IoT based Automated Plant Disease Classification using Support Vector Machine

Leaf - a significant part of the plant, produces foodusing the process called photosynthesis. Leaf disease can causedamage to the entire plant and eventually lowers crop production.Machine learning algorithm for classifying five types of diseases,such as Alternaria leaf diseases, Bacterial Blight, Gray Mildew,Leaf Curl and Myrothecium leaf diseases, is proposed in theproposed study. The classification of diseases needs front faceof leafs. This paper proposes an automated image acquisitionprocess using a USB camera interfaced with Raspberry PI SoC.The image is transmitted to host PC for classification of diseasesusing online web server. Pre-processing of the acquired image byhost PC to obtain full leaf, and later classification model basedon SVM is used to detect type diseases. Results were checkedwith a 97% accuracy for the collection of acquired images

Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image

The segmentation of a single leaf from an image with overlapping leaves is an important step towards the realization of effective precision agricultural systems. A popular approach used for this segmentation task is the hybridization of the Chan-Vese model and the Sobel operator CV-SO. This hybridized approach is popular because of its simplicity and effectiveness in segmenting a single leaf of interest from a complex background of overlapping leaves. However, the manual threshold and parameter tuning procedure of the CV-SO algorithm often degrades its detection performance. In this paper, we address this problem by introducing a dynamic iterative model to determine the optimal parameters for the CV-SO algorithm, which we dubbed the Dynamic CV-SO (DCV-SO) algorithm. This is a new hybrid automatic segmentation technique that attempts to improve the detection performance of the original hybrid CV-SO algorithm by reducing its mean error rate. The results obtained via simulation indicate that the proposed method yielded a 1.23% reduction in the mean error rate against the original CV-SO method

Segmentation of Cotton Leaves Based on Improved Watershed Algorithm

International audienceCrop leaf segmentation was one important research content in agricultural machine vision applications. In order to study and solve the segmentation problem of occlusive leaves, an improved watershed algorithm was proposed in this paper. Firstly, the color threshold component (G−R)/(G+R) was used to extract the green component of the cotton leaf image and remove the shadow and invalid background. Then the lifting wavelet algorithm and Canny operator were applied to extract the edge of the pre-processed image to extract cotton leaf region and enhance the leaf edge. Finally, the image of the leaf was labeled with morphological methods to improve the traditional watershed algorithm. By comparing the cotton leaf area segmented using the proposed algorithm with the manually extracted cotton leaf area, successful rates for all the images were higher than 97 %. The results not only demonstrated the effectiveness of the algorithm, but also laid the foundation for the construction of cotton growth monitoring system