scholarly journals Crop Diseases and Pest Detection using Deep Learning and Image Processing Techniques

2021 ◽  
Vol 9 (VI) ◽  
pp. 372-380
Author(s):  
Rohit V
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Crop Production ◽  
Early Stage ◽  
Diagnostic System ◽  
Yield Reduction ◽  
Pests And Diseases ◽  
Crop Diseases ◽  
Crop Disease ◽  
Pest Detection

Crop pests and diseases play a significant role in yield reduction and quality. Controlling and preventing pests and crop diseases has therefore become a priority. If disease is detected at an early stage, this can increase crop production and provide benefit to farmers. Manual detection of these diseases and pests can be very tedious and time consuming for farmers, especially if they have large farms. We plan to model a crop disease and pest diagnostic system using image processing and deep learning techniques. Crop disease and pest detection can be done using deep learning and image recognition techniques on leaves and other areas of the crop.

scholarly journals The development of methodology and techniques for crop disease identification

2019 ◽  
Author(s):  
N. S. Tiwari ◽  
J. W. Richmond
Keyword(s):  
Image Classification ◽  
Crop Production ◽  
Early Stage ◽  
Cost Effective ◽  
Tea Leaves ◽  
Detection Problem ◽  
Pests And Diseases ◽  
Crop Disease ◽  
Pesticide Usage

AbstractIn India, an estimated 15-25% of potential crop production is lost due pest and diseases (Roy and Bezbaruah, 2002). The country needs not only to raise production but also ensure food security for its growing consumption needs while curbing excessive pesticide usage. Detection of pests and diseases at an early stage plays a significant role in addressing the above-mentioned concerns and image classification offers a cost-effective and scalable solution to the disease detection problem (A. Ramcharan et al. 2017). Here, the principles of transfer learning are implemented with pretrained model – Resnet34 (K. He et al. 2015), and test its effectiveness in image classification using a dataset of tea leaves. The novelty of this work is that the images used are not curated, individual leaves with controlled backgrounds but of plants in-situ. The effect of the level of zoom and background is examined and class activation maps are used to validate that the basis of classification is indeed the disease and not an artificial bias from factors such as background, lighting etc.

A Review on Rice Crop Disease Classification Using Computational Approach

2021 ◽  
Author(s):  
V. Malathi ◽  
M. P. Gopinath
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Disease Classification ◽  
Rice Crop ◽  
Support Vector ◽  
Processing Machine ◽  
Crop Diseases ◽  
Crop Disease

Rice is a significant cereal crop across the world. In rice cultivation, different types of sowing methods are followed, and thus bring in issues regarding sampling collection. Climate, soil, water level, and a diversified variety of crop seeds (hybrid and traditional varieties) and the period of growth are some of the challenges. This survey mainly focuses on rice crop diseases which affect the parts namely leaves, stems, roots, and spikelet; it mainly focuses on leaf-based diseases. Existing methods for diagnosing leaf disease include statistical approaches, data mining, image processing, machine learning, and deep learning techniques. This review mainly addresses diseases of the rice crop, a framework to diagnose rice crop diseases, and computational approaches in Image Processing, Machine Learning, Deep Learning, and Convolutional Neural Networks. Based on performance indicators, interpretations were made for the following algorithms namely support vector machine (SVM), convolutional neural network (CNN), backpropagational neural network (BPNN), and feedforward neural network (FFNN).

scholarly journals Image Recognition of Crop Diseases and Insect Pests Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Mingyuan Xin ◽  
Yong Wang
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Recognition ◽  
Insect Pest ◽  
Quality Level ◽  
Crop Pest ◽  
Crop Diseases ◽  
Crop Disease

