Coffee plant is a plant whose seeds called coffee beans are grown in all over the world particularly in Ethiopia. The research focuses on three major type of coffee disease which occurs on the leave part of a coffee plant, these are Coffee Leaf Rust (CLR), Coffee Berry Disease (CBD), and Coffee Wilt Disease (CWD). The aim of this paper is recognition of the three types of coffee disease using imaging and machine learning techniques. The image of Coffee plant diseases were taken from the regions of Ethiopia where more coffee is produced i.e. Southern Nations, Nationalities, and Peoples, Jimma and Zegie. In this paper artificial neural network (ANN), k-Nearest Neighbours (KNN), Naïve and a hybrid of self organizing map (SOM) and Radial basis function (RBF) are used. We conduct experiment for each group of feature set in order to get a highly correlated and the more representing features. The total number of data sets is 9100. From the total of 9100, 70% were used for training and the remaining 30% were used for testing.. In general, the overall result showed that color features represents more than texture features regarding recognition of coffee plant diseases and the performance of combination of RBF (Radial basis function) and SOM (Self organizing map) is 90.07%.

<p>Coffee Leaf Rust (CLR), Coffee Berry Disease (CBD) and Coffee Wilt Disease (CWD) are the three main diseases that attack coffee plants. This paper presents the identification of these types diseases using hybrid approaches of image processing and decision tree. The images are taken from Southern Ethiopia, Jimma and Zegie. In this paper backpropagation artificial neural network (BPNN) and decision tree had been used as techniques; a total of 9100 images were collected. From these, 70% are used for training and the remaining 30% are used for testing. In general, 94.5% accuracy achieved when decision tree and BPNN with tanh activation function are combined.</p>

Abstract

Crop pest is an organism that creates damage on to the agriculture by feeding crops.

This paper presents soil characterization and classification using computer vision &amp; sensor network approach. Gravity Analog Soil Moisture Sensor with arduino-uno and image processing is considered for classification and characterization of soils. For the data sets, Amhara regions and Addis Ababa city of Ethiopia are considered for this study. In this research paper the total of 6 group of soil and each having 90 images are used. That is, form these 540 images were captured. Once the dataset is collected, pre-processing and noise filtering steps are performed to achieve the goal of the study through MATLAB, 2013. Classification and characterization is performed through BPNN (Back-propagation neural network), the neural network consists of 7 inputs feature vectors and 6 neurons in its output layer to classify soils. 89.7% accuracy is achieved when back-propagation neural network (BPNN) is used.