Application of Feature Selection for Identification of Cucumber Leaf Diseases (Cucumis sativa L.)

According to data from BPS Kabupaten Jember, the amount of cucumber production fluctuated from 2013 to 2017. Some literature also mentions that one of the causes of the amount of cucumber production is disease attacks on these plants. Most of the cucumber plant diseases found in the leaf area such as downy mildew and powdery mildew which are both caused by fungi (fungal diseases). So far, farmers check cucumber plant diseases manually, so there is a lack of accuracy in determining cucumber plant diseases. To help farmers, a computer vision system that is able to identify cucumber diseases automatically will have an impact on the speed and accuracy of handling cucumber plant diseases. This research used 90 training data consisting of 30 healthy leaf data, 30 powdery mildew leaf data and 30 downy mildew leaf data. while for the test data as many as 30 data consisting of 10 data in each class. To get suitable parameters, a feature selection process is carried out on color features and texture features so that suitable parameters are obtained, namely: red color features, texture features consisting of contrast, Inverse Different Moment (IDM) and correlation. The K-Nearest Neighbor classification method is able to classify diseases on cucumber leaves (Cucumis sativa L.) with a training accuracy of 90% and a test accuracy of 76.67% using a variation of the value of K = 7.

OTKRIVANJE I KLASIFIKACIJA BOLESTI USJEVA NA TEMELJU INFORMACIJSKOG HIBRIDNOG PRISTUPA

The objective of this paper to identify the diseases in the leaves of the all plants. Plant disease diagnosis helps to improve both the quality and quantity of crop productivity. In existing, to detect the diseases they used the spectroscopic techniques. These techniques are very expensive and can only be utilized by trained persons only. This work proposes an approach for the detection of leaf diseases based on the characterization of texture, shape and color properties. The detection of diseases which are detected using ISRC(improved sparse Representation Classifier) technique. First the GENABC clustering approach is applied to the input image to segment the affected area. Then extract the features from the affected area by using feature extraction techniques. In this paper Improved Transform Encoded Local Pattern used to extract the texture feature, Enhanced Gradient Feature (EGF) to extract the shape and Improved Color Histogram Techniques(ICH) are used to extract the color. And then these features are given to the ISRC classifier to get the exact type of disease on affected leaves. To analyze the performance of the proposed method we use four metrics. They are classification accuracy, error rate, precision value and recall value. From the analysis of experimental results, the ISRC method provides the best result than the existing approach.Cilj ovog rada je identificirati bolesti u listovima svih biljaka. Dijagnoza biljnih bolesti pomaže poboljšati kvalitetu i količinu produktivnosti usjeva. Za otkrivanje bolesti koriste se spektroskopske tehnike. Te tehnike su vrlo skupe i mogu ih koristiti samo obučene osoba. Ovaj rad predlaže pristup za otkrivanje bolesti listova na temelju karakterizacije svojstava teksture, oblika i boja. Otkrivanje bolesti koje se detektiraju uporabom ISRC tehnike. Najprije se primjenjuje GENABC klastering pristup na ulaznu sliku za segmentiranje pogođenog područja. Zatim se ekstrahiraju značajke sa zahvaćene površine pomoću tehnika ekstrakcije značajki. U ovom se radu koristi poboljšana transformirana enkodirana lokalna shema koja se koristi za izdvajanje značajki teksture, poboljšane značajke gradijenata (EGF) za izdvajanje oblika i poboljšane tehnike hektologije boja (ICH) za izdvajanje boje. Zatim se ove značajke daju ISRC klasifikatoru kako bi dobili točnu vrstu bolesti na zahvaćenom lišću. Za analizu izvedbe predložene metode koristimo četiri metrike. To su točnost klasifikacije, stopa pogrešaka, preciznost i vrijednost opoziva. Iz analize eksperimentalnih rezultata ISRC metoda daje bolji rezultat od postojećeg pristupa

Techniques of deep learning and image processing in plant leaf disease detection: a review

Computer vision techniques are an emerging trend today. Digital image processing is gaining popularity because of the significant upsurge in the usage of digital images over the internet. Digital image processing is a practice that can help in designing sophisticated high-end machines, which can hold the ophthalmic functionality of the human eye. In agriculture, leaf examination is important for disease identification and fair warning for any deficiency within the plant. Many prominent plant species are facing extinction because of a lack of knowledge. A proper realization of computer vision techniques aid in extracting a significant amount of information from leaf image. This necessitates the requirement of an automatic leaf disease detection method to diagnose disease occurrences and severity, for timely crop management, by spraying pesticides. This study focuses on techniques of digital image processing and machine learning rendered in plant leaf disease detection, which has great potential in precision agriculture. To support this study, techniques exercised by various researchers in recent years are tabulated

