Affiliation:
1. Division of Agricultural Engineering ICAR‐IARI New Delhi India
2. Division of Plant Pathology ICAR‐IARI New Delhi India
3. Division of Computer Applications ICAR‐IASRI New Delhi India
4. Vasantrao Naik Marathwada Krishi Vidyapeeth Parbhani Maharastra India
5. Division of Agricultural Statistics ICAR‐IASRI New Delhi India
6. Division of Agricultural Physics ICAR‐IARI New Delhi India
7. Department of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture Ataturk University Erzurum Turkey
8. Ataturk University to Electrical Engineering and Computer Science South Dakota State University Brookings South Dakota USA
Abstract
AbstractA machine learning‐based approach was utilized to develop a device for groundnut bud necrosis virus (GBNV) disease severity detection and estimation in tomato plants (Solanum lycopersicum L.). The study involved inoculating tomato plants with GBNV, monitoring changes in morphological and spectral characteristics, evaluating machine learning algorithms (decision tree [DT] classifier) for analysis and classification of disease severity, and developing and validating a device for disease detection and severity estimation. Spectral data analysis revealed distinct patterns in reflectance, with notable peaks observed in the 680 and 760 nm bands, while reflectance remained low and constant beyond 900 nm. Machine learning techniques, specifically a DT model, were employed to classify disease severity based on spectral data with high accuracy (95.01% training accuracy and 93.65% testing accuracy). The model identified the near‐infrared band as highly correlated (correlation coefficient of 0.82) with disease severity. Furthermore, a compact handheld device integrating a spectral sensor, organic light‐emitting diode display, and Raspberry Pi 3B was developed for real‐time disease severity estimation. The device demonstrated robust performance, accurately predicting disease severity at different growth stages, even in the absence of visible symptoms. Additionally, disease severity percentages obtained via reverse transcription polymerase chain reaction were used to validate the accuracy of the device's estimations. Its responsive nature, with estimated response times ranging from milliseconds to seconds, facilitates timely interventions in agricultural settings. Overall, this interdisciplinary approach, combining spectral analysis, machine learning, and device development, presents a promising solution for efficient disease monitoring and management in agriculture, contributing to enhanced crop health and food security.
Reference74 articles.
1. Plant Microbiome Engineering: Hopes or Hypes
2. Weather based forecasting of crop yields, pests and diseases—IASRI models;Agrawal R.;Journal of the Indian Society of Agricultural Statistics,2014
3. Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance
4. Smart farming for detection and identification of tomato plant diseases using light weight deep neural network
5. Development of a web based system to practice the estimation of plant disease severity;Andrahanndi D.T.;International Journal of Innovative Research in Technology,2020