Agricultural Image Processing Research Articles

Revolutionizing Precision Agriculture with Drone-Based Imaging and Fuzzy Intelligent Algorithms

This paper proposes a clear understanding of drone based agricultural image processing techniques using fuzzy intelligent algorithm. This procedure is used for drone-based agricultural monitoring through intelligent calculations by various images acquired and trained from the field. The objective is to upgrade the exactness and proficiency of crop yield and the accuracy in agriculture. The proposed methods use high quality images caught by drones to remove crop-related infections and evaluate their wellbeing status. The fuzzy based calculation is applied to characterize the harvest wellbeing status and produce a choice to guide and assist the farmers of various regions to concentrate the area of infection based on the images captured from drones. The projected approach is assessed utilizing the information and the consideration and appropriate recommendation can be made after careful evaluation of infected region in the field. Graphical User Interface (GUI) is developed using fuzzy based algorithm which can segment the infectious leaves and classify the type of diseases and check the healthiness of the leaves. Results show that the proposed approach accomplishes higher exactness and improved effectiveness in distinguishing crop wellbeing status in comparable with existing the procedures. This study shows the capability of integrating drone-based agriculture with fuzzy intelligent image processing tool which makes efficient for farmers to improve the quality of crops, attain greater yield and to develop the nation with drone-based agribusiness.

Current developments in machine learning for agriculture: Semantic division approaches and critical evaluation of farm image analysis

As agriculture continues to evolve, there is an increasing demand for efficient as well as sustainable practices to ensure food security and optimize resource utilization. Agricultural image processing is revolutionized by the integration of machine learning techniques with conventional approaches and semantic segmentation has become a critical area of study attention. Performance in tasks like crop cover analysis, type identification and pest/disease detection has been improved by this synthesis. Using segmentation principles as a guide, this paper thoroughly examines current developments in conventional and machine learning-based semantic segmentation techniques for agricultural imagery. The conversation focuses on machine learning's effective features that can be used in concert with the original image data. There are acknowledged challenges with agricultural image segmentation, such as the requirement for improved generalization and resilience. The review tackles the lack of labelled samples by showcasing novel approaches such as dataset augmentation and multimodal information integration that improve machine learning techniques. This culminates in a valuable guide for using image semantically segmented in agricultural informatization.

An inquiry of image processing in agriculture to perceive the infirmity of plants using machine learning

An inquiry of image processing in agriculture to perceive the infirmity of plants using machine learning

CS-Net: Conv-Simpleformer Network for agricultural image segmentation

Agricultural image segmentation needs to catch up to the development speed of deep learning, and the explosive computational overhead and limited high-quality labeled datasets are the main reasons preventing the application of Transformers to agricultural image segmentation. This study proposes a Simple-Attention Block (SIAB) using channel-by-channel and spatial convolutional computation, whose computational complexity is linearly correlated with the input image size. Then, we design a Simpleformer by cascading SIAB and FFN to reshape the Transformer architecture. Further, the fusion of CNN and Simpleformer constructs a dataset quality-independent agricultural image segmentation model (CS-Net). Finally, we evaluate CS-Net on four datasets, and compared with the state-of-the-art models, CS-Net has more advantageous inference speed and segmentation accuracy, which pushes the development of Transformers in the field of agricultural image processing. Additionally, we explore the reasons for the Transformers’ performance collapse for agricultural applications, providing research scholars with a theoretical foundation for related issues.

Application of Image Processing in Agriculture

Agriculture will face significant challenges in the 21st century to feed a record number of people and animals and generate resources for industry (for example, wood, cellulose, and energy); thus, it is essential increasing yield and reducing pollution, water consumption, and energy consumption [...]

Insights on assessing image processing approaches towards health status of plant leaf using machine learning

<p class="Abstract"><span lang="EN-US">With the advancement of digital image processing in agriculture and crop cultivation, imaging techniques are adopted to acquire real-time health status. Out of all the parts of plants, the leaf is the direct indicator of its health status, and hence applying various image processing approaches could benefit the process of yielding informative cases of plant health. At present, there are various approaches, e.g., feature extraction, segmentation, identification, the classification being evolved up with more dependencies being found in using machine learning; the studies show many contributions towards this challenge. However, it is not yet conclusive to understand the optimal approach. Hence, this paper highlights an explicit strength and weakness associated with the existing approaches existing imaging processing techniques to identify the disease condition from an input of plant leaves' image. The study also contributes to highlighting open-end research problems to have conclusive remarks about effectiveness. </span></p>

Survey Report on Image Processing in Agriculture

India is an agricultural country; wherein about 70% of the population depends on agriculture. Agriculture has changed more in the past century than it has since farming began many millennia ago. Research in agriculture is aimed towards increase of productivity and food quality at reduced expenditure and with increased profit. Current crop production practices, often called Precision Agriculture (PA), benefited from all earlier revolutions in crop production. Many times expert advice may not be affordable, majority times the availability of expert and their services may consume time. Image processing along with availability of communication network can change the situation of getting the expert advice well within time and at affordable cost since image processing was the effective tool for analysis of parameters. Image processing techniques in agriculture are mainly used for image categorization based on shape, size, color and texture for decision-making. This leads to agricultural input rationalization and environmental damage reduction by adjusting the agricultural practices like fertilizer and pesticide application to the site that demands and profit maximization. This paper intends to focus on the survey of application of image processing in agriculture field such as imaging techniques, weed detection and fruit grading. This paper presents a survey on some of the existing image processing techniques in agriculture.

