The Application Of Machine Learning In Plants And Crops Disease Detection
Agriculture has a wealth of data, but many farmers are unaware of how to use it using machine learning models for improved crop selection and production prediction.
FREMONT, CA: Agriculture has benefited from machine learning (ML) in various ways, ranging from irrigation scheduling to insect management. Below discusses the agriculture application cases for ML and deep learning that can assist farmers in overcoming obstacles. Numerous ML applications are possible based on these agricultural use cases. It will be beneficial for data scientists will develop a broad understanding of use cases and associated ML algorithms.
Precision agriculture is an agricultural area where ML techniques enable farmers to make crop care and harvesting decisions at the individual plant level rather than at the field level. Fast, robust, and accurate ML modeling is the primary enabler of this skill. Farmers may employ precision agriculture technologies to boost farm yields, reduce expenses, and safeguard the environment.
Detection of illness in plants and crops: By 2050, human agricultural crop yields will need to grow by an estimated 70 percent to meet the predicted population number. Crop diseases currently diminish the productivity of the six most essential food crops by 42 percent, and some farms are wiped out altogether on an annual basis, according to specific data. Thus, it becomes critical to develop precise crop disease diagnoses through technology. This is an area where ML approaches can be advantageous. Deep learning algorithms can be accurately taught on photos of crops and plants for crop disease identification. The challenge is to obtain photographs of the plants or crops and then label them. Unmanned Aerial Vehicles (UAVs) combined with large-scale backend systems utilizing ML models for disease detection is one of the most widely employed strategies now. To overcome the data collecting difficulty, modeling techniques such as the generative adversarial network (GAN) can generate synthetic data from crop disease photos. Another obstacle to developing high-accuracy training models is a class imbalance in the collected data. This is where DC-GAN (Deep convolutional GAN) comes in handy for resolving the issue of class imbalance through the generation of synthetic images. Then, using a deep convolutional neural network (CNN) model, crops or plant illnesses can be classified and detected. The CNN model may be trained to detect diseases by identifying problems that manifested physically on the leaf or stem of the crop. As a refresher, generative adversarial networks are pairs of neural networks that perform two functions: generator and discriminator. The generator acquires the ability to create synthetic images of a particular class, while the discriminator distinguishes between actual and synthetic images. The models learn from one another to better their performance.
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