International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | FarmNet: A Hybrid Deep Learning-Based Multilingual Smart Farming System for Early Crop Disease Detection and Intelligent Decision Support |
|---|---|
| ABSTRACT | That crop failures in remote agricultural areas is a big problem in part because those farmers just can’t seem to catch issues early enough before its too late. Meanwhile , the automated systems that do exist are so focused on getting results just right that they completely forget about the fact that the real farmers need a few different things from a system – like it to be able to speak their local language, and understand the local weather, and offer some real guidance that actually makes sense to them and is easy to use even for people who have trouble reading .Our team came up with FarmNet as a solution to this mess. FarmNet is basically an intelligent farming system that can tell you right away if any of your plants are sick before its too late-across the whole area its supposed to be working in but our goal with FarmNet went way beyond just making it super accurate-we wanted it to actually help farmers out with some real assistance. So FarmNet can run in lots of different languages, figure out the current weather and give you some super specific guidance that you can actually use – and you can even have a voice assistant .We came up with the idea to use three really powerful deep learning models – ResNet50 and DenseNet121 & EfficientNetB5 – as the core of our system. These models are great at pulling out all the important stuff from images ,which lets us diagnose those plant illnesses early on with pretty good accuracy .But FarmNet is way more than just that – it can even help you look back at past epidemics, get some advice on treatment & product choices through an e-commerce interface and get all the latest info on the local climate in real time. Our system helps farmers make better choices by offering them advide at the right moments when they really need it. We tested FarmNet out and it turned out to be pretty effective – it managed to catch 94.89% of plant diseases. And that not just some number – it shows that we were able to take some of really A1 tech out there and actually use it to solve real problems for farmers in practical ways. |
| AUTHOR | PILLA UDAY BHASKAR, KANCHARLA SRAVANI, DEVARAKONDA MAHESH BABU, MADALA LAVANYA, KILLO - SADDU Department of Computer Science & Engineering – Data Science, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.14060118 |
| pdf/118_FarmNet A Hybrid Deep Learning-Based Multilingual Smart Farming System for Early Crop Disease Detection and Intelligent Decision Support.pdf | |
| KEYWORDS | |
| References | [1] "Plant Leaf Disease Identification Using VGG, ResNet, Inception-ResNet, and DenseNet Models with Optimized Image Enhancement," in IEEE Conference Publication, IEEE Xplore, 2024. [2] "Plant Diseases Detection Using Machine Learning and Image Processing: Review," in IEEE Conference Publication, IEEE Xplore, 2024. [3] "Machine Learning for Predicting Plant Diseases and Optimizing Fertilizer Use," in IEEE Conference Publication, IEEE Xplore, 2024. [4] "Detection of Plant Leaf Diseases using Machine Learning Techniques and CNN," in IEEE Conference Publication, IEEE Xplore, 2024. [5] "Performance Analysis of Tomato Leaf Disease Detection using VGG, ResNet and MobileNet," in IEEE Conference Publication, IEEE Xplore, 2024. [6] "Plant Leaf Disease Detection Using Multiple CNN Models," in IEEE Conference Publication, IEEE Xplore, 2024. [7] "Comprehensive Analysis of Plant Leaf Disease Detection using Artificial Intelligence and Optimization Algorithms," in IEEE Conference Publication, IEEE Xplore, 2024. [8] "Comparative Analysis of Different Plant Leaf Disease Classification and Detection using CNN," in IEEE Conference Publication, IEEE Xplore, 2024. [9] M. A. Jawad et al., "Real-Time Plant Disease Dataset Development and Detection of Plant Disease Using Deep Learning," IEEE Access, vol. 12, pp. 18556–18573, 2024, doi: 10.1109/ACCESS.2024.10414062. [10] A. Jafar, N. Bibi, R. A. Naqvi, A. Sadeghi-Niaraki, and D. Jeong, "Revolutionizing Agriculture With AI: Plant Disease Detection Methods, Applications, and Their Limitations," Frontiers in Plant Science, vol. 15, p. 1356260, Mar. 2024, doi: 10.3389/fpls.2024.1356260 |
