Automatic Apple Leaf DIseases Detection and Classification Using Multilayer Convolution Neural Network

An improved CNN model is proposed for apple leaf diseases. Effective data augmentation and image preprocessing operations are devised. Our method boosts the apple leaf diseases recognition accuracy and reduces computational time.We are thankful to Ural Federal University, Yekaterinburg, Russia for their support and necessary facilities to carry out this study

Optimizing Pre-Trained Models of Deep Learning for Identification of Plant Disease

The Plant diseases should be identified early to prevent the economic loss of farmers and ensure the availability of food for humans. The plant disease identification can be automated by using the Artificial Intelligence techniques. Researchers have proposed many deep learning methods for identifying plant diseases. Deep learning models use an increased number of parameters, it requires higher computational power, training a deep learning model from start requires more time. In this article we utilized transfer learning along with fine tuning for identification of plant diseases. Cassava plant disease dataset was utilised for training. and evaluate the suggested model. The performance accuracy achieved by Resnet50 is 73.12 % and fine-tuned Resnet50 is 80.84 %. The fine-tuned model achieves greater accuracy with a lesser amount of parameters Impact Statement–Artificial Intelligence is evolving all around the world. The AI techniques are used to automate the process of plant disease identification. Traditional methods are not accurate and time consuming. To help the farmers in diagnosing plant disease and stop economic loss to them, we employ deep learning models to do the work. The pretrained models predict the plant diseases, further we fine-tune them in order to get high accuracy. Early identification of the diseases accurately will avoid loss and improve productivity of the crops

Deteksi Bola dan Gawang dengan Metode YOLO Menggunakan Kamera Omnidirectional pada Robot KRSBI-B

This research is a form of development of object detection capabilities on wheeled soccer robots using an omnidirectional camera with the You Only Look Once (YOLO) method where the results show that the robot can detect more than one object, namely the ball and the goal on the green field. This study uses the KRSBI-Wheeled UAD robot using an omnidirectional camera as a tool to carry out the detection process and then uses OpenCV 4.0, Deep Learning, and a laptop as a place to create a detection model, as well as balls and goals as objects to be detected. The results obtained from this study are that the two types of YOLO models tested, namely YOLOv3 and YOLOv3-Tiny can detect ball and goal objects in two different types of frame sizes, namely 320x320 and 416x416 which can be seen from the performance of the YOLOv3 model which has an mAP value of 76%. on the 320x320 frame and an mAP value of 87.5% in the 416x416 frame then the YOLOv3-Tiny model has an mAP value of 68.1% in the 320x320 frame and an mAP value of 75.5% in the 416x416 frame where the YOLOv3 model can detect both object class is much more stable compared to YOLOv3-Tiny. Penelitian ini merupakan bentuk pengembangan dari kemampuan deteksi objek pada robot sepak bola beroda dengan menggunakan kamera omnidirectional dengan metode You Only Look Once (YOLO) dimana hasil penelitian menunjukkan bahwa robot dapat mendeteksi lebih dari satu objek yaitu bola dan gawang di atas lapangan hijau. Penelitian ini menggunakan robot KRSBI-Beroda UAD dengan memakai kamera omnidirectional sebagai alat untuk melakukan proses pendeteksian lalu menggunakan OpenCV 4.0, Deep Learning, dan laptop sebagai tempat membuat model pendeteksian, serta bola dan gawang sebagai objek yang akan dideteksi. Hasil yang didapatkan dari penelitian ini yaitu kedua jenis model YOLO yang diuji yaitu YOLOv3 dan YOLOv3-Tiny dapat mendeteksi objek bola dan gawang pada dua jenis ukuran frame yang berbeda yaitu 320x320 dan 416x416 yang dapat dilihat dari performa pada model YOLOv3 memiliki nilai mAP sebesar 76% pada frame 320x320 dan serta nilai mAP sebesar 87,5% pada frame 416x416 lalu pada model YOLOv3-Tiny memiliki nilai mAP sebesar 68,1% pada frame 320x320 serta nilai mAP sebesar 75,5% pada frame 416x416 yang dimana model YOLOv3 dapat mendeteksi kedua kelas objek jauh lebih stabil dibandingkan dengan model YOLOv3-Tiny

