TL;DR
A YOLOv12-based object detection model for the automatic multiple classification of fruit leaf diseases is developed and is considered a strong backbone for real-time and field-deployable leaf disease monitoring systems and decision support tools.
The timely identification and diagnosis of leaf diseases is crucial for crop productivity and health. This study proposes a robust approach to this issue by combining beetle swarm optimization (BSO) with other ML models. Four different datasets were used to train our model: apple leaf, grape leaf, plant village leaf, a...
Fruit crops such as apple, grape, and tomato suffer significant yield loss every year due to leaf diseases that are usually identified by manual visual inspection, a process that is slow, subjective, and depends heavily on the availability of trained agronomists. This paper presents a machine-learning-based system that...
Banana cultivation is affected by several leaf diseases that can alter leaf colour, texture and visible surface patterns. Identifying these symptoms early is useful for crop monitoring and disease management. In practice, diagnosis based only on visual inspection can be time-consuming and may vary with the experience o...
: Tomatoes are among the most important horticultural crops for the processing industry worldwide and play a key role in human diets due to their high nutritional and culinary value. However, plant diseases have become a major threat to global food security, increasing the need for reliable and early detection methods....
In agricultural productivity, there is a decrease in crop yield due to tomato leaf disease. An early detection system with high accuracy is needed to address this issue. This study evaluates the performance of a standalone SqueezeNet-based Convolutional Neural Network (CNN) architecture for identifying five tomato leaf...
In this study, there is better DL and computer vision techniques or strategies to optimize tomato leaf ailment detection and growth agricultural productiveness. The PCA DeepNet architecture represents a modern approach for shooting facts complexity and disease patterns with the aid of combining conventional ML with a c...
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