17 articles for “Crop Yield Prediction”

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Review ArticleOpen accessIn the abstractComparative Analysis of Classical Feature Detection Methods for UAV-Based Tomato DetectionMuhammad Sarwar Jahan Morshed, Md. Nasim Adnan, Md. Rafiqul IslamRemote sensing technologies, especially Unmanned Aerial Vehicles (UAVs), become crucial for Precision Agriculture (PA) to perform different tasks such as crop detection, yield prediction, leaf disease diagnosis, weed detection, and harvest forecasting to ensure higher productivity. Therefore, using various image analytics methods feature detection from the UAV-captured images plays a vital role for conducting these PA tasks. To enhance the … …Journal of Computer Science, Vol. 21, Issue 9 · 7 Nov 2025 · 2,404 accessesView PDF Cite

Research ArticleOpen accessIn the abstractSmart Weather Data Management System for Sustainable Agriculture in Maharashtra Using Machine LearningAishwarya V. Kadu, Kuraparthi Tirumala V. ReddyAgriculture will benefit greatly from efficient weather data sustainable management. Data can be utilized to schedule the prediction yield, crop growth, and irrigation. This paper collects real-time meteorological station data via APIs, primarily focusing on the weather data sustainable management system and data related to Maharashtra Wardha, India, which is involved in a live series period. Agriculture can make potential … …Journal of Computer Science, Vol. 21, Issue 7 · 7 Jul 2025 · 3,507 accessesView PDF Cite

Research ArticleOpen accessIn the abstractAutomated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine LearningSimon H. Chiwamba, Jackson Phiri, Philip O.Y. Nkunika et al.Maize is the main food crop that meets the nutritional needs of both humans and livestock in the sub-Saharan African region. Maize crop has in the recent past been threatened by the fall armyworm (Spodoptera frugiperda, J.E Smith) which has caused considerable maize yield losses in the region. Controlling this pest requires knowledge on the time, location and extent of … …Journal of Computer Science, Vol. 15, Issue 12 · 19 Dec 2019 · 8,364 accessesView PDF Cite

Research ArticleOpen accessIn the abstractIn Silico Prediction and Functional Characterization of Genes Related to Abiotic and Biotic Stresses in Chickpea (Cicer arietinum)Sukhdeep Kaur, Satendra Singh, Gitanjali Tandon et al.Chickpea (Cicer arietinum L.) is second largest grown legumes worldwide contributing 75% of total pulse production. It is a cool season legume crop and grown in tropical and subtropical areas. Due to drastic climatic changes, chickpea suffers from many biotic (blight and wilt) and abiotic (salinity, drought, cold) stresses that directly impact the growth and yield. In our study, we … …Current Research in Bioinformatics, Vol. 7, Issue 1 · 25 Sep 2017 · 14,188 accessesView PDF Cite

EditorialOpen accessIn the abstractProtecting crop species from biotic and abiotic constraints in the era of Global Change: are we ready for this challenge?Marco Landi, Giovanni BenelliReliable and affordable supply of food is of crucial importance to the progress and stability of human societies. During the last century, we have assisted to an extraordinary increase of crop yields, especially for the most widespread and consumed crop species, such as rice, wheat, corn and soybean. The Broadbalk experiment, one of the oldest continuous agronomic experiments in the … …American Journal of Agricultural and Biological Sciences, Vol. 11, Issue 2 · 4 Jun 2016 · 8,399 accessesView PDF Cite

Research ArticleOpen accessAll words, across the recordAn Intelligent Hybrid Machine Learning Model for Paddy Disease DetectionS. Hrushikesava Raju, S. Adinarayna, U. Sesadri et al.Most countries rely on paddy/rice as a preferred staple crop due to its desirable agronomic characteristics. However, during the growth cycle, undetected or delayed identification of diseases can significantly reduce crop yield. To address this, machine learning approaches are increasingly employed for early-stage disease detection. Traditional Recurrent Neural Networks (RNNs), while useful for sequential data, suffer from limitations such as … …Journal of Computer Science, Vol. 22, Issue 4 · 29 Apr 2026 · 1,747 accessesView PDF Cite

Research ArticleOpen accessAll words, across the recordValidation of the Ceres-Wheat for Prediction of Wheat Varieties in Irrigation and Terminal Drought StressFarzad Paknejad, Fatemeh Majidi Fakhr, Seied Mehdi MirtaheriProblem statement: Crop growth models have many potential uses for crop managements. Approach: In order to evaluation of CERES-Wheat model on five varieties of winter wheat in Karaj weather condition in Full Irrigation (FI) and Stop Irrigation (SI) after flowering until final growth stage an experiment conducted in shape of split plot in based on randomize complete block design with … …American Journal of Agricultural and Biological Sciences, Vol. 7, Issue 2 · 16 Mar 2012 · 7,723 accessesView PDF Cite