Deep Learning-Based Segmentation and Quantification of Cucumber Powdery Mildew Using Convolutional Neural Network

Powdery mildew is a common disease in plants, and it is also one of the main diseases in the middle and final stages of cucumber (Cucumis sativus). Powdery mildew on plant leaves affects the photosynthesis, which may reduce the plant yield. Therefore, it is of great significance to automatically identify powdery mildew. Currently, most image-based models commonly regard the powdery mildew identification problem as a dichotomy case, yielding a true or false classification assertion. However, quantitative assessment of disease resistance traits plays an important role in the screening of breeders for plant varieties. Therefore, there is an urgent need to exploit the extent to which leaves are infected which can be obtained by the area of diseases regions. In order to tackle these challenges, we propose a semantic segmentation model based on convolutional neural networks (CNN) to segment the powdery mildew on cucumber leaf images at pixel level, achieving an average pixel accuracy of 96.08%, intersection over union of 72.11% and Dice accuracy of 83.45% on twenty test samples. This outperforms the existing segmentation methods, K-means, Random forest, and GBDT methods. In conclusion, the proposed model is capable of segmenting the powdery mildew on cucumber leaves at pixel level, which makes a valuable tool for cucumber breeders to assess the severity of powdery mildew

Classification Models for Plant Diseases Diagnosis: A Review

Plants are important source of our life. Crop production in a good figure and good quality is important to us. The diagnosis of a disease in a plant can be manual or automatic. But manual detection of disease in a plant is not always correct as sometimes it can be not be seen by naked eyes so an automatic method of detection of plant diseases should be there. It can make use of various artificial intelligence based or machine learning based methods. It is a tedious task as it needs to be identified in earlier stage so that it will not affect the entire crop. Disease affects all species of plant, both cultivated and wild. Plant disease occurrence and infection severity vary seasonally, regarding the environmental circumstances, the kinds of crops cultivated, and the existence of the pathogen. This review attempts to provide an exhaustive review of various plant diseases and its types, various methods to diagnose plant diseases and various classification models used so as to help researchers to identify the areas of scope where plant pathology can be improved

Numerical Modeling and Design of Machine Learning Based Paddy Leaf Disease Detection System for Agricultural Applications

In order to satisfy the insatiable need for ever more bountiful harvests on the global market, the majority of countries deploy cutting-edge technologies to increase agricultural output. Only the most cutting-edge technologies can ensure an appropriate pace of food production. Abiotic stress factors that can affect plants at any stage of development include insects, diseases, drought, nutrient deficiencies, and weeds. On the amount and quality of agricultural production, this has a minimal effect. Identification of plant diseases is therefore essential but challenging and complicated. Paddy leaves must thus be closely watched in order to assess their health and look for disease symptoms. The productivity and production of the post-harvest period are significantly impacted by these illnesses. To gauge the severity of plant disease in the past, only visual examination (bare eye observation) methods have been employed. The skill of the analyst doing this analysis is essential to the caliber of the outcomes. Due to the large growing area and need for ongoing human monitoring, visual crop inspection takes a long time. Therefore, a system is required to replace human inspection. In order to identify the kind and severity of plant disease, image processing techniques are used in agriculture. This dissertation goes into great length regarding the many ailments that may be detected in rice fields using image processing. Identification and classification of the four rice plant diseases bacterial blight, sheath rot, blast, and brown spot are important to enhance yield. The other communicable diseases, such as stem rot, leaf scald, red stripe, and false smut, are not discussed in this paper. Despite the increased accuracy they offer, the categorization and optimization strategies utilized in this work lead it to take longer than typical to finish. It was evident that employing SVM techniques enabled superior performance results, but at a cost of substantial effort. K-means clustering is used in this paper segmentation process, which makes figuring out the cluster size, or K-value, more challenging. This clustering method operates best when used with images that are comparable in size and brightness. However, when the images have complicated sizes and intensity values, clustering is not particularly effective

An advanced deep learning models-based plant disease detection: A review of recent research

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation

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