Crop leaf disease identification based on ensemble classification

Livestock and horticulture are well-known contributors to the global economy, particularly in countries where farming is the sole motivation for income. Yet, it is regretful that infection degeneration has affected this. Vegetables are a significant source of power for people and animals. Leaves and stems are the most common way for plants to interact with the surroundings. As a consequence, researchers and educators are responsible for investigating the problem and developing ways for recognizing disease-infected leaves.

Growers everywhere across the world will be able to take immediate action to avoid their produce from getting heavily affected, so sparing the globe and themselves from a potential global recession. Because manually diagnosing ailments might not have been the ideal solution, a mechanical methodology for recognizing leaf ailments could benefit the agricultural sector while also enhancing crop output. The goal of this research is to evaluate classification outcomes by combining composite classification with hybrid Law's mask, LBP, and GLCM.

The proposed method illustrates that a group of classifiers can surpass individual classifiers. The attributes employed are also vital in attaining the best findings because ensemble classification has demonstrated to be much more reliable.

Read the complete research at www.researchgate.net.

Kaur, Navneet & V, Devendran & Verma, Sahil. (2021). Crop leaf disease identification based on ensemble classification. 


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