Banana Leaf Disease Identification Technique

There is no machine learning techniques have been used in an attempt to detect diseases in the banana plant such as banana bacterial wilt (BBW) and banana black sigatoka (BBS) that have caused a huge loss to many banana growers. The study investigated various computer vision techniques which led to the development of an approach that consists of four main phases. In phase one, images of Banana leaves were acquired using a standard digital camera. Phase two is the preprocessing phase where resizing and morphological operations occur. Next phase is the segmentation phase which translates RGB(Red Green Blue) image to YCbCr (Luminance Chrominance) color space which is then converted to a gray scale image and finally to a binarized image using Adaptive Contrast Map method. Next is the feature extraction phase where extraction of leaf features like color, texture and, shape occurs. Then comes the prominent phase were classification done Using Support Vector Machine classifier as classifier. Lastly, the performance of the classifier is evaluated to determine whether a leaf is diseased or not

Stubble retention and leaf disease in lupin and cereal crops

Retention of cereal stubbles can reduce leaf disease in lupins but increase leaf disease in cereals. The extent of cereal disease carry-over in stubbles depends on the locality and whether multiple cropping or crop rotation is practise

Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: a review

A Quick and precise crop leaf disease detection is important to increasing agricultural yield in a sustainable manner. We present a comprehensive overview of recent research in the field of crop leaf disease prediction using image processing (IP), machine learning (ML) and deep learning (DL) techniques in this paper. Using these techniques, crop leaf disease prediction made it possible to get notable accuracies. This article presents a survey of research papers that presented the various methodologies, analyzes them in terms of the dataset, number of images, number of classes, algorithms used, convolutional neural networks (CNN) models employed, and overall performance achieved. Then, suggestions are prepared on the most appropriate algorithms to deploy in standard, mobile/embedded systems, Drones, Robots and unmanned aerial vehicles (UAV). We discussed the performance measures used and listed some of the limitations and future works that requires to be focus on, to extend real time automated crop leaf disease detection system

Mixtures of modern and historical wheat cultivars under organic management in western Canada

Two historic (Red Fife and Marquis) and two modern (5602HR; AC Barrie) wheat (Triticum aestivum) cultivars were assessed to determine if cultivar mixtures provided a benefit to grain yield and disease and weed suppression in Manitoba over 3 site-years. 5602HR was the highest yielding sole cultivar while Marquis and AC Barrie were the lowest yielding sole cultivars. Red Fife yielded similar to 5602HR in several cases. Orthogonal contrasts across all site-years showed that 3 and 4 cultivar mixtures yielded similar (P>0.05) to 5602HR, the highest yielding monocrop

Identification of new sources of resistance to RHBV- rice hoja blanca virus

With the aim to find new sources of resistance to rice hoja blanca (white leaf) disease, transmitted by the insect Tagosodes orizicolus, 660 genotypes were evaluated under greenhouse and field conditions. Seven resistant genotypes were identified, and genomic studies were performed to demonstrate that the resistance in these sources is genetically different from that of Fedearroz 2000, which is currently the variety with the most resistance to hoja blanca. These new resistance sources constitute a resource that can be used to sustainably extend hoja blanca disease management throughout all of the rice-growing regions of tropical America. This is the first report of hoja blanca resistance in indica rice and different from that of Fedearroz 2000

Controlling Corn Diseases

Root and Stalk Rots, Leaf Diseases, and Leaf Disease Contro