A Deep Learning-Based Mobile Application for Classifying Rice Crop Diseases in Labo, Camarines Norte

The primary concern of the rice farming community is the early detection of rice crop disease. Rice crop disease can be detected with high accuracy with the availability of advanced digital cameras and smartphones to improved image acquisition modes and deep learning methods such as convolutional neural networks (CNN). This study used a qualitative approach employing focus group discussions with selected farmers and an online meeting with the Department of Agriculture (DOA). Also compared and evaluated different optimizers using several optimization techniques namely Stochastic Gradient Descent with Momentum (SGDM), Root Mean Squared Propagation (RMSProp), Nesterov-accelerated Adaptive Moment Estimation (Nadam), and Adaptive Moment Estimation (Adam) in different dataset partitioned by 80/20%, 60/40%, 50/50%, 40/60%, and 20/80% using cv2 module from OpenCV library. Furthermore, presents the hardware and software to developed a free, easy-to-use and widely accessible mobile application that can efficiently and accurately diagnose 22 types of diseases and a healthy leaf sample. The experiment results show that Nadam optimizer achieve a maximum accuracy of 97.67-100.00% in the 80/20 partition, 88.17-100% in the 60/40 partition, 84.93-100% in the 50/50 partition, 64.67-100% in the 40/60 partition, and 37.03-99.90% in the 20/80 dataset partition. Therefore, the android application “Rice Crop Diseases Classification” can accurately classify rice diseases using Nadam optimizers including healthy rice. Addiditonally, despite employing various dataset partitioning methods, it achieves the highest accuracy from both low and high records using 80 by 20% dataset partitioned

Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf

In the whole agriculture plays a very important in country’s economic condition specially in Indian agriculture has a crucial role for raising the Indian economic structure and its level. India’s frequent changing climatic situation, various bacterial disease is much normal that drastically decreases the productivity of crop productivity. Most of the researcher is moving towards into this topic to find the early detection technique to identify the disease in small green leaves plants. A single, micro bacterial infectious disease can destroy all the agricultural small green leaves plants get damaged overnight and hence must be prevented and cured as earliest as possible so that agriculture production. In this research work, we had tried to developed a green small green leaves plants bacterial disease early detection system based on the deep learning network system which will detect the disease at very earlier state of symptoms observed. Deep learning technique is has various algorithms to detect the earliest stage of any of the procedural processing of any bacterial infections or disease. This paper consists of investigations and analysis of latest deep learning techniques. Initially we will explore the deep learning architecture, its various source of data and different types of image processing method that can be used for processing the images captured of leaf for data processing. Different DL architectures with various data visualization’s tools has recently developed to determine symptoms and classifications of different type of plant-based disease. We had observed some issue that was un identified in previous research work during our literature survey and their technique to resolve that issue in order to handle the functional auto-detection system for identifying the certain plant disease in the field where massive growth of green small green leaves plants production is mostly done. Recently various enhancement has been done in techniques in CNN (convolution neural network) that generates much accurate images classification of any object. Our research work is based on deep learning network that will observe and identifies the symptoms generated in leaflet of plant and identifies the type of bacterial infection in progress in that with the help of plant classification stated in the plant dataset. Our research work represents the implementation DCGAN and Hybrid Net Model using Deep learning algorithm for early-stage identification of green plant leaves disease in various environmental condition. Our result obtained shows that it has DCGAN accuracy 96.90% when compared withHybrid Net model disease detection methodologies

Advancements in Deep Learning for Early Detection of Plant Diseases: Techniques, Challenges, and Opportunities in Precision Agriculture

Deep learning (DL) has emerged as a transformative technology in the field of agriculture, revolutionizing various applications such as disease recognition, plant classification, and fruit counting. Compared to traditional image processing techniques, deep learning has demonstrated a remarkable ability to achieve significantly higher accuracy, surpassing the performance of conventional methods.One of the primary advantages of leveraging deep learning in agriculture is its unparalleled capacity to provide more precise predictions, enabling farmers and researchers to make better-informed decisions that lead to improved outcomes. Deep learning models have consistently exhibited impressive performance across a wide range of tasks, including visual recognition, language processing, and speech detection, making them highly suitable for diverse agricultural applications. Furthermore, the success of deep learning in medical imaging has been successfully extended to the agricultural domain. By applying deep learning's powerful capabilities, stakeholders in the agricultural sector can now accurately classify plant species, detect diseases, and identify pests with unprecedented precision. This advancement has the potential to drive significant improvements in productivity, reduce crop losses, and optimize resource allocation, ultimately transforming the way we approach agricultural practices

Usefulness of interpretability methods to explain deep learning based plant stress phenotyping

Deep learning techniques have been successfully deployed for automating plant stress identification and quantification. In recent years, there is a growing push towards training models that are interpretable -i.e. that justify their classification decisions by visually highlighting image features that were crucial for classification decisions. The expectation is that trained network models utilize image features that mimic visual cues used by plant pathologists. In this work, we compare some of the most popular interpretability methods: Saliency Maps, SmoothGrad, Guided Backpropogation, Deep Taylor Decomposition, Integrated Gradients, Layer-wise Relevance Propagation and Gradient times Input, for interpreting the deep learning model. We train a DenseNet-121 network for the classification of eight different soybean stresses (biotic and abiotic). Using a dataset consisting of 16,573 RGB images of healthy and stressed soybean leaflets captured under controlled conditions, we obtained an overall classification accuracy of 95.05 \%. For a diverse subset of the test data, we compared the important features with those identified by a human expert. We observed that most interpretability methods identify the infected regions of the leaf as important features for some -- but not all -- of the correctly classified images. For some images, the output of the interpretability methods indicated that spurious feature correlations may have been used to correctly classify them. Although the output explanation maps of these interpretability methods may be different from each other for a given image, we advocate the use of these interpretability methods as `hypothesis generation' mechanisms that can drive scientific insight.This is a pre-print of the article Nagasubramanian, Koushik, Asheesh K. Singh, Arti Singh, Soumik Sarkar, and Baskar Ganapathysubramanian. "Usefulness of interpretability methods to explain deep learning based plant stress phenotyping." arXiv preprint arXiv:2007.05729 (2020). Posted with permission.</p