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Detection of plant diseases plays a crucial role in taking disease control measures to increase the quality and quantity of crops produced. Plant disease automation is beneficial because it eliminates surveillance work at significant farms. As plants are a food source, diagnosing leaf conditions early and accurately is essential. This work involves a detailed learning approach that automates leaf disease detection in mango plant species. This paper presents a detection system using Brightness Preserving Bi-Histogram Equalization (BBHE) and Convolutional Neural Network (CNN). The photographs of mango leaves were first flattened, then resized and translated to their threshold value, followed by feature extraction. CNN and BBHE have extensively been used for pattern recognition. The test images of affected leaves were subsequently uploaded to the system and then matched to the ailments being trained. Training data and test data were cross-validated to balance over-adjustment and under-adjustment problems. The proposed method correctly detects the mango leaves disease at the early stage with 99.21% maximum accuracy.

Image protection is essential part of the scientific community today. The invisible watermark is widely being used in past to secure the medical imaging data from copyright protection. In this paper novel hybrid combination of the invisible image watermarking and the Blockchain based encryption is proposed to design. The watermarking is implemented using edge detection (ED) of discrete wavelet transform (DWT) coefficient. The medical image is decomposed using L level DWT transform to generate multi-resolution coefficients. The edge detection is applied to HH wavelet band to generate the edge coefficients. To improve robustness difference of dilation and edge coefficient are used for watermark embedding. The watermark image is encrypted using Blockchain based hash algorithm for medical images. Then at the decoding end first decryption is achieved and then image is reconstructed. The results are sequentially presented for both stages. The PSNR performance is compared with additional level of security.

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