Early Rise Disease Detection In Crops Using Machine Learning with UAV

Project Title

Early Rise Disease Detection In Crops Using Machine Learning with UAV

Project Area of Specialization Artificial IntelligenceProject Summary

 Disease diagnosis is one of the major tasks for increasing food production in agriculture.
Although precision agriculture (PA) takes less time and provides a more precise application of agri-
cultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging
task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The
UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant
diseases. Several types of image processing software are available for vignetting and orthorectifica-
tion. The training and validation of datasets are important characteristics of data analysis. Currently,
different algorithms and architectures of machine learning models are used to classify and detect
plant diseases. These models help in image segmentation and feature extractions to interpret results.
Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index
(NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspec-
tral sensors to fit into the statistical models to deliver results. There are still various drifts in the
automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth,
resolution, background noise of the image, etc. The future of crop health monitoring using UAVs
should include a gimble consisting of multiple sensors, large datasets for training and validation,
the development of site-specific irradiance systems, and so on. This review briefly highlights the
advantages of automatic detection of plant diseases to the grower

Project Objectives

• Following are the Objective
• Use of AI in agriculture for more efficient results
• Early Rise protection of crops help to prevent economy
• Help Farmers
• Compare different CNN models to achieve best results

Project Implementation Method

• Following are the methods
• Data acquisition
• Data Pre Processing
• Generate a system for image processing
• Buying drone
• Field Testing

Benefits of the Project

Rapid population growth and climate change are the leading causes of food insecurity.
The advancements in UAVs and their systems to diagnose crop stress, pests, and diseases
have greatly benefitted growers. Increasing farm productivity and lowering the cost of pro-
duction using advanced technology is helping growers to increase yields and sustainability
on their farms. The development in the automatic detection of plant diseases using UAVs
has emerged as a novel technology of precision agriculture. UAVs are accurate and provide
large amounts of data regarding crop status, which aids in making management decisions.
However, there is still immense opportunity in plant disease diagnosis. As discussed in the
future considerations section, the development of various algorithms of machine learning
and collaboration with the other stems will help to reach this milestone.

Technical Details of Final Deliverable

Early disease prediction is important to help farmers to reduce the loss of crop yield especially for
crops that are of high value. This can be accomplished by developing models that can easily be
used by the farmers on their fields without requiring extensive technological know how. The present
work outlines one such application for rice growers that takes images of the farm and processes
it through Deep Learning (DL) model to predict the disease affected regions of the crop. The DL
model is based on Mask R-CNN which identifies the localized disease portion of the plant along
with its classification with accuracy of 87.6%. The paper shows that Mask R-CNN outperforms the
standard CNN model in terms of accuracy, precision, recall, and F1-score. The model was trained
on a locally collected dataset comprising of 1700 images of both diseased and healthy plants. The
data was collected using a simple mobile phone and was annotated manually.
At present we are working on developing an easy to use multi-lingual mobile application that could
be used on field by the farmers. We are also in the process of extending the dataset and the developed
model into a multi-class disease detector and classifier.
4

Final Deliverable of the Project HW/SW integrated systemCore Industry AgricultureOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Good Health and Well-Being for People, Decent Work and Economic GrowthRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	75000
Drone	Equipment	1	40000	40000
Camera	Equipment	1	30000	30000
Other minor cost	Miscellaneous	1	5000	5000