Artificial intelligence-based solutions for coffee leaf disease classification

Coffee is one of the most widely consumed beverages and the quantity and quality of coffee beans depend significantly on the health and condition of coffee plants, particularly their leaves. The automation of coffee leaf disease classification using AI is an essential need, providing not only economic benefits but also contributing to environmental conservation and creating better conditions for sustainable coffee cultivation. Through the application of AI, early disease detection is facilitated, thereby reducing pest and disease control costs, minimizing crop losses, increasing coffee productivity and product quality, and promoting environmental preservation. Many studies have proposed AI algorithms for coffee disease classification. However, numerous algorithms employ classical algorithms, while some utilize deep learning, the current state-of-the-art in computer vision. The challenge lies in the fact that when using deep learning, a substantial amount of data is required for training. The design of deep learning architectures to enhance model accuracy while still working with a small training dataset remains an area of ongoing research. In this study, we propose deep learning-based method for coffee leaf disease classification. We propose the combination of different deep convolutional neural networks to further improve overall classification performance. Early and late fusion have been conducted to evaluate the effectiveness of the pre-trained model. Our experimental results demonstrate that the ensemble method outperforms single-model approaches, achieving high accuracy and precision in BRACOL coffee disease leaf

Apple scab detection using CNN and Transfer Learning

Received: January 11th, 2021 ; Accepted: April 10th, 2021 ; Published: April 22nd, 2021 ; Correspondence: [email protected] goal of smart and precise horticulture is to increase yield and product quality by simultaneous reduction of pesticide application, thereby promoting the improvement of food security. The scope of this research is apple scab detection in the early stage of development using mobile phones and artificial intelligence based on convolutional neural network (CNN) applications. The research considers data acquisition and CNN training. Two datasets were collected - with images of scab infected fruits and leaves of an apple tree. However, data acquisition is a time-consuming process and scab appearance has a probability factor. Therefore, transfer learning is an appropriate training methodology. The goal of this research was to select the most suitable dataset for transfer learning for the apple scab detection domain and to evaluate the transfer learning impact comparing it with learning from scratch. The statistical analysis confirmed the positive effect of transfer learning on CNN performance with significance level 0.05

Aplikasi Pendeteksi Penyakit Pada Daun Tanaman Apel Dengan Metode Convolutional Neural Network

According to 2017 statistical fruit and vegetable crops published by BPS, total apple production in 2017 amounted to 319004 tons. There are many diseases that can attack apple plants, therefore early detection and identification of plant diseases are the main factors to prevent and reduce the spread of apple plant diseases. CNN method is used in this study with LeNet-5 architecture which can process 3151 imagery data with a mini-mum accuracy level of 75%. This study uses a dataset derived from PlantVillage created by SP Mohanty CEO & Co-founder of CrowdAI with a total of 3151 leaf images that have been classified according to their respec-tive classes. CNN stages include Convolution Layer, Rectified Linear Unit (ReLU), Subsampling, Flattening, Fully Connected Layer. The test results are evaluated using image testing data. The evaluation process is done using a confusion matrix. Based on the results of testing applications that are designed with 99,4% model ac-curacy and 97,8% validation accuracy, the application is useful for detecting apple disease using apple leaf images

Numerical Modeling and Design of Machine Learning Based Paddy Leaf Disease Detection System for Agricultural Applications

In order to satisfy the insatiable need for ever more bountiful harvests on the global market, the majority of countries deploy cutting-edge technologies to increase agricultural output. Only the most cutting-edge technologies can ensure an appropriate pace of food production. Abiotic stress factors that can affect plants at any stage of development include insects, diseases, drought, nutrient deficiencies, and weeds. On the amount and quality of agricultural production, this has a minimal effect. Identification of plant diseases is therefore essential but challenging and complicated. Paddy leaves must thus be closely watched in order to assess their health and look for disease symptoms. The productivity and production of the post-harvest period are significantly impacted by these illnesses. To gauge the severity of plant disease in the past, only visual examination (bare eye observation) methods have been employed. The skill of the analyst doing this analysis is essential to the caliber of the outcomes. Due to the large growing area and need for ongoing human monitoring, visual crop inspection takes a long time. Therefore, a system is required to replace human inspection. In order to identify the kind and severity of plant disease, image processing techniques are used in agriculture. This dissertation goes into great length regarding the many ailments that may be detected in rice fields using image processing. Identification and classification of the four rice plant diseases bacterial blight, sheath rot, blast, and brown spot are important to enhance yield. The other communicable diseases, such as stem rot, leaf scald, red stripe, and false smut, are not discussed in this paper. Despite the increased accuracy they offer, the categorization and optimization strategies utilized in this work lead it to take longer than typical to finish. It was evident that employing SVM techniques enabled superior performance results, but at a cost of substantial effort. K-means clustering is used in this paper segmentation process, which makes figuring out the cluster size, or K-value, more challenging. This clustering method operates best when used with images that are comparable in size and brightness. However, when the images have complicated sizes and intensity values, clustering is not particularly effective