AI-DRIVEN CROP DISEASE DETECTION AND MANAGEMENT IN SMART AGRICULTURE

Abstract

Agriculture is a fundamental component of human civilization. It contributes to the economy while also providing sustenance. Plant foliage or crops are susceptible to many illnesses during agricultural agriculture. The illnesses impede the development of their respective species. Timely and accurate identification and categorization of illnesses may mitigate the risk of further harm to the plants. The identification and categorization of these disorders have emerged as significant challenges. The conventional methods used by farmers to anticipate and categorize plant leaf diseases may be tedious and inaccurate. Challenges may occur while endeavouring to manually forecast illness kinds. The failure to promptly identify and categorize plant diseases may lead to the devastation of crops, causing a substantial reduction in yield. Agriculturalists using computerized image processing techniques in their fields may mitigate losses and enhance output. A multitude of strategies has been used in the identification and categorization of plant diseases using photographs of sick leaves or crops. In this research, convolutional neural networks (CNNs) are often used for image recognition and classification because of their intrinsic ability to autonomously extract relevant visual characteristics and comprehend spatial hierarchies. Consequently, in many sophisticated image recognition and classification tasks, deep learning, mostly via convolutional neural networks, is favoured when substantial data and computing resources are accessible, demonstrating effective detection and classification outcomes on their datasets. This methodology seeks to enhance productivity, minimize crop losses, and foster sustainable agricultural practices via the provision of valuable information and the automation of disease identification. The multilingual solution guarantees inclusion for diverse agricultural communities by automating disease detection and providing actionable information.

Plant phenotyping relevance

病葉画像から植物病害を検出・分類する画像解析手法が研究の中心であり、植物の病害状態を直接推定しているため、植物フェノタイピング手法として採用する。

Code and data availability

The paper describes a MobileNetV2-based tomato leaf disease detection system, but provides no public dataset, image collection, code, model checkpoint, or supplement with availability language or URLs. The tomato leaf dataset is only vaguely referenced ('the tomato leaf dataset') with no deposit or access statement, so

No evidence-backed public reproduction asset is currently recorded.