This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model (F-HOBINM) and adaptive neuro classifier (ANFIS). India exports USD 0.28-million worth of neem leaf to the UK, USA, UAE, and Europe in the form of dried leaves and powder, both of which help reduce diabetes-related issues, cardiovascular problems, and eye disorders. Diagnosing neem leaf disease is difficult through visual interpretation, owing to similarity in their color and texture patterns. The most common diseases include bacterial blight, Colletotrichum and Alternaria leaf spot, blight, damping-off, powdery mildew, Pseudocercospora leaf spot, leaf web blight, and seedling wilt. However, traditional color and texture algorithms fail to identify leaf diseases due to irregular lumps and surfaces, and rough ridges, as the classification time involved takes as long as a week. The proposed F-HOBINM algorithm recognizes the leaf intensity through the leaky capacitor, and uses subjective intensity and physical stimulus to interpret the diagnosis. Further, the processed leaf images from the HOBINM algorithm are applied to the ANFIS classifier to identify neem leaf diseases. The experimental results show 92.18% accuracy from a database of 1,462 neem leaves.

KSII Transactions on Internet and Information Systems TIIS Paper Details

Herbal leaves are used widely in local medication. But now a day, an ordinary person has little knowledge about those herbs and he may not identify such herbals easily. As a first step of computer based recognition of herbs, an analysis has been made to identify the best method for segmenting leaves object from its background. This type of segmentation is a preprocessing step required in identification of species of leaves or plants. Several methods are available for detecting the objects based on global and local features of an image. In this paper we are examining various object detection techniques for segmenting leaves based on color, shape and texture. Features like local adaptive mean color, evidence based color model, color histogram techniques are used. Boundary structure model is used to detect the leaves based on boundary descriptors of an image and Chan-Vese algorithm is used to segment the leaves from complex background. To extract leaves from texture background, edge focusing algorithm is used. From our experimentation analysis, shape is the powerful characteristics of segmenting leaf images and Chan-Vese algorithm provides better results compared to other techniques without affecting the leaf colors, texture etc

Leaves are assuming basic part in today's life, on the grounds that numerous leaves are going about as herbs to plan drugs to cure diseases. In any case, every one of the leaves cannot be arranged effectively as herbs by people. To maintain a strategic distance from such trouble, we need to examine the morphological characters of every leave in light of the fact that morphological qualities of every plant and leaf differ for every crew. For breaking down morphological characters of leaves PCs are utilised, in the field of software engineering computerised picture handling strategies are utilised to investigate the pictures painstakingly. In this paper, we present shape context which is accustomed to extricating different elements from leaves, rather than separating symmetry driven components by SIFT, relative and SURF operators. Shape context is utilised to concentrate nearby and worldwide components from form focuses. In the wake of extricate components, for perceiving leaves delicate processing systems, for example, ant colony optimisation and bipartite matching calculations are utilised.