Analysis on Digital Image Processing for Plant Health Monitoring

Alina Granwehr,Verena Hofer

doi:10.53759/181x/jcns202101002

Alina Granwehr, Verena Hofer

Open Access

https://doi.org/10.53759/181x/jcns202101002

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Abstract

The country's ability to become self-sufficient in food production is becoming increasingly important. Agriculture is the primary occupation of a large portion of the population in equatorial countries like India, where the climate is ideal for the spread of plants. Pests and diseases are in control of about 25% of crop loss, according to a recent study released by the Food and Agriculture Organization. Black spot, leaf spot, rust, mildew, and botrytis blight are the most common plant diseases. Deep learning is a relatively new research technique for image processing and pattern recognition that has been proven to be highly productive in detection of plant leaf diseases.

Highlights

Plant nutrition is the analysis of the chemical elements and compounds that are needed for plant growth, metabolism, and external supply
Images taken in the experimental fields of the International Institute of Tropical Agriculture (IITA) in Bagamoyo District, Tanzania, were used to create the dataset for detecting cassava leaf diseases
The American Phyto pathological society's online image database contains thousands of scientifically peer-reviewed photographs of diseased, nutritionally impaired, pestaffected crops and plants. They are published in journals and are useful for plant pathology training, identification, and diagnosis

Summary

INTRODUCTION

Plant nutrition is the analysis of the chemical elements and compounds that are needed for plant growth, metabolism, and external supply. Plant disease detection is a critical step in avoiding crop losses in terms of both quality and quantity in the agricultural production system. Since image processing techniques are quick, automatic, and precise, they are used for the detection, quantification, and identification of plant diseases. The key steps in disease detection using image processing techniques are image acquisition, image pre-processing, image segmentation, feature extraction, and disease recognition. A. Research Methodology Crop diseases and nutrient shortages can be detected using image processing. Images taken in the experimental fields of the International Institute of Tropical Agriculture (IITA) in Bagamoyo District, Tanzania, were used to create the dataset for detecting cassava leaf diseases. The dataset, which included 2,415 cassava leaf images for pronounced symptoms of each class, as well as an additional nutrient deficiency class of 336 images, was fine-tuned using the transfer learning process. The software tool and its source code are freely accessible online (as a Matlab programme, which is described in the Plant Image Analysis database) and can be used with iPlant, a web and cloud-based plant biology infrastructure

LITERATURE SURVEY

DEEP LEARNING

Findings

CONCLUSION