Identification of wheat plant diseases using Computer Vision technique
Modern technologies have given human society the ability to produce enough food to meet the demand of more than 7 billion people. However, food security remains threatened by a number of factors including climate change. Plant diseases are not only a threat to food security at the global scale, but
2025-06-28 16:27:45 - Adil Khan
Identification of wheat plant diseases using Computer Vision technique
Project Area of Specialization Artificial IntelligenceProject SummaryModern technologies have given human society the ability to produce enough food to meet the demand of more than 7 billion people. However, food security remains threatened by a number of factors including climate change. Plant diseases are not only a threat to food security at the global scale, but can also have disastrous consequences for smallholder farmers whose livelihoods depend on healthy crops. In the developing world, more than 80 percent of the agricultural production is generated by smallholder farmers and reports of yield loss of more than 50% due to pests and diseases are common. Furthermore, the largest fraction of hungry people (50%) live in smallholder farming households making smallholder farmers a group that's particularly vulnerable to pathogen-derived disruptions in food supply.
Smartphone in particular offer very novel approaches to help identify diseases because of their computing power, high-resolution displays, and extensive built-in sets of accessories, such as advanced HD cameras. It is widely estimated that there will be between 5 and 6 billion Smartphone on the globe by 2020.The combined factors of widespread Smartphone penetration, HD cameras, and high performance processors in mobile devices lead to a situation where disease diagnosis based on automated image recognition, if technically feasible, can be made available at an unprecedented scale. Here, we demonstrate the technical feasibility using a deep learning approach utilizing 15000 images of wheat specie with 6 diseases (or healthy). Computer vision, and object recognition in particular, has made tremendous advances in the past few years. The PASCAL VOC Challenge and more recently the Large Scale Visual Recognition Challenge (ILSVRC) based on the ImageNet dataset have been widely used as benchmarks for numerous visualization-related problems in computer vision, including object classification. In 2012, a large, deep Convolutional Neural Network (CNN) achieved a top-5 error of 16.4% for the classification of images into 1000 possible categories. In the following 3 years, various advances in deep Convolutional Neural Network (CNN) lowered the error rate to 3.57%. While training large neural networks can be very time-consuming, the trained models can classify images very quickly, which makes them also suitable for consumer applications on Smartphone.
We are head toward to achieve accuracy of already 100% or nearly 100% but to reduce error from 16% to minimum as much as we can do for. Our results are a first step toward a Smartphone-assisted plant disease diagnosis system.
Project ObjectivesThe objective of the project is to identify the different diseases of wheat plant and cure them at early stages to prevent the loss of crop. The following diseases are to be identified:
- Identification of leaf rust in wheat plant.
- Identification of stem rust in wheat plant.
- Identification of barley dwarf disease in wheat plant.
- Identification of powdery mildew disease in wheat plant.
Agriculture plays a vital role in the economic growth of any country. Wheat crop are the main crop of Pakistan and plays an important role in the economy of Pakistan. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without risk to loss the crop being explicitly programmed.
We will use the Machine learning algorithms to identify the diseases in wheat crop and cure them at early stages to maintain the crop health and reduce the
IoT based smart farming system is built for monitoring soil nutrients and soil moisture using sensors. ML algorithms are explored for determining the optimum amount of fertilizers required for soils before the sowing of crops.
Drones are revolutionizing the agriculture industry. These drones are cameras enabled and are used for different applications such as field and crop monitoring, spraying of pesticides, and drip irrigation. The images captured by the drones over the entire lifecycle of crops can be examined using DL and computer vision algorithms for disease and weed identification. Thereafter, these drones are used for spraying pesticides over the weeds and infected crops.
Benefits of the project named Identification of Wheat Plant Diseases Using Computer Vision techniques are countless but as a member of this project i m responsible to show the detail benefits of the project.
As cleared from the name using Computer Vision it's mean we will focus on the area of solving problem without human involvment. We will deploy our model in a drone and using these informations, data we collected from different field, the drone will capture data from field and compare it with the model and if find any problem than the field will be sprayed automatically from the drone according to the disease find in.
Technical Details of Final DeliverableFinal Deliverable should be in hardware form. For further completion we will use resberry pi to install our system in a drone. Further more final deliverable should be in software form too if anyone have to use his/her mobile phone camera to detect the disease and take the next action to cure the disease.
Pakistan’s population is expected to reach more than 262.96 million by 2030. With this huge hike in population, one can expect massive demand for agricultural consumption as well. With the advancement in the service sector, there is a big migration of the workforce from the primary sector to the tertiary sector. In addition, the ignorance of rising diseases in crops is decreasing the yield of cultivation as well. Food being the primary necessity of human life, future researcher needs to take direction for reviving the agriculture arena. Artificial Intelligence should be the major tool for researchers to address the above-mentioned issues. With the great diversity in agronomy species, a detailed database needs to be obtained for various portions of agriculture. By using the proper tools of artificial intelligence and with the proper dataset, farming can be made more efficient for farmers. These methods can be considered as the major implementations to solve the future crisisPakistan’s population is expected to reach more than 262.96 million by 2030. With this huge hike in population, one can expect massive demand for agricultural consumption as well. With the advancement in the service sector, there is a big migration of the workforce from the primary sector to the tertiary sector. In addition, the ignorance of rising diseases in crops is decreasing the yield of cultivation as well. Food being the primary necessity of human life, future researcher needs to take direction for reviving the agriculture arena. Artificial Intelligence should be the major tool for researchers to address the above-mentioned issues. With the great diversity in agronomy species, a detailed database needs to be obtained for various portions of agriculture. By using the proper tools of artificial intelligence and with the proper dataset, farming can be made more efficient for farmers. These methods can be considered as the major implementations to solve the future crisis
Final Deliverable of the Project Hardware SystemCore Industry AgricultureOther Industries IT , Food Core Technology Artificial Intelligence(AI)Other Technologies Internet of Things (IoT), NeuroTech, OthersSustainable Development Goals Zero Hunger, 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) | 48000 | |||
| Camera | Equipment | 1 | 30000 | 30000 |
| Drone | Equipment | 1 | 18000 | 18000 |