A Review on Potato Disease Detection and Classification by exploiting Deep Learning Techniques

The edible potato comes in fifth for human consumption and fourth among main food crops. Since it is a crop that is vegetative grown, many pests and disease can be passed along from one generation to the next. Crop diseases, which have a negative impact on food security as well as economic losses, have a significant impact on the production and quality of yields from potato crops. Thus, the application of unique and precise deep learning-based algorithms for disease detection and classification is highly required. Identifying weaknesses in agricultural products, particularly potatoes requires the use of machine vision and image processing techniques. Deep learning and image processing have been used in agriculture to classify and number of disease and pests affecting potatoes has grown, and study in this area is still continuing. The use of artificial intelligence and image processing in agriculture for the classification and identification of potato pests and disease has grown, and work in this area is still ongoing. Different deep learning techniques, such as VGG19, VGG16, Google Net, Alex Net, and convolution neural network methods, can be used to address the disease problem in potatoes. These methods also examined multiple classes of potato diseases as: Healthy, Black Leg, Black Scurf, Pink Rot, Common Scab, etc. Food safety could be seriously threatened by the spread of potato disease. In this article, deep learning techniques for early detection of potato disease are discussed.

Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

A decrease in agricultural yield has been caused by uneven climatic conditions. Global agricultural economies are affected by this. Furthermore, if the crops are infected by any disease, the situation becomes even worse. In addition, farmers have to increase their yield in order to cope with a growing population. In order to detect and prevent various diseases from affecting crops, modern agricultural techniques, and systems are needed. One of the most profitable fruits on the market is the pomegranate, a fruit that produces an astounding yield in a number of Asian nations. Unfortunately, due to a number of conditions, the plants are infected with several diseases that destroy the entire crop leaving a terribly low yield of products. A key challenge in agricultural image processing is the recognition of disease in plants. To identify and classify diseases on pomegranate plants precisely, this framework uses image processing techniques such as acquisition, resizing, enhancement, segmentation, ROI extraction, and feature extraction to process images. The framework is implemented using a training set and a test set of images associated with pomegranate leaf disease. Image enhancement and image segmentation are usually used during the implementation process in order to identify ROIs and features. The use of machine learning (ML) may be a viable way to solve this problem. The problems are solved using ML models like linear discriminant analysis (LDA), K-Nearest Neighbor, Naive Bayes (NB), and ensembles (LDA–NB). By combining LDA with NB, the framework can achieve an accuracy of 96.49% for classifying diseases on pomegranate leaves.

Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

In recent years, agricultural image processing research has been a key emphasis. Image processing techniques are used by computers to analyze images. New advancements in image capture and data processing have simplified the resolution of a wide range of agricultural concerns. Crop disease classification and identification are crucial for the agricultural industry’s technical and commercial well-being. In agriculture, image processing begins with a digital color picture of a diseased leaf. Plant health and disease detection must be monitored on a regular basis in property agriculture. Plant diseases have had a tremendous impact on civilization and the Earth as a whole. Extensions of detection strategies and classification methods try to identify and categorize each ailment that affects the plant rather than focusing on a single disease among several illnesses and symptoms. This article describes a new support vector machine and image processing-enabled approach for detecting and classifying grape leaf disease. The given architecture includes steps for image capture, denoising, enhancement, segmentation, feature extraction, classification, and detection. Image denoising is conducted using the mean function, image enhancement is performed using the CLAHE method, pictures are segmented using the fuzzy C Means algorithm, features are retrieved using PCA, and images are eventually classed using the PSO SVM, BPNN, and random forest algorithms. The accuracy of PSO SVM is higher in performing classification and detection of grape leaf diseases.

Embedded System-Based Sticky Paper Trap with Deep Learning-Based Insect-Counting Algorithm

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.

Recognition and classification of pomegranate leaves diseases by image processing and machine learning techniques

Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features. An image classification will then be implemented by combining a supervised learning model with a support vector machine. The proposed framework is developed based on MATLAB with a graphical user interface. According to the experimental results, the proposed framework can achieve 98.39% accuracy for classifying diseased and healthy leaves. Moreover, the framework can achieve an accuracy of 98.07% for classifying diseases on pomegranate leaves.

