A particle swarm optimization based ensemble for vegetable crop disease recognition

Chaudhary, Archana; Thakur, Ramesh; Kolhe, Savita; Kamal, Raj

doi:10.1016/j.compag.2020.105747

Cited by 37 publications

(13 citation statements)

References 41 publications

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“…This experimental analysis was undergone with a population count of 10 and maximum iterations of 25 for the proposed pest identification and classification model. The proposed AHBA-CNLSTM was compared with other meta-heuristic algorithms like "Particle Swarm Optimization (PSO) [27], Tunicate Swarm Algorithm (TSA) [28], Deer Hunting Optimization Algorithm (DHOA) [29], HBA [26] and deep learning algorithms like CNN [7], deep-CNN [6], RCNN [5] and LSTM [2]".…”

Section: Resultsmentioning

confidence: 99%

IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

Kathole¹,

Vhatkar²,

Patil³

2022

Cybernetics and Systems

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The insect pests and crop diseases are the most critical factors that affect agricultural production, which reduces the sustainable development of agriculture. While detecting the pest, it is inconsistent to place the surveillance cameras near the target pests and the captured images from the Internet of Things (IoT) monitoring equipment at a constant location that is mostly insufficient for pest detection. IoT is a well-known advanced technology and an analytics system incorporated in diverse industries based on its unique abilities and flexibilities over a particular environment like agriculture. There is a demand for the IoT in agricultural areas to reduce the chemical crop protection agents and fertilizers to manage the efficient crop state and crop production. Hence, the data collection is through the IoT devices in this research model. This research aims to develop a pest identification and classification model to detect and identify the pests in the images. Initially, the IoT platform is created, and IoT devices conduct the data collection. Then, object detection is performed using Yolov3 to detect the pests in the images from the gathered images. The detected images are subjected to the Convolutional Neural Network (CNN) for gathering the deep features, which are then forwarded to the enhanced classifier, termed Convolution Neural Long Short-Term Memory (CNLSTM) for getting the classified outcomes as pest details, in which the optimization of parameters is done by Adaptive Honey Badger Algorithm (AHBA). These results demonstrated that the proposed method shows enhanced performance by rapidly collecting the information in agriculture and ensures the technical indication for population estimation and pest monitoring.

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Section: Resultsmentioning

confidence: 99%

IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

Kathole¹,

Vhatkar²,

Patil³

2022

Cybernetics and Systems

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“…It is an ensemble of various Decision Trees. Random Forest algorithm is executed on RapidMiner, Orange and Weka for predicting various vegetable crop diseases [10]. The performance observations are presented in Table 1 and Fig.…”

Section: Resultsmentioning

confidence: 99%

Data Mining in Disease Prediction

Thakur¹

2022

ijarcs

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Enhancement in information technology has led to design of many applications in the field of crop disease recognition. Disease recognition applications generate voluminous data. The disease related data can be processed using data mining techniques to predict various diseases. Data mining is a field of analysing, extracting data for furnishing new knowledge which represents the relationship between different patterns of data. Some of the data mining methods include classification, clustering, prediction and association rule mining. In the present work data mining is used for disease prediction.

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“…Collecting data is the main start before proceeding with the experiment's process [2]. To identify the healthiness and disease of the plant, the machine learning technique [3]- [5] was often applied to gain accuracy and performance. The clustering algorithm, as defined by L.a.b (value from the CIELAB colour scale) and coordinates of x and y-axis of the pixels [6], [7].…”

Section: Related Workmentioning

confidence: 99%

Time-Series Classification Vegetables in Detecting Growth Rate Using Machine Learning

et al. 2021

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IoT based innovative irrigation management systems can help in attaining optimum water-resource utilisation in the exactness farming landscape. This paper presents a clustering of unsupervised learning based innovative system to forecast the irrigation requirements of a field using the sensing of a ground parameter such as soil moisture, light intensity, temperature, and humidity. The entire system has been established and deployed. The sensor node data is gained through a serial monitor from Arduino IDE software collected directly and saved using the computer. Orange and MATLAB software is used to apply machine learning for the visualisation, and the decision support system delivers real-time information insights based on the analysis of sensors data. The plants organise either water or non-water includes weather conditions to gain various types of results. kNN reached 100.0%, SVM achieved 99.0% owhile Naïve Bayes achieved 87.40%.

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A particle swarm optimization based ensemble for vegetable crop disease recognition

Cited by 37 publications

References 41 publications

IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

Data Mining in Disease Prediction

Time-Series Classification Vegetables in Detecting Growth Rate Using Machine Learning

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