International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
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Kisan Seeva Kunal Jain, Sankalp Kumar Gupta, Mohammed Ahmed Ansari, Deepali Shrikhande UG Student, Dept. of I.T., Vidyalankar Institute of Technology, Mumbai University, India UG Student, Dept. of I.T., Vidyalankar Institute of Technology, Mumbai University, India UG Student, Dept. of I.T., Vidyalankar Institute of Technology, Mumbai University, India Assistant Professor, Dept. of IT Engineering, VIT college, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------deficiencies, and other factors that harm their crops. Many Abstract - The world population is rapidly increasing and is
farmers in the area rely on experience rather than proper understanding, and they make decisions based on visual inspections of their plants. However, this method requires ongoing evaluation of expertise, which can be too expensive for large farms.
expected to reach 8.6 billion by 2030. As a result, food production and consumption will increase, leading to a greater threat to food security from crop diseases that can damage agricultural products. Unfortunately, in several parts of India, inadequate infrastructure makes it challenging to identify crop-damaging diseases quickly. Farmers now grow a diverse array of crops and often aim to expand the variety they grow, making it difficult to foresee crop illnesses at an early stage. This "Experimental farming" mentality often results in significant losses, making it more expensive for farmers to learn from past mistakes.
In addition, some farmers have to travel long distances to consult with agricultural officers, which can be costly and time-consuming. To address these challenges, automatic diagnosis of plant diseases from the symptoms that occur on the plant leaves is an interesting study area that could prove advantageous in monitoring vast fields of crops. Machine vision makes this possible, allowing for image-based process control, robot navigation, and autonomous inspection.
To address these challenges, an electronic expert system, in the form of an android app, is proposed. This app will enable farmers to make wise decisions and enhance their farming operations without incurring significant losses. The system employs an innovative method of object detection to identify plant diseases, significantly improving the speed and accuracy of disease detection on leaves. Convolutional neural network (CNN) models are utilized to detect diseases, which are more accurate than other models available in the market. With this approach, the system will identify whether crops are infected or not, and if they are, the user will be informed, and appropriate action can be taken to address the sick crop.
Diagnosing plant diseases by visually inspecting the symptoms on plant leaves is a complex process, even for seasoned agronomists and plant pathologists. This complexity is due to the vast number of cultivated plants and their Phyto pathological issues. Therefore, the development of an automated computational system for identifying and diagnosing plant diseases would greatly benefit agronomists who are requested to make these diagnoses. For farmers in regions without the necessary infrastructure for agronomic and Phyto pathological advice, a simple-to-use mobile application could prove to be a useful tool. However, for this system to be effective, it must be able to detect and diagnose certain diseases in real-world settings and be compatible with a suitable mobile application
The app's approach allows farmers to take crop photographs and analyze the presence or absence of illnesses quickly, providing a workable solution to the lack of adequate infrastructure. It also caters to farmers' needs for a diverse array of crops while minimizing the risk of significant losses. The suggested system offers a solution to the issue of food security by providing farmers with the tools to detect and address crop diseases at an early stage, thus improving food production and consumption.
1.1 Problem Statement To increase production in their fields, farmers should regularly inspect their crops for pests and diseases. However, many farmers struggle to identify infections in a timely manner and even seeking help from farming professionals can result in significant delays. To address this challenge, we are creating an Android application that uses plant photographs to help farmers detect diseases. Furthermore, the app will provide information on the prices of various crops at nearby markets through an API, offer rental options for farming equipment, and support farmers in multiple regions with its multilingual features.
Key Words:
Farmer, Crop, Leaf Disease, Renting Farming Equipment, Mandi Price, Disease Prediction
1. INTRODUCTION After conducting a survey in some villages and cities, we found that over 70% of farmers are interested in trying new crops instead of their traditional ones. However, due to a lack of knowledge and experience, many farmers struggle to grow these new crops, and they often suffer from diseases, nutrient
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