Deep learning algorithms have the advantages of clear structure and high accuracy in image recognition. Accurate identification of pests and diseases in crops can improve the pertinence of pest control in farmland, which is beneficial to agricultural production. This paper proposes a DCNN-G model based on deep learning and fusion of Google data analysis, using this model to train 640 data samples, and then using 5000 test samples for testing, selecting 80% as the training set and 20% as the test set, and compare the accuracy of the model with the conventional recognition model. Research results show that after degrading a quality level 1 image using the degradation parameters above, 9 quality level images are obtained. Use YOLO’s improved network, YOLO-V4, to test and validate images after quality level classification. Images of different quality levels, especially images of adjacent levels, are subjectively observed by human eyes, and it is difficult to distinguish the quality of the images. Using the algorithm model proposed in this article, the recognition accuracy is 95%, which is much higher than the basic 84% of the DCNN model. The quality level classification of crop disease and insect pest images can provide important prior information for the understanding of crop disease and insect pest images and can also provide a scientific basis for testing the imaging capabilities of sensors and objectively evaluating the image quality of crop diseases and pests. The use of convolutional neural networks to realize the classification of crop pest and disease image quality not only expands the application field of deep learning but also provides a new method for crop pest and disease image quality assessment.

scholarly journals Leaf Disease Detection using Deep Learning Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 3172-3175
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Learning Algorithm ◽  
Image Feature ◽  
Disease Detection ◽  
Proper Solution ◽  
Classification Image ◽  
Deep Learning Algorithm ◽  
Crop Diseases ◽  
Crop Disease

India is a nation of agriculture and over 70 per cent of our population relies on farming. A portion of our national revenue comes from agriculture. Agriculturalists are facing loss due to various crop diseases and it becomes tedious for cultivators to monitor the crop regularly when the cultivated area is huge. So the plant disease detection is important in agriculture field. Timely and accurate disease detection is important for the loss caused due to crop diseases which affects adversely on crop quality and yield. Early diagnosis and intervention can reduce the loss of plant due to disease and reduce the unnecessary drug usage. Earlier, automatic detection of plant disease was performed by image processing. For disease detection and classification, image processing tools and the machine learning mechanism are proposed. Crop disease will be detected through various stages of image processing such as image acquisition, pre-processing of image, image feature extraction, feature classification, disease prediction and fertilizer recommendation.detection of disease is important because it will may help farmers to provide proper solution to prevent these disease.

Deep learning framework for leaf damage identification

2021 ◽  
pp. 1063293X2199495
Author(s):  
Eddy Sánchez-DelaCruz ◽  
Juan P Salazar López ◽  
David Lara Alabazares ◽  
Edgar Tello Leal ◽  
Mirta Fuentes-Ramos
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Crop Production ◽  
Damage Identification ◽  
Machine Learning Algorithms ◽  
Leaf Damage ◽  
Nutritional Deficiencies ◽  
Learning Framework ◽  
Valencia Orange ◽  
Local Plants

Foliar disease is common problem in plants; it appears as an abnormal change in the plant’s characteristics, such as the presence of lesions and discolorations, among others. These problems may be related to plant growth, which causes a decrease in crop production, impacting the agricultural economy. The causes of leaf damage can be variable, such as bacteria, viruses, nutritional deficiencies, or even consequences of climate change. Motivated to find a solution for this problem, we aim that using image processing and machine learning algorithms (MLA), these symptomatic characteristics of the leaf can be used to classify diseases. Then, contributions of this research are (i) the use of image processing methods in the feature extraction (characteristics), and (ii) the combination of assembled algorithms with deep learning to classify foliar features of Valencia orange (Citrus Sinensis) tree leaves. Combining these two classification approaches, we get optimal rates in binary datasets and highly competitive percentages in multiclass sets. This, using a database of images of three types of foliar damage of local plants. Result of combination of these two classification strategies is an exceptional reliable alternative for leaf damage identification of orange and other citrus plants.

scholarly journals Multi-Level Deep Learning Model for Potato Leaf Disease Recognition

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2064
Author(s):  
Javed Rashid ◽  
Imran Khan ◽  
Ghulam Ali ◽  
Sultan H. Almotiri ◽  
Mohammed A. AlGhamdi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Disease Detection ◽  
Potato Leaf ◽  
Crop Species ◽  
Leaf Disease ◽  
Crop Diseases ◽  
Learning Technique ◽  
Multi Level ◽  
Deep Learning Model

