Disease Detection in coffee plants using convolutional Neural Network
ABSTARCT :
Rust is a severe disease affecting many productive coffee regions. It is caused by pathogenic fungi that attack the underside of coffee leaves and it is characterized by the presence of yellow-orange and powdery points. If not treated, rust can cause a drop in coffee production of up to 45%. In this sense, this paper presents a contribution to the problem of rust identification that doesn’t use “handcrafted” features, i.e., features extracted according to rules established by human programmers. Instead, we propose to train a Convolutional Neural Network (CNN) to learn to identify rust infection. We evaluated our CNN in a set of images provided by an expert and comparison results show that our approach is able to to detect the infection with a high precision, as corroborated by the high Dice coefficient obtained.
EXISTING SYSTEM :
Coffee farmers or growers have difficulty in classifying nutritional deficiencies in coffee plants. Proper detection of these nutritional deficiencies could help them in giving proper intervention to plants.
DISADVANTAGE :
? Coffee growers or farmers should have enough knowledge to these symptoms so that they could perform the correct interventions
? Identification of nutritional deficiencies is done manually by the coffee growers or experts.
PROPOSED SYSTEM :
The proposed approach in classifying and identifying the nutritional deficiencies in coffee plants. The images of coffee leave were taken using two (2) Logitech cameras. The leave should be place inside the prototype and should be seen in the display. Once the images are captured it will be saved in a SD card as storage. During the image processing, the images is converted from RGB to grayscale. The resize image in grayscale is converted into vector input. Once the images are in vector format, tensor flow will be used in the Convolutional Neural Network (CNN). The CNN algorithm will classify and detect the input images. The LCD will be used to display the detected nutritional deficiency in the leaves. It will be the basis for the recommended fertilizer in the plant.
ADVANTAGE :
? CNN has a high accuracy in detecting and classifying the nutritional deficiencies in coffee plants.
? The prototype was evaluated, and results shows that it is an effective alternative for classifying and detecting the nutritional deficiencies in coffee plants.
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