Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

Automatic Leaf Extraction from Outdoor Images

Automatic plant recognition and disease analysis may be streamlined by an image of a complete, isolated leaf as an initial input. Segmenting leaves from natural images is a hard problem. Cluttered and complex backgrounds: often composed of other leaves are commonplace. Furthermore, their appearance is highly dependent upon illumination and viewing perspective. In order to address these issues we propose a methodology which exploits the leaves venous systems in tandem with other low level features. Background and leaf markers are created using colour, intensity and texture. Two approaches are investigated: watershed and graph-cut and results compared. Primary-secondary vein detection and a protrusion-notch removal are applied to refine the extracted leaf. The efficacy of our approach is demonstrated against existing work.Comment: 13 pages, India-UK Advanced Technology Centre of Excellence in Next Generation Networks, Systems and Services (IU-ATC), 201

Prediction of Plant Disease from Weather Forecasting using Data Mining

Plant disease determination is an art and also science. Plant disease is essential problem that lower the quantity and also reduced the quality of agricultural production. Recent, pesticide is applied on plant without learned what the essential requirement of plant. Disease is the main the cause to death the plant and also influences the human health. Data mining is a comparatively novel research in agricultural. In this paper, our aim to develop the system to evaluate high accuracy and also detects the disease of the orange plant. The proposed system use segmentation techniques such as k-means clustering and deep neural network learning to predict the disease based on weather feature of the orange plant. This system helps the farmer to understand the disease of orange plant and also increase the yield of orange plant

A Brief Review on Plant Leaf Disease Detection Using Auto Adaptive Approach

This proposal is regarding automatic detection of diseases and pathological part present within the leaf pictures of plants and even within the agriculture Crop production it is through with advancement of technology that helps in farming to extend the production. Primarily there is downside of detection accuracy and in neural network approach support vector machine (SVM) is exist already. During this analysis proposal, a completely unique approach can design to extend accuracy victimization KNN. During this analysis work, we are going to work upon the advancement of the plant diseases prediction techniques and going to propose a completely unique approach for the detection rule

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

Neural Network for Papaya Leaf Disease Detection

The scientific name of papaya is Carica papaya which is an herbaceous perennial in the family Caricaceae grown for its edible fruit. The papaya plant is tree-like,usually unbranched and has hollow stems and petioles. Its origin is Costa Rica, Mexico and USA. The common names of papaya is pawpaw and tree melon. In East Indies and Southern Asia, it is known as tapaya, kepaya, lapaya and kapaya. In Brazil,it is known as Mamao. Papayas are a soft, fleshy fruit that can be used in a wide variety of culinary ways. The possible health benefits of consuming papaya include a reduced risk of heart disease, diabetes, cancer, aiding in digestion, improving blood glucose control in people with diabetes, lowering blood pressure, and improving wound healing. Disease identification in early stage can increase crop productivity and hence lead to economical growth. This work deals with leaf rather than fruit. Images of papaya leaf samples, image compression and image filtering and several image generation techniques are used to obtain several trained data image sets and then hence providing a better product. This paper focus on the power of neural network for detecting diseases in the papaya. Image segmentation is done with the help of k-medoid clustering algorithm which is a partitioning based clustering method

Study of Turmeric Plant Diseases and Methods of Disease Identification using Digital Image Processing Techniques

The vast economic potentiality of the crop can be adequately established by the fact that about 20-30 million people consume turmeric in India on a regular basis besides those in other countries of the world which may include over 2 billion consumers. Its cultivation is highly labor intensive and offers employment to about 2.0 million families engaged in cultivation, trading and commerce in turmeric throughout India. Turmeric powder is used as medicine for certain diseases and also used as an antiseptic. During cultivation turmeric is very much affected by disease and also procedure for to identify diseases early infected stage using digital image processing and pattern recognition techniques, such as rhizome rot disease, leaf spot disease, leaf blotch disease and dry rot disease that result in great loss for the farmers. It occurs in a very virulent form and if not controlled, causes widespread damage and even total destruction of the entire turmeric plantations without any early indications of the diseases. The aim of this paper is to study and identify various diseases in the turmeric plants and also procedure for to identify diseases early infected stage using digital image processing and pattern recognition techniques

Implementation Strategy of Tomato Plant Disease Detection using Optimized Feature Extraction Method

Tomato plants normally have a single growing season, during which they develop, bear fruit, and then perish. The species first appeared in Western South America, Mexico, and Central America. In the sixteenth century, they were brought to various regions. They produce self-pollinating yellow blooms. After being pollinated, the blooms turn into fruits, which, depending on the type, might be red, yellow, green, or even purple. Tomatoes are a well-liked element in many recipes, including salads, sauces, and soups. They are high in vitamins A and C, potassium, and antioxidants. They are afflicted by several illnesses that can seriously harm the plant and lower crop output. These illnesses were brought on by a variety of minor inadequacies in the soil, air, and the major. These diseases are produced by a range of mineral deficiencies in the soil, and the air, and their primary causes include insects and fungi. We discovered that machine learning is a potential avenue for detecting these diseases before they spread to the plant. As a result, we thought about using Feature Extraction Methods to optimize the dat

Analysis Of Banana Plant Disease Characterization Using Thermal Camera With Tressolding Method

Banana is a fruit plant that is widely produced in Indonesia. Unfortunately, this plant is very susceptible to diseases which can reduce the quality and quantity of the crop. This paper proposes disease detection in banana plants using a thermal camera. The detection is carried out using image processing techniques with multilevel thresholding methods. The image is captured using a thermal camera, then the image is preprocessed to suit what is desired. After that, so that the position is the same as the image taken using a digital camera, the image produced by the thermal camera is carried out by an image registration process. The image processing result is compared with the ground truth image obtained from a digital camera to determine the effectiveness of the proposed method. The effectiveness of the proposed method is measured using the parameters Recall, Precision, F-measure, and Accuracy. The effectiveness of the proposed method is quite effective because it produces parameter values above 80%, namely the recall value of 86,59%, the Precision of 99,1%, the F-measure of 92%, and the accuracy of 89,78%