Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives

Deep learning (DL), a subset of machine learning approaches, has emerged as a versatile tool to assimilate large amounts of heterogeneous data and provide reliable predictions of complex and uncertain phenomena. These tools are increasingly being used by the plant science community to make sense of the large datasets now regularly collected via high-throughput phenotyping and genotyping. We review recent work where DL principles have been utilized for digital image–based plant stress phenotyping. We provide a comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios. Finally, we outline several avenues of research leveraging current and future DL tools in plant science

Implementation of Deep CNN Model for the Detection of Plant Leaf Disease

The potato is the most important tuber crop in the world, and it is grown in about 125 different nations. Potato is the crop that is most commonly consumed by a billion people worldwide, virtually every day, behind rice and wheat. However, a number of bacterial and fungal diseases are causing the potato crop's quality and yield to decline. Potato Leaf diseases must be promptly identified and prevented to increase production. Various researchers look for solutions to protect plants instead of   traditional processes which take more time. Recent technological developments have thrown up many alternates to traditional methods which are labour intensive. The application of AlexNet model Deep Convolutional Neural Network(CNN) to recognise diseases in potato plants avoids the disadvantages of selecting disease spot features artificially and makes more objective the plant disease feature extraction. It improves research efficiency and speeds up technology transformation. Accuracies ranging from 85% - to 95% were obtained using AlexNet model Deep

A Detailed Review on Plant Leaf Disease Detection and Classification Methodologies using Deep Learning Techniques

The rapid emergence and evolution of deep learning methodologies in the field of plant disease classification and detection has resulted in significant progress. Their application has revolutionized the way agriculture is done. This paper provides an overview of the advancements in utilizing deep learning models to address the crucial task of identifying and categorizing plant diseases. By harnessing the power of deep convolutional neural networks (CNNs) and transfer learning, researchers have achieved remarkable accuracy in disease classification, often surpassing traditional methods. This study also delves into the challenges that persist in this field, such as the scarcity of labeled data and potential biases in models. To address these concerns, the integration of visualization techniques is explored, allowing for better model interpretation and transparency. The collaborative efforts of agricultural experts and machine learning researchers are deemed crucial for overcoming these challenges and driving the future direction of research. Looking ahead, the interdisciplinary approach is anticipated to play a pivotal role in refining deep learning models for plant disease detection. A seamless collaboration between domain-specific professionals, machine learning experts, and agricultural practitioners is essential to foster innovation, enhance the reliability of models, and create a sustainable agricultural ecosystem. With the integration of cutting-edge architectures, emerging technologies like edge computing, and broader datasets, the field is poised to bring about transformative changes in agricultural practices, bolstering crop health and productivity

Cotton Plants Diseases Detection Using CNN

Identifying cotton infections is a major problem that often requires expert assistance in determining and treating the disease. This investigation aims to create a sophisticated learning model that can tell a plant's illness apart from images of its leaves. Convolution Brain Organization is used to do move training to complete deep learning. For the dataset used, this method produced outcomes for a given state of quality. The main goal is to offer this approach to as many individuals as is realistically expected while reducing the cost of professional aid in identifying cotton plant diseases. The ability to recognize and understand items from photographs has been made possible by rapid advancements in deep learning (DL) techniques

Machine Learning-Based Algorithms for the Detection of Leaf Disease in Agriculture Crops

Identifying plant leaves early on is key to preventing catastrophic outbreaks. An important studyarea is automatic disease detection in plants. Fungi, bacteria, and viruses are the main culprits in most plantillnesses. The process of choosing a classification method is always challenging because the quality of the results can differ depending on the input data. K-Nearest Neighbor Classifier (KNN), Probabilistic NeuralNetwork (PNN), Genetic Algorithm, Support Vector Machine (SVM) and Principal Component Analysis,Artificial Neural Network (ANN), and Fuzzy Logic are a few examples of diverse classification algorithms.Classifications of plant leaf diseases have many uses in a variety of industries, including agriculture andbiological research. Presymptomatic diagnosis and crop health information can aid in the ability to managepathogens through proper management approaches. Convolutional neural networks (CNNs) are the mostwidely used DL models for computer vision issues since they have proven to be very effective in tasks likepicture categorization, object detection, image segmentation, etc. The experimental findings demonstrate theproposed model's superior performance to pre-trained models such as VGG16 and InceptionV3. The range ofcategorization accuracy is 76% to 100%, based on