scholarly journals A Smartphone-based Plant Disease Detection and Treatment Recommendation System using Machine Learning Techniques

2022 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Folasade Isinkaye
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Plant Disease ◽  
Recommendation System ◽  
Research Effort ◽  
Plant Diseases ◽  
Machine Learning Techniques ◽  
Treatment Recommendation ◽  
Disease Detection ◽  
Recommended Treatment

Plant diseases cause major crop production losses worldwide, and a lot of significant research effort has been directed toward making plant disease identification and treatment procedures more effective. It would be of great benefit to farmers to be able to utilize the current technology in order to leverage the challenges facing agricultural production and hence improve crop production and operation profitability. In this work, we designed and implemented a user-friendly smartphone-based plant disease detection and treatment recommendation system using machine learning (ML) techniques. CNN was used for feature extraction while the ANN and KNN were used to classify the plant diseases; a content-based filtering recommendation algorithm was used to suggest relevant treatments for the detected plant diseases after classification. The result of the implementation shows that the system correctly detected and recommended treatment for plant diseases

scholarly journals MACHINE LEARNING TECHNIQUES IN PLANT DISEASE DETECTION AND CLASSIFICATION – A STATE OF THE ART

2021 ◽  
pp. 362-372
Author(s):  
John Sreya ◽  
Leena Rose Arul
Keyword(s):  
Machine Learning ◽  
Plant Disease ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
Disease Detection ◽  
Detection Systems ◽  
Learning Techniques ◽  
Processing Techniques

As we belong to a developing country, the agricultural importance is a known criterion. Majority of the Indians depend on agriculture for their basic living. It also serves as the backbone of the Indian economy. Therefore this sector should be considered important and taken care of. Diseases affecting the plants and pest are the two major threats of agriculture production. Naked eye observation followed by the addition of chemical fertilizers is the traditional method adopted by most of the farmers to avoid plant diseases. But the main limitation to this method is that it works only in the case of small scale farming. In order to tackle this issue many automatic plant disease detection systems have been developed from the early 70s. This paper is intended to survey some of the existing works in plant disease recognition that include various procedures, materials and approaches. They use different machine learning algorithms, image processing techniques and deep learning methods for disease detection. This paper also compares and suggests novel methods to recognize and classify the various kinds of infections affecting agricultural plants.

scholarly journals A Review on Advances in Automated Plant Disease Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Range Images

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

Machine Learning Techniques for Plant Disease Detection

2021 ◽  
Author(s):  
Divyanshu Varshney ◽  
Burhanuddin Babukhanwala ◽  
Javed Khan ◽  
Deepika Saxena ◽  
Ashutosh kumar Singh
Keyword(s):  
Machine Learning ◽  
Plant Disease ◽  
Machine Learning Techniques ◽  
Disease Detection ◽  
Learning Techniques

scholarly journals Review and Further Prospects of Plant Disease Detection Using Machine Learning

2021 ◽  
pp. 105-115
Author(s):  
Vempati Ramsanthosh ◽  
Anati Sai Laxmi ◽  
Chepuri Sai Abhinay ◽  
Vadepally Santosh ◽  
Vybhav Kothareddy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Sustainable Development ◽  
Feature Extraction ◽  
Processing Time ◽  
Plant Disease ◽  
Health Surveillance ◽  
Plant Diseases ◽  
Disease Detection ◽  
Machine Learning Methods

Identifying of the plant diseases is essential in prevention of yield and volume losses in agriculture Product. Studies of plant diseases mean studies of visually observable patterns on the plant. Health surveillance and detecting diseases in plants is essential for sustainable development agriculture. It is very difficult to monitor plant diseases manually. It requires a lot of experiences in work, expertise in these field plant diseases and also requires excessive processing time. Therefore; image processing is used to detect plant diseases. Disease detection includes steps such as acquisition, image Pre-processing, image segmentation, feature extraction and Classification. We describe these methods for the detection of plant diseases on the basis of their leaf images; automatic detection of plant disease is done by the image processing and machine learning. The different leaf images of plant disease are collected and feature extracted of the various machine learning methods.

An Application of Machine Learning in Plant Leaves Disease Detection and Classification

2020 ◽  
Vol 17 (6) ◽  
pp. 2645-2652
Author(s):  
Sachin Dahiya ◽  
Tarun Gulati
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Automatic System ◽  
Crop Production ◽  
Plant Disease ◽  
Data Augmentation ◽  
Machine Intelligence ◽  
Disease Detection ◽  
Initial Stage

Plant disease severely affects the crop production. Food security is always a challenge because the population of the world is increasing at a rapid rate. Diseases in plants can be controlled at the initial stage with the help of automatic system that can be able to detect the wide variety of diseases before its spreading to the whole cultivation area. With the development of various machine learning and deep learning algorithms it is now possible to design such an automatic system. Deep neural network like convolution neural network are able to detect the plant disease with high accuracy. In this paper we have discussed about the deep learning techniques, CNN and its parameters, data augmentation, transfer learning and various factor that affects the performance of DL model. Recent studies that apply the machine intelligence in plant leaf disease detection are also discussed.

