An Automated Crop and Plant Disease Identification Scheme Using Cognitive Fuzzy C-Means Algorithm

The cultivation of crops, conservation of plants, restoration of landscape, and management of soil are the phases incorporated in agriculture and horticulture. During the cultivation and conservation stages, the plants and the crops are affected by various diseases such as Bacterial scourge, Bacterial Leaf Blight, Brown spot, Seeding blight, Leaf streak, Powdery Mildew, Fire Blight, Black Rot and Apple Scab. These diseases in plants will lead to losses such as manufacturing and financial loss in farming industry worldwide. To maintain the sustainability in horticulture, the detection of crop disease and maintaining the condition of the plants are important. The Computer Aided Detection (CAD) in the agriculture and horticulture is the emerging trend, based on the digital imaging that provides the detailed analysis about the disease by applying the image mining process. In this work, the Cross Central Filter (CCF) technique is proposed to perform the noise removal process in the image and the identification of objects in the image is applied by using the Cognitive Fuzzy C-Means (CFCM) algorithm to differentiate the suspicious region from the normal region. The evaluation is conducted against the diseases affected in the rice crop and apple trees. The performance evaluation proves that the proposed design achieves the best performance results compared to the other filters and the segmentation techniques.

Automated summary

The phases of agriculture and horticulture include cultivation of crops, conservation of plants, restoration of landscape, and management of soil. Diseases such as Bacterial scourge, Bacterial Leaf Blight, Brown spot, Seeding blight, Leaf streak, Powdery Mildew, Fire Blight, Black Rot and Apple Scab can cause manufacturing and financial loss in the farming industry worldwide. Computer Aided Detection (CAD) using digital imaging is an emerging trend for disease detection and analysis in horticulture.