Leaf Disease Detection using NN


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Agricultural productivity is that issue on that Indian Economy extremely depends. this is often the one in all the explanations that malady detection in plants plays a very important role in the agriculture field, as having the malady in plants are quite natural. If correct care isn’t taken during this space then it causes serious effects on plants and because of that various product quality, amount or productivity is affected. Detection of disease through some automatic technique is helpful because it reduces an oversized work of watching in huge farms of crops, and at terribly early stage itself it detects the symptoms of diseases means that after they seem on plant leaves. This paper presents a neural network algorithmic program for image segmentation technique used for automatic detection still as the classification of plants and survey on completely different diseases classification techniques that may be used for plant leaf disease detection. Image segmentation, that is a very important facet for malady detection in plant disease, is completed by victimization genetic algorithmic program



The existing methodology for disease detection is a just optic observation by specialists through that identification and detection of plant diseases is completed. Fordoing thus, an oversized team of specialists still as continuous watching of specialists are needed, that prices terribly high once farms are massive. At an equivalent time, in some countries, farmers don’t have correct facilities or maybe concept that they’ll contact specialists. Because of that consulting specialists even price high still as time overwhelming too. In such condition, the advised technique proves to be helpful in watching massive fields of crops. And automatic detection of the diseases by simply seeing the symptoms on the plant leaves makes it easier still as cheaper. 


Leaf shape description is that the key downside in leaf identification. Up to now, several form options are extracted to explain the leaf form. however, there’s no correct application to classify the leaf once capturing its image and identifying its attributes, however. In plant leaf classification leaf is classed supported its completely different morphological options. a number of the classification techniques used are 

  • Fuzzy logic
  • Principal component  Analysis
  • k-Nearest Neighbor Classifier

 Plant  disease classification has wide application in Agriculture.


There are many techniques that are presently being utilized to make computer-based vision systems victimization options of plants extracted from pictures as input parameters to varied classifier systems. during this paper, a method to argument already existing techniques of plant leaves identification system is represented. this paper, a brand new classification model involving neural networks (NN) was utilized to develop a pc primarily based vision system for automatic identification of plant species.


Block diagram of Leaf Disease detection using Neural Network for classification in Image processing through Matlab


  • High Accuracy
  • Low complexity
  • Detection of images been classified without any noise


  • Bio-Farm
  • Bio-Pesticides


To improve recognition rate in classification process Artificial Neural Network, Bayes classifier, Fuzzy Logic, and hybrid algorithms can also be used.  


This paper gives the executed results on different diseases classification techniques that can be used for plant leaf disease detection and an algorithm for image segmentation technique used for  Automatic detection as well as classification of plant leaf diseases has been described later. Banana, beans, jackfruit, lemon, mango, potato, tomato, and sapota are some of those ten species on which proposed algorithm was tested. Therefore, related diseases for these plants were taken for identification. With very less computational efforts the optimum results were obtained, which also shows the efficiency of the proposed algorithm in recognition and classification of the leaf diseases. Another advantage of using this method is that the plant diseases can be identified at an early stage or the initial stage. 


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