Deep Learning in Plant Diseases Detection for Agricultural Crops

2020 ◽  
Vol 11 (2) ◽  
pp. 41-58 ◽  
Author(s):  
Mohamed Loey ◽  
Ahmed ElSawy ◽  
Mohamed Afify
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Plant Disease ◽  
Plant Diseases ◽  
Food Plants ◽  
Disease Detection ◽  
Human Society ◽  
Research Papers ◽  
Limited Scope

Deep learning has brought a huge improvement in the area of machine learning in general and most particularly in computer vision. The advancements of deep learning have been applied to various domains leading to tremendous achievements in the areas of machine learning and computer vision. Only recent works have introduced applying deep learning to the field of using computers in agriculture. The need for food production and food plants is of utmost importance for human society to meet the growing demands of an increased population. Automatic plant disease detection using plant images was originally tackled using traditional machine learning and image processing approaches resulting in limited accuracy results and a limited scope. Using deep learning in plant disease detection made it possible to produce higher prediction accuracies as well as broadened the scope of detected diseases and plant species considered. This article presents a survey of research papers that presented the use of deep learning in plant disease detection, and analyzes them in terms of the dataset used, models employed, and overall performance achieved.

scholarly journals Assessment of Plant Disease Identification using GLCM and KNN Algorithms

2020 ◽  
Vol 8 (5) ◽  
pp. 4900-4904
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Plant Disease ◽  
Nearest Neighbors ◽  
Input Image ◽  
Plant Diseases ◽  
Disease Detection ◽  
Textural Features ◽  
K Nearest Neighbors ◽  
Disease Identification

One of the significant segments of Indian Economy is Cultivation. Occupation to almost 50% of the nation’s labor force is delivered by Indian cultivation segment. India is recognized to be the world's biggest manufacturer of pulses, rice, wheat, spices and spice harvests. Agronomist's financial progress is contingent on the excellence of the goods that they yield, which depend on on the plant's progress and the harvest they get. Consequently, in ground of cultivation, recognition of disease in plants shows an involved part. Plants are exceedingly disposed to to infections that disturb the progress of the plant which in chance distresses the natural balance of the agronomist. In order to distinguish a plant disease at right preliminary period, usage of automatic disease detection procedure is beneficial. The indications of plant diseases are noticeable in various portions of a plant such as leaves, etc. Physical recognition of plant disease by means of leaf descriptions is a wearisome job. The k-mean clustering procedure is utilized for the segmentation of input images. The GLCM (gray-level co-occurrence matrices) procedure is utilized which excerpts textural features from the input image and implementation of KNN (k-nearest neighbors) algorithm for image classification and produced classification accuracy from 70 to 75% for different inputs. Hence, it is required to develop machine learning based computational methods which will make the process of disease detection and classification using leaf images automatic. .. To advance concert of standing methods machine learning and deep learning algorithms will be utilized for more accurate classification.

scholarly journals CNN based Leaf Disease Identification and Remedy Recommendation System

2021 ◽  
Vol 9 (8) ◽  
pp. 953-957
Author(s):  
Mr. Yeresime Suresh
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Crop Yield ◽  
Plant Disease ◽  
Recommendation System ◽  
Plant Diseases ◽  
Human Intervention ◽  
Traditional Methods ◽  
Disease Identification ◽  
Growing Population

Abstract: In this present era, agriculture has become simply a means to feed ever growing population. It is very important where in more than 70% population depends on agriculture in India. The plant diseases effect the humans directly or indirectly by health and also economically. To detect these plant diseases we need an automatic way without much of human intervention. We attempt to analyse the disease using image processing and automate the detection by implementing machine learning methodology. Traditional methods were used to detect the diseases which lead to the use of large amount of pesticides harming the fertile soil and also the nature. A solution to this is to use current methodologies like Image Processing and Machine Learning that helps the farmers to detect the diseases faster and increase the crop yield. Keywords: Plant Disease, Remedy, Image Processing

scholarly journals Detection of soilborne disease utilizing sensor technologies: Lessons learned from studies on stem rot of peanut

2021 ◽  
Author(s):  
Xing Wei ◽  
Marcela Aguilera ◽  
Rachael Walcheck ◽  
Dorothea Tholl ◽  
Song Li ◽  
...  
Keyword(s):  
Crop Production ◽  
Plant Disease ◽  
Lessons Learned ◽  
Plant Diseases ◽  
Stem Rot ◽  
Disease Detection ◽  
Soilborne Disease ◽  
Current State ◽  
Major Constraint ◽  
Crop Disease

Soilborne plant diseases are a major constraint to crop production worldwide. Effective and economical management of these diseases is dependent on the ability to accurately detect and diagnose their signs and/or symptoms prior to widespread development in a crop. Sensor-based technologies are promising tools for automated crop disease detection, but research is still needed to optimize and validate methods for the detection of specific plant diseases. The overarching goal of our research is to use the peanut-stem rot plant disease system to identify and evaluate sensor-based technologies that can be utilized for the detection of soilborne plant diseases. Here we summarize the current state of sensor-based technologies for plant disease detection and provide examples from our own research that illustrate the advantages and limitations of different sensor-based methods for detecting soilborne diseases. In addition, the potential to adapt different sensor-based technologies to practical use in the field is discussed.

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