Multifractal detrended fluctuation analysis parallel optimization strategy based on openMP for image processing

In the past few years, multifractal detrended fluctuation analysis (MF-DFA) method has been widely applied in the field of agricultural image processing. However, the agricultural image feature MF-DFA analyses involves a great deal of iterative processes and complex matrix operations, which require massive computation and processing time. In order to reduce processing time and improve analysis efficiency, we first develop a MF-DFA program that involves image preprocessing, image segmentation, local area accumulation matrix calculation, local area trend fitting, local area trend elimination, a global qth-order fluctuation function, and the Hurst index. Then, we analyze and compare MF-DFA each modules’ performance characteristics and explore its parallelism according to various segmentation scales s. Lastly, we propose a parallel optimization scheme based on OpenMP for the MF-DFA. The results of our rigorous performance evaluation clearly demonstrate that our proposed parallel optimization scheme can efficiently use multicore capability to extract rape leaf image texture characteristics.

Color calibration of digital images for agriculture and other applications

Image processing in agriculture relies primarily on correlating color changes within images’ region of interest to specific quality attributes (e.g., plant phenology, plant health, crop stress, maturity). Changes in lighting conditions during image acquisition affect image color, even though there is no change in quality, and produces misleading inference when used without calibration. The focus of this study was to develop a method for calibrating images to make them homogeneous to improve phenological comparisons. The method was developed with synthetic images and validated with actual plant images in laboratory and field conditions using a standard ColorChecker (X-Rite) chart. Six different color schemes were tested to determine the effect of patch order, and minimum number of patches required for efficient calibration. A user-coded ImageJ plugin named ‘ColorCal’ was developed in Fiji package for color calibration that derived and applied a [3×3] color calibration matrix, based on selected color patches and standard values. Modified total error and calibration performance index (CPI) were developed to evaluate calibration performance. Calibration using any 12 color patches taken in any order gave equal performance (0.14⩽CPI⩽0.26). Calibration performance using only commonly followed neutral color patches (e.g., white, gray) was poor (0.26⩽CPI⩽1.0). Using red (R), green (G), and blue (B) color patches was recommended as it produced visually similar images, the performance was comparable with 24 color patches (0.21⩽CPI⩽0.24), and was simple and practical. The developed plugin took ≈7 s for calibration (Windows laptop, Intel Core i5, and 8 GB RAM). Determining phenological and other applications using the plugin was more reliable than using the raw images.

Classification and recognition scheme for vegetable pests based on the BOF-SVM model

The current detection technology for vegetable pests mainly relies on artificial statistics, which exists many shortages such as requiring a large amount of labor, low efficiency, feedback delay and artificial faults. By rapid detection and image processing technology targeting at vegetable pests, not only can reduce manpower and pesticide use, but also provide decision support for precise spraying and improve the quality of vegetables. Practical research achievements are still relatively lacking on the rapid identification technology based on image processing technology in vegetable pests. Given the above background, this paper presents a classification and recognition scheme based on the bag-of-words model and support vector machine (BOF-SVM) on four important southern vegetable pests including Whiteflies, Phyllotreta Striolata, Plutella Xylostella and Thrips. This paper consists of four sub-algorithms. The first sub-algorithm is to compute the character description of pest images based on scale-invariant feature transformation. The second sub-algorithm is to compute the visual vocabulary based on bag of features. The third sub-algorithm is to compute the classifier of pests based on support vector machines. The last one is to classify the pest images using the classifier. In this study, C++ and Python language were used as implementation technologies with OpenCV and LibSVM function library based on BOF-SVM classification algorithm. Experiments showed that the average recognition accuracy was 91.56% for a single image category judgment with 80 images from the real environment, and the average time was 0.39 seconds. This algorithm has achieved the ideal operating speed and precision. It can provide decision support for UAV precise spraying, and also has good application prospect in agriculture. Keywords: agricultural image processing, vegetable pests, classification, recognition, bag of features, support vector machine DOI: 10.25165/j.ijabe.20181103.3477 Citation: Xiao D Q, Feng J Z, Lin T Y, Pang C H, Ye Y W. Classification and recognition scheme for vegetable pests based on the BOF-SVM model. Int J Agric & Biol Eng, 2018; 11(3): 190–196.

A Comprehensive Survey on Agricultural Image Processing

A Comprehensive Survey on Agricultural Image Processing

Applications of Image Processing in Agriculture: A Survey

Image processing has been proved to be effective tool for analysis in various fields and applications. Agriculture sector where the parameters like canopy, yield, quality of product were the important measures from the farmers’ point of view. Many times expert advice may not be affordable, majority times the availability of expert and their services may consume time. Image processing along with availability of communication network can change the situation of getting the expert advice well within time and at affordable cost since image processing was the effective tool for analysis of parameters. This paper intends to focus on the survey of application of image processing in agriculture field such as imaging techniques, weed detection and fruit grading. The analysis of the parameters has proved to be accurate and less time consuming as compared to traditional methods. Application of image processing can improve decision making for vegetation measurement, irrigation, fruit sorting, etc.