incorporating machine learning with agriculture, provides a much more efficient and effective way to sell, harvest, and produce all the crops, also it helps to check whether the crops produced are fine or not with the help of image recognition and insight, this ensures that the consumption of defective ones is minimized also ensure more amount of healthy crop, with the help of drones large fields can be monitored, the seeding and all kind of factors that add up to a plants health can be looked after in this way, SkySqurrel Technologies has brought drone-based Ariel imaging solutions for monitoring crop health. In this technique, the drone captures data from fields and then data is transferred via a USB drive from the drone to a computer and analyzed by experts. This company uses algorithms to analyze the captured images and provide a detailed report containing the current health of the farm. It helps the farmer to identify pests and bacteria helping farmers to timely use of pest control and other methods to take the required action, the farmers can easily figure out the best way to maximize their return, using the advancement they can figure out the perfect crop choice not only that they can also find which hybrid seed is best for a crop mix, the model can be trained in such a way that it keeps all the factors in consideration like the soil type, humidity, infestations in the area, need and price of the crop, chances of diseases, also considering how things went earlier with the data this will help farmers take more informed and rapid decisions. One problem that can easily be terminated by using machine learning is irrigation, the agriculture sector alone consumes 80% of available freshwater, and this percentage going higher each year, using ML from the right time to the right amount everything can be looked after and then processed keeping the environmental factors in mind. The method was the subsurface drip irrigation process, which minimized the amount of water loss due to evaporation and runoff as it is directly buried beneath the crop. Later researchers came with different sensors that were used to detect the need for water supply to the fields as soil moisture sensor and raindrop sensor, which were instructed through the wireless broadband network and powered by solar panels.
Machine learning makes it possible to replicate manual visual inspection of crops. It offers a non-destructive method of quality inspection, helping with the analysis of grain characteristics and grading. Computer vision can be used to understand images and recognize external characters of grains, further providing information on the quality to sort and grade the crops.
An example of machine learning being used to find defects in vegetables is Dlib, a machine learning library that supports image recognition capabilities, being used for defect pattern recognition on tomatoes. A similar ML model can be used on tomatoes by building a dataset of patterns such as them being infested with insects, having abrasions, or squashing due to mishandling.
The dataset can be used to create a knowledge base that can further be used to train machine learning algorithms to detect tomatoes with various patterns. This can help in sorting them out into different quality grades.
In the coming years, we can see lots of tech companies invest in algorithms that are becoming useful in agriculture. Also, the number of labor challenges can be overcome by using ML-trained bots, they can help farmers save crops from weed also they can harvest at a higher volume with better accuracy, they can keep track of the soil nutrition also.
Deep learning is used for disease detection in leaf, due to its complexity it’s used to solve complex problems with great accuracy, and also it has fewer errors comparatively. Using DL we can have feature extraction from the raw data. DL consists of various features like pooling, activation, convolutions, fully connected layers, gates, memory cells, and many more. Due to its hierarchical structure, it performs classification and prediction very well, for leaf detection CNN can be used.
Algorithms are applied to the dataset with different crops, pH values, soil features, rainfall areas, and production. After removing the noise from the dataset data mining algorithms are applied then F-measure is used to determine the accuracy. WEKA tool is used on the dataset with attributes NPK values of soil, crop yield, district, are of production, rainfall, pH soil type for yield prediction using the ANN model.
