Advancing sustainable agriculture through multi-parameter fuzzy soft set-based plant disease classification.

Comprehensive plant disease classification and severity estimation for sustainable farming via automatic segmentation and multi-scale feature fusion.

Detecting plant leaf diseases at an early stage is one of the most important requirements for sustainable agriculture, increasing crop productivity, and achieving the global Sustainable Development Goals (SDGs). However, accurately recognizing them in real-world farm fields can still be difficult due to factors such as background complexity, changes in light conditions, and very similar looking classes from a visual standpoint. In order to solve these problems, the authors here present a new Multi-Scale Feature Fusion (MSFF) model that can offer robust and highly accurate performance in identifying plant leaf diseases. Firstly, the brand new hybrid method starts with a U-Net segmentation designed exclusively to separate the diseased parts and thus allow the classification to be more robust. Next, Rank Order Fuzzy (ROF) is implemented to get rid of the background while still maintaining the edges, and the additional data is used for the network to generalize better. The color distribution is then analyzed to determine the variations in color brought about by the infection. In terms of features, EfficientNet is paired with an Attention-based Autoencoder to produce both spatially detailed global features and compact latent representations. The two sets of features are then combined through Canonical Correlation Analysis (CCA) which not only identifies the dependencies between the features but also enhances the discriminative strength. The final fused feature set is fed to module based on YOLO for detection and classification in order to obtain the final result of the plant disease identification system which is both accurate and fast. Among other datasets, the model has been tested on different apple leaf datasets such as FGVC7, AppleLeafSet, PlantVillage Apple, Kaggle Apple Leaves, and ATLDSD, which contain five disease classes. The results of the experiments indicate a classification accuracy of 99.95%, thus the model is superior to several state-of-the-art deep learning and classical machine learning methods. Statistical methods like five-fold cross-validation, paired t-tests (p < 0.05), and effect size analysis support the strength and importance of the model. Besides, the Grad-CAM heat maps also reveal that the model is pinpointing the disease areas that are biologically relevant. These findings make it very clear that the MSFF is a reliable, explainable, and scalable tool for precise plant disease recognition even in complex farming environments.

A Resource-Efficient Framework for Plant Disease Classification Using Classical Image Features

Plants can suffer a number of diseases that impact agricultural productivity and food security, particularly in developing farming communities. Although deep learning is capable of classification of diseases with outstanding results, its use is limited due to the difficulty of obtaining large labeled databases and the high requirement of computation. To address these challenges, this study proposes a new method for plant disease classification based on traditional image processing and machine learning algorithms with lightweight and low computation requirements. This one uses several handcrafted descriptors such as color histograms, Haralick texture features and Hu moments to retrieve the information relevant to a disease from the segmented leaf images. Performance of top five classifiers, namely Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression and Naïve Bayes classifiers are evaluated from the dataset of healthy plant leaves and diseased plant leaves images on 10-fold cross validation. Based on the results of the research work, the best classification model was the Random Forest Classifier model with the accuracy value is 98.12%, 0.98 precision, 0.98 recall, and 0.98 F1 value. The proposed solution was also found to be uncomputation complex and low memory consuming and can be made realtime inference. Therefore, this solution can be implemented onto agricultural systems, considering edge computing and IoT. The results of the research also demonstrated that feature-based machine learning approaches afford interpretable and reliable plant disease detection at a low computation cost, further contributing to sustainable, and precision agriculture.

Enhanced tomato leaf disease detection using multi-modal deep-handcrafted feature fusion with optimization-driven learning techniques

DEVELOPMENT OF AN ENHANCED HYBRID INTELLIGENT MACHINE LEARNING MODEL FOR MAIZE LEAF DISEASE DETECTION AND CLASSIFICATION

Maize leaf diseases are common and have adverse effects on agricultural productivity by reducing crop yield and grain quality, which leads to significant economic losses and raises concerns about food security. Although Support Vector Machine (SVM) techniques have been widely applied for automated disease diagnosis, their effectiveness is often limited by inadequate optimisation, resulting in reduced classification accuracy and poor generalisation. This research presents an Enhanced Hybrid Intelligent Machine Learning Model (EHIMLM-SVM) that integrates the Enhanced Binary Particle Swarm Optimisation (EBPSO) and the Enhanced Reptile Search Algorithm (ERSA) for optimisation of the classifier. The model was evaluated using 2,222 maize leaf images obtained from the Kaggle PlantVillage dataset. Preprocessing involved grayscale conversion, contrast enhancement, adaptive median filtering, Sobel edge detection, and extraction of colour, texture, and shape features. Experimental results produced 97.50% accuracy, 98.15% precision, 97.76% sensitivity, 97.80% specificity, and a 2.20% false positive rate, indicating reliable performance for precision agriculture and sustainable crop management.

A Convolutional Neural Network for Accurate Cotton Leaf Disease Classification

The proposed CNN framework provides a scalable, computationally efficient, and intelligent solution for automated cotton leaf disease classification, contributing to the advancement of AI-driven precision agriculture and sustainable crop management.

Plant Disease Detecting System Using CNN

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.