Potato leaf disease detection in an early stage is challenging because of variations in crop species, crop diseases symptoms and environmental factors. These factors make it difficult to detect potato leaf diseases in the early stage. Various machine learning techniques have been developed to detect potato leaf diseases. However, the existing methods cannot detect crop species and crop diseases in general because these models are trained and tested on images of plant leaves of a specific region. In this research, a multi-level deep learning model for potato leaf disease recognition has developed. At the first level, it extracts the potato leaves from the potato plant image using the YOLOv5 image segmentation technique. At the second level, a novel deep learning technique has been developed using a convolutional neural network to detect the early blight and late blight potato diseases from potato leaf images. The proposed potato leaf disease detection model was trained and tested on a potato leaf disease dataset. The potato leaf disease dataset contains 4062 images collected from the Central Punjab region of Pakistan. The proposed deep learning technique achieved 99.75% accuracy on the potato leaf disease dataset. The performance of the proposed techniques was also evaluated on the PlantVillage dataset. The proposed technique is also compared with the state-of-the-art models and achieved significantly concerning the accuracy and computational cost.

scholarly journals Combining experiential and social learning approaches for crop disease management in a smallholder context: a complex socio-ecological problem

2020 ◽  
Vol 2 (3) ◽  
pp. 265-282
Author(s):  
Shiferaw Tafesse ◽  
B. van Mierlo ◽  
C. Leeuwis ◽  
R. Lie ◽  
B. Lemaga ◽  
...  
Keyword(s):  
Social Learning ◽  
Disease Management ◽  
Crop Production ◽  
Smallholder Farmers ◽  
Disease Incidence ◽  
Disease Diagnosis ◽  
Ecological Problem ◽  
Learning Approaches ◽  
Crop Diseases ◽  
Crop Disease

Abstract Effective management of crop diseases is a key precondition for sustainable crop production and to improve food security globally. However, learning approaches that improve smallholder farmers’ knowledge, perceptions, and practices to deal with crop diseases by fostering social and technical innovations are seldom studied. A study was conducted to examine: (1) how a combination of experiential and social learning approaches influences potato farmers’ knowledge, perceptions, and practices in bacterial wilt and its management in Ethiopia and (2) the implications of combining the two approaches for complex crop disease management in smallholder context. Data were derived from face-to-face in-depth interviews, reflective workshops, and participant observations. The findings showed that farmers’ knowledge and perceptions about disease incidence, the pathogen that causes the disease, its spreading mechanisms, host plants, and disease diagnosis were changed. Farmers’ practices in management of the disease were also improved. Learning about the cause of the disease stimulated the identification of locally relevant spreading mechanisms and the feasibility of a range of recommended disease management methods. Moreover, farmers recognized their interdependency, role, and responsibility to cooperate to reduce the disease pressure in their community. We conclude that learning interventions aiming to improve smallholder farmers’ knowledge, perceptions, and practices to deal with complex crop diseases need to combine experiential and social learning approaches and consider farmers’ local knowledge.

scholarly journals Early-Stage Brown Spot Disease Recognition in Paddy Using Image Processing and Deep Learning Techniques

2021 ◽  
Vol 38 (6) ◽  
pp. 1755-1766
Author(s):  
Santosh Kumar Upadhyay ◽  
Avadhesh Kumar
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Early Stage ◽  
Brown Spot ◽  
Classification Model ◽  
Crop Loss ◽  
Spot Disease ◽  
Total Leaf ◽  
Brown Spot Disease ◽  
Fully Connected

