Jul 2026· International Conference Computing Methodologies and Communication· pp. 1413-1417· 0 citations· 23 references
Abstract
Agriculture is the most important and fundamental source of domestic income across countries. However, it suffers huge losses due to plant diseases caused by bacteria, fungi, and viruses, which directly affect crop yield and farmers’ income. Ensuring crop security through efficient disease tracking becomes crucial to protecting the quantity and quality of crops. Therefore, identifying plant diseases is crucial. The plant disease can be recognised commonly with the disease in its leafy part. In this paper, we have proposed a transfer learning model which has backbone of InceptionV3. The model is trained and tested on publically available dataset PlantVillage and we have chosen only the Potato and Tomato Plants. The model achieves an accuracy of 98% which is better than existing methods.
Background: In the Indian economy agriculture plays important role. Many of the crops are damaged due to diseases, therefore plant leaf disease detection at early stage is important. Tomatoes are the second most consumed vegetable in Indian households, with a rank second largest producer and consumption in world. Tomat...
V. Nemade, V. Fegade, Deepti Barhate et al.· Indian Journal of Agricultur...· 0 citations
Plant diseases, particularly in tomato crops pose a significant threat to agricultural productivity which results in yield losses, accounting for an estimated 10-30% of global tomato production annually. In this era characterized by technological advancement farmers continue to follow traditional practices regarding di...
Isaac Phiri, Regi Anbumozhi Y., Esther J.· International Journal of Dat...· 0 citations
The proposed Sugarcane Leaf Disease Detection and Classification System provides a fast, accurate, and user-friendly solution for automated disease diagnosis and contributes to improved crop management, reduced crop losses, and enhanced agricultural productivity.
The Plant Disease Detecting System leverages advances in artificial intelligence and deep learning to provide an automated, efficient, and reliable solution for identifying plant diseases at an early stage and contributes to increased crop productivity, reduced chemical usage, and sustainable farming practices.
Plants play a vital role in providing food on a global scale. Several environmental factors contribute to the occurrence of plant leaf diseases, leading to substantial reductions in crop yields. Nevertheless, the process of manually detecting plant leaf diseases is both time-consuming and detection to errors. However,...
D. Basha, K. Amarnath, P. A. Devi et al.· International Journal Of Eng...· 0 citations
Many leaf diseases have significant effects on yield and quality and wheat is an important crop contributing to food security globally. Accurate and timely diagnosis of these diseases is important for the sustainable use of agriculture. This study assesses the effectiveness of deep learning (DL) technique using ResNet...
S. R· International Conference on...· 0 citations
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