India is an agricultural country. Paddy is the main crop here on which the livelihood of millions of people depends. Brown spot disease caused by fungus is the most predominant infection that appears as oval and round lesions on the paddy leaves. If not addressed on time, it might result in serious crop loss. Pesticide use for plant disease treatment should be limited because it raises costs and pollutes the environment. Usage of pesticide and crop loss both can be minimized if we recognize the disease in a timely manner. Our aim is to develop a simple, fast, and effective deep learning structure for early-stage brown spot disease detection by utilizing infection severity estimation using image processing techniques. The suggested approach consists of two phases. In the first phase, the brown spot infected leaf image dataset is partitioned into two sets named as early-stage brown spot and developed stage brown spot. This partition is done on the basis of calculated infection severity. Infection severity is computed as a ratio of infected pixel count to total leaf pixel count. Total leaf pixel counts are determined by segmenting the leaf region from the background image using Otsu's thresholding technique. Infected pixel counts are determined by segmenting infected regions from leaf regions using Triangle thresholding segmentation. In the second phase, a fully connected CNN architecture is built for automatic feature extraction and classification. The CNN-based classification model is trained and validated using early-stage brown spot, developed stage brown spot, and healthy leaves images of rice plants. Early-stage brown spot and developed stage brown spot images used in training and validation are the same images that are obtained in phase 1. The experimental analysis shows that the proposed fully connected CNN-based early-stage brown spot disease recognition model is an effective approach. The classification accuracy of the suggested model is found to be 99.20%. The result of the suggested method is compared with those existing CNN-based disease recognition and classification methods that have used leaf images to recognize the diseases. It is observed that the performance of our method is significantly better than compared methods.

scholarly journals Pest Detection using Image Processing

2019 ◽  
Vol 9 (2) ◽  
pp. 1496-1498
Keyword(s):  
Image Processing ◽  
Economic Activity ◽  
Large Scale ◽  
Early Stage ◽  
Productivity Loss ◽  
Morphological Operations ◽  
Image Processing Techniques ◽  
Pest Detection ◽  
Processing Techniques ◽  
Leaf Image

Agriculture is one of the most significant economic activity. They are many ways that leads to the low productivity of agriculture, but the best method to protect the crop is by detecting the diseases in the early stage. In most of the cases diseases are caused by pest, insects, pathogens which reduce the productivity of the crop at the large scale. If pests are detected on the leaves then, precautions should be taken to avoid huge productivity loss at the end. The main objective of this paper is to identify the pests using image processing techniques like Gaussian blur, segmentation, watershed separation, morphological operations. These techniques are more efficient and less time consuming while identifying the pests over the leaf image with high intensity.

scholarly journals Pest Detection in Plants Using Convolutional Neural Network

2021 ◽  
Vol 9 (11) ◽  
pp. 1583-1592
Author(s):  
Savita Sharma
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Climate Changes ◽  
Agricultural Sector ◽  
Predictive Analytics ◽  
Early Stage ◽  
Maximum Yield ◽  
Disease Classification ◽  
The World ◽  
Pest Detection

Abstract: Agriculture or farming is an imperative occupation since the historical backdrop of humanity is kept up. Artificial Intelligence is leading to a revolution in the agricultural practices. This revolution has safeguarded the crops from being affected by distinct factors like climate changes, porosity of the soil, availability of water, etc. The other factors that affect agriculture includes the increase in population, changes in the economy, issues related to food security, etc. Artificial Intelligence finds a lot of applications in the agricultural sector also which includes crop monitoring, soil management, pest detection, weed management and a lot more. Significant problems for sustainable farming include detection of illness and healthy monitoring of plants. Therefore, plant disease must automatically be detected with higher precision by means of image processing technology at an early stage. It consists of image capturing, preprocessing images, image segmentation, extraction of features and disease classification. The digital image processing method is one of those strong techniques used far earlier than human eyes could see to identify the tough symptoms. Considering different climatic situations in various regions of the world that impact local weather conditions. These climate changes affect crop yield directly. There is a great demand for such a platform in the world of today which would enable the farmer market his farm products. We have proposed in this study a system where farmers can sell their products directly to customers without the intervention of distributors and traders. The predictive analytics system is necessary for the farmer to get the maximum yield which benefit the farmer. This may be done if the environment, market conditions and knowledge of the timely planning of farms are known properly. Keywords: Pest Detection, Artificial Intelligence, Agriculture, Image processing, Convolutional